From f3dbb02001c9c65c457e8b03edecc90f161b8799 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 5 Oct 2026 11:46:58 -0400 Subject: [PATCH 1/3] Audit the Medicare Part B premium in medical expenses; propose excluding scenario_114's Virginia income tax policyengine-us 2.15.17 treats every Medicare-eligible person as enrolled (takes_up_medicare_if_eligible defaults to true) and adds the modeled Part B premium, $2,434.80 for 2026, to medical expenses. No prompt states Medicare enrollment or a premium. A sweep of all 1,984 outputs on the reference system with the premium at 0, and with enrollment off, moves exactly two: scenario_114 federal (10,729.61 -> 11,265.27, already proposed in #191) and Virginia (3,514.15 -> 3,654.15). No SNAP or eligibility output moves by any amount. Adds reference_audit/2026-10-05-medicare-part-b: the sweep, a per-household explanation, the drafted records (one for the Virginia output, and a fallback for the federal output if #191's record is not adopted) and the leaderboard impact under each combination with #191. Changes no reference or published number; the exclusion waits for Max's ruling. Co-Authored-By: Claude Opus 5.5 --- .../2026-10-05-medicare-part-b/README.md | 166 + .../proposed_exclusions.json | 121 + .../scripts/explain_households.py | 103 + .../scripts/leaderboard_impact.py | 404 + .../scripts/propose_exclusions.py | 384 + .../scripts/sweep_part_b.py | 624 + .../verification/leaderboard_impact.json | 358 + .../leaderboard_impact_marginal.csv | 47 + .../leaderboard_impact_models.csv | 47 + .../verification/model_answers.csv | 93 + .../verification/part_b_households.csv | 34 + .../verification/sweep_part_b.csv | 1985 + .../verification/sweep_part_b_households.json | 51772 ++++++++++++++++ .../verification/sweep_part_b_summary.json | 4216 ++ 14 files changed, 60354 insertions(+) create mode 100644 reference_audit/2026-10-05-medicare-part-b/README.md create mode 100644 reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json create mode 100644 reference_audit/2026-10-05-medicare-part-b/scripts/explain_households.py create mode 100644 reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py create mode 100644 reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py create mode 100644 reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact_marginal.csv create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact_models.csv create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/model_answers.csv create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/part_b_households.csv create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b.csv create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b_households.json create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b_summary.json diff --git a/reference_audit/2026-10-05-medicare-part-b/README.md b/reference_audit/2026-10-05-medicare-part-b/README.md new file mode 100644 index 00000000..dc022afb --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/README.md @@ -0,0 +1,166 @@ +# Medicare Part B premium in medical expenses, October 5, 2026 + +This directory audits the Medicare Part B premium that PolicyBench's US references count as a medical expense. It proposes one exclusion, scenario_114's Virginia income tax, and drafts a fallback record for scenario_114's federal income tax. It changes no reference. Adopting the record changes published scores, so it waits for Max's ruling, alongside the state income tax withholding proposal in PolicyEngine/policybench#191 (decision d963), so that one release can carry both. + +## The mechanism + +These files hold for policyengine-us 2.15.17, the version that built the published references, as installed in the reference venv (`results/local/adds202609/triage/.venv-pe21517`). The conventions module `latest_final` (`../2026-09-28/fixes/`) touches none of them. + +- `medical_expense_health_insurance_premiums` (`variables/household/expense/health/`) returns `health_insurance_premiums` when that input is nonzero. Otherwise it returns `health_insurance_premiums_without_medicare_part_b + medicare_part_b_premium * medicare_enrolled`. `health_insurance_premiums` has no formula, and no household sets it. +- `medicare_enrolled` adds `takes_up_medicare_if_eligible`, whose default is true, for everyone `is_medicare_eligible` covers. That variable covers age 65 and over, or Social Security disability benefits with at least 24 months of `months_receiving_social_security_disability`. So the engine treats every eligible person as enrolled. +- `medicare_part_b_premium` is `gross_medicare_part_b_premium` less `msp_part_b_premium_coverage` (Medicare Savings Program), floored at 0. + - The gross premium is `base_part_b_premium` plus an income-related amount. The base premium is the CMS standard premium times 12: $202.90 × 12 = $2,434.80 for 2026. + - The income-related amount reads `medicare_irmaa_magi_two_years_prior`. Its formula computes AGI plus tax-exempt interest for 2024. No household sets any 2024 income, so it is 0 and no income-related amount applies. +- These variables consume the premium through `medical_expense_health_insurance_premiums`: + - `itemized_medical_expenses`. It feeds the federal medical expense deduction (`medical_expense_deduction`, 26 U.S.C. 213(a): expenses above 7.5% of AGI) and the state deductions built on it. Examples are Virginia's itemized deductions (which start from the federal itemized deductions), Alabama's, New Jersey's, Kansas's, New York's and Arizona's. + - `snap_allowable_medical_expenses`, the SNAP excess medical deduction for elderly or disabled members. + - `medicaid_medically_needy_medical_expenses`. + - `oh_insured_unreimbursed_medical_care_expense_amount`, which counts premiums for Medicare-eligible people only. + - `mt_medical_expense_deduction_indiv`, `hud_medical_expenses` and `pa_ccw_medical_expenses`. +- `medicare_part_b_premium` also enters the SPM unit's premiums and Washington's senior property tax exemption income; no output reads either. Enrollment also gates: + - the Part A premium and Part D income-related surcharge (SPM only); + - MSP coverage; + - `medicare_cost`, the impact weight of the Medicare eligibility outputs; + - the ACA "at interview" coverage list and CHIP's disqualifying-coverage list. + + The ACA premium tax credit tests `is_medicare_eligible`, not enrollment. + +## The prompts + +No PolicyBench household sets `medicare_enrolled`, `takes_up_medicare_if_eligible`, `medicare_part_b_premium` or `health_insurance_premiums`, so no prompt states Medicare enrollment or a Part B premium. `policybench/scenarios.py` would also hide the first two: `medicare_enrolled` is in `EXCLUDED_INPUT_VARIABLES`, and `takes_up_` is an excluded prefix. The prompt's rules (`policybench/prompts.py`, `TASK_PREFACE`) say: + +- "Treat any unlisted numeric input as 0" +- "Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage" + +Sixty-one people carry `health_insurance_premiums_without_medicare_part_b`, which the prompt labels "health insurance premiums excluding Medicare Part B". That line states the person's other premiums; it states no Part B amount. scenario_114's prompt has no premium line. It lists a single head, age 69, with Social Security retirement income, $10,000 of other medical expenses and $2,000 of over-the-counter health expenses. The engine leaves over-the-counter purchases out of itemized medical expenses. + +## Method + +1. **Sweep.** `scripts/sweep_part_b.py` rebuilds every household with `Scenario.to_pe_household` and computes every output and impact weight on policyengine-us 2.15.17 with `latest_final`, the system that built the references. Its baseline reproduces all 1,928 scored references. It then reruns every household under four readings: + - `no_part_b`: each person's `medicare_part_b_premium` is 0 (the prompt's unlisted-number rule; enrollment unchanged); + - `not_enrolled`: each person's `takes_up_medicare_if_eligible` is false, which removes every channel that reads enrollment (the rule against inferring health coverage); + - `not_enrolled_direct`: each person's `medicare_enrolled` is false, a differential check on `not_enrolled`; the two agree on every output; + - `irmaa_from_2026_income`: the income-related amount read on the household's 2026 MAGI instead of the unset 2024 MAGI, a check on the premium's size. + + For each household with a Medicare-eligible person it records each person's Medicare variables and a propagation trace. The trace lists every engine variable, at every 2026 period the simulation computed, whose value differs between the reference and the `no_part_b` or `not_enrolled` simulation. Households where an output moves also run on a grid: the Part B readings crossed with the four readings of the state income tax in SALT from #191 (withholding estimate, liability at a fixed point, none paid, none paid without the local sales tax estimate). Outputs: `verification/sweep_part_b.csv` (every output and impact weight under every reading), `verification/sweep_part_b_summary.json`, `verification/sweep_part_b_households.json` and `verification/sweep_part_b.log`. +2. **Explain.** `scripts/explain_households.py` writes `verification/part_b_households.csv`. Each of the 33 Medicare households gets a row with the premium, the federal itemization election and medical deduction with and without it, its federal, state and SNAP outputs, and the state variables its trace shows moving. +3. **Propose.** `scripts/propose_exclusions.py` writes `proposed_exclusions.json`. It holds a `reference_depends_on_unlisted_input` record, in the format of the frozen run's `reference_exclusions.json`, for every scored output that `no_part_b` moves by more than the $1 exact-match tolerance. An output #191 already proposes to exclude gets a standalone record under `conditional_on_salt_decision` instead, to install only if #191's record is not adopted. The script also writes `verification/model_answers.csv`, which tags every model's answer on the moved outputs with the readings it lands within $1 of. +4. **Impact.** `scripts/leaderboard_impact.py` copies the frozen run to scratch directories and never writes the snapshot. It scores each copy with `python -m policybench.cli analyze`, the command the freeze runs. The unchanged copy reproduces every compared value of the published payload. It then scores three cases: + - this audit's two records alone, as if #191 is not adopted; + - #191's three records alone, which reproduces #191's figures exactly; + - #191's three plus this audit's Virginia record. + + Outputs: `verification/leaderboard_impact.json`, `leaderboard_impact_models.csv`, `leaderboard_impact_marginal.csv` and `leaderboard_impact.log`. +5. **Verify.** Independent reviewers checked the work. Their reports are in `verification/independent_reviews.json`, and the adversarial review is in `verification/reviews/`. + +## What moves + +- **Who is charged.** 39 people in 33 households are Medicare-eligible, all of them by age. The engine charges the premium to 35 people in 30 households. The Medicare Savings Program pays it for the other 4 people, in scenario_001, scenario_043 and scenario_118. +- **What moves.** Under `no_part_b`, `not_enrolled` and `not_enrolled_direct`, exactly two outputs move by more than $1, both in scenario_114, and both are scored. +- **What does not.** No SNAP or eligibility output in any Medicare household changes by any amount under any reading. `irmaa_from_2026_income` moves no output. + +| Output | Reference | No Part B premium | Status | +|---|---:|---:|---| +| scenario_114 federal income tax before refundable credits | 10,729.61 | 11,265.27 | scored; #191 proposes to exclude it for the withholding estimate | +| scenario_114 Virginia income tax before refundable credits | 3,514.15 | 3,654.15 | scored; proposed here | + +The household itemizes. Its federal medical expense deduction is $12,434.80 of expenses less 7.5% of $106,572.56 AGI: $4,441.86 with the premium and $2,007.06 without it. The $2,434.80 difference is taxed at 22% federally ($535.66). Virginia's itemized deductions start from the federal ones, so it is also taxed at Virginia's 5.75% ($140.00). Virginia follows the federal election (`va_deductions` reads `tax_unit_itemizes`). + +### Why the other 29 households do not move + +From `verification/part_b_households.csv`: + +- **Below the floor.** In 7 households (000, 022, 033, 042, 055, 120, 122), medical expenses stay below 7.5% of AGI with or without the premium. 022 and 120 itemize, but their medical deduction is $0 either way. +- **Standard deduction.** In 21 households (002, 013, 021, 027, 031, 040, 044, 048, 059, 060, 062, 071, 085, 092, 095, 098, 104, 107, 108, 111 and 115), the deduction moves but the household takes the federal standard deduction under both readings. +- **Itemizes only with the premium.** scenario_072 itemizes with the premium and takes the standard deduction without it. Its federal income tax is $0 either way. +- **State variables.** State variables move in 13 households, all but one of them tax variables; 085's is Pennsylvania's child care medical expense. Every state output stays the same: + - $0 in AZ (013, 040), MO (021), CA (031), KS (044), OH (048, 107), NY (071, 104), AL (092) and NJ (095). In NJ, `nj_main_income_tax` moves by $26.35. + - $4.68 in AL (115), which takes Alabama's standard deduction either way. +- **SNAP.** SNAP's excess medical deduction moves in every household charged the premium. In 27 of them SNAP is $0 under both readings. In 013, 027 and 108, the expected contribution exceeds the maximum allotment under both, so SNAP pays the $24 monthly minimum either way. +- **Medicaid.** No Medicaid eligibility output turns on the premium. In particular, scenario_031's Medicaid reference (eligible) is the same under every reading (see "Related findings"). + +### scenario_114 under both unlisted inputs + +#191 shows that the federal SALT deduction's state income tax part is also an unlisted input. The two inputs interact through the itemization election: + +| Part B premium | SALT state income tax | Federal | Virginia | Itemizes | +|---|---|---:|---:|---| +| modeled | withholding estimate (reference) | 10,729.61 | 3,514.15 | yes | +| modeled | liability | 11,137.31 | 3,514.15 | yes | +| modeled | none paid | 11,758.09 | 3,514.15 | yes | +| modeled | none paid, no local sales tax | 11,783.48 | 3,514.15 | yes | +| none | withholding estimate | 11,265.27 | 3,654.15 | yes | +| none | liability | 11,642.16 | 3,654.15 | yes | +| none | none paid | 11,969.80 | 4,127.67 | no | +| none | none paid, no local sales tax | 11,969.80 | 4,127.67 | no | + +`not_enrolled` gives the same values as "none" in every row. + +No model answered within $1 of either frozen reference. Model answers land on the other readings (`verification/model_answers.csv`): + +- **Virginia.** GPT-6 Astra ($3,654.16) and GPT-6.1 Sol ($3,654.66) are within $1 of the value without the premium. +- **Federal, liability.** GPT-6.1 Sol ($11,642.09) and GPT-6 Astra ($11,642.20) are within $1 of the value without the premium under the liability. +- **Federal, literal.** Kimi K3 ($11,969.85) and GPT-5.6 Sol ($11,970.00) are within $1 of the literal reading: no premium and no state income tax paid. + +## Proposed records + +- **scenario_114 Virginia income tax.** One record in `proposed_exclusions.json` (`exclusions`): frozen $3,514.15, alternative $3,654.15. Its note gives the values under #191's readings. +- **scenario_114 federal income tax.** #191 proposes to exclude this output for the withholding estimate, and the loader refuses two records for one output. #191's record already quotes the values without the premium ($11,265.27, $11,642.16 and $11,969.80). + - If d963 adopts #191's record, a release adds the Part B input to that record's `unlisted_input` text; the exact words are in `proposed_exclusions.json`. + - If #191's record is not adopted, the output still rests on the Part B premium. `conditional_on_salt_decision` holds a standalone record for it: frozen $10,729.61, alternative $11,265.27. + +## Leaderboard impact + +From `verification/leaderboard_impact.json`. Scored outputs per model are 1,928 today. + +| Case | Records added | Scored outputs | Exact-rate change, all models | GPT-6 Sol exact | Always-zero exact | +|---|---:|---:|---|---:|---:| +| published | 0 | 1,928 | | 95.00% | 69.98% | +| this audit alone (if #191 is not adopted) | 2 | 1,926 | +0.18 to +0.26 points | 95.27% | 70.14% | +| #191 alone | 3 | 1,925 | +0.42 to +0.61 points | 95.61% | 70.39% | +| #191 and the Virginia record | 4 | 1,924 | +0.47 to +0.69 points | 95.69% | 70.44% | + +- **This audit alone.** No exact or bounded-score rank changes. One within-1% pair swaps: Claude Opus 4.8 and MiniMax M3 (#39/#40). Exact match rises from 61.6% to 62.4% for federal income tax and from 64.3% to 65.1% for state income tax. +- **#191 and the Virginia record.** The rank changes are #191's: + - exact: Claude Sonnet 5.5 passes GPT-6 Luna for fourth; + - within 1%: three adjacent pairs swap; + - bounded score: three adjacent pairs swap, plus Grok 4.3 and Gemini 3.1 Flash Lite Preview (#35/#36). + + On top of #191's records, the Virginia record adds 0.05 to 0.08 points to every model's exact rate and changes no exact rank. + +Every model missed both scenario_114 outputs, so excluding them raises every model's rate. + +## Related findings + +- **Stale impact weights, with no effect on scores.** The published `reference_outputs.csv` carries impact weights for 90 eligibility outputs that differ from those 2.15.17 computes. They comprise 39 Medicare, 37 Medicaid, 9 school-meal and 5 WIC outputs. For example, `head_medicare_eligible` is $5,285.20 published and $12,065.20 on 2.15.17, a gap of $6,780.00. The references' 2026-09-29 rebuild (`../2026-09-28/scripts/build_references_latest.py`) rewrote the `value` column only. `leaderboard_impact.py --weights-check` scores a copy with 2.15.17's weights, and no compared payload value changes. +- **Enrollment changes only weights.** `not_enrolled` sets `medicare_cost`, the impact weight of the 39 Medicare eligibility outputs, to 0. It changes no output. +- **scenario_031's Medicaid annotations name the wrong mechanism.** They explain the reference (eligible, through California's optional senior-or-disabled pathway) by subtracting the Part B premium from income, and 43 row annotations fault models for not doing so. + - On 2.15.17, `medicaid_optional_senior_or_disabled_countable_income` subtracts no premium. It applies SSI income rules with California's $230 monthly disregard: $23,853.47 less $2,760.00 is $21,093.47, under the $22,024.80 limit (138% of the 2026 poverty guideline). + - The values are the same under every reading here, so the reference is unaffected. The annotation text needs correcting; a separate task covers it. +- **scenario_114's case notes.** The federal note lists "missing the Part B premium" among model errors. The Virginia note says the medical deduction includes the $2,000 of over-the-counter expenses, which the engine leaves out. A release adopting the records rewords both. +- **#191's README rounding.** With its records, #191 gives GPT-6 Sol's exact rate as 95.62% and the always-zero baseline as 70.40%. Its own outputs give 95.6146 and 70.3946, and this audit's #191-alone case reproduces both, so they round to 95.61% and 70.39%. + +## What a release adopting the records must also do + +Two sets of counts follow, one if d963 adopts #191's three records and one if it does not. + +1. **Adjudications.** Add a developer adjudication (`excluded_from_scoring: true`, `adjudicated_failure_source: prompt_ambiguity`) to `annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_adjudications.json` for each record. Without them, `scripts/freeze_snapshot.py`'s `reference_exclusions_block` stops the freeze. +2. **Annotation rows.** Relabel the excluded outputs' `llm_error` rows to `prompt_ambiguity`, as for every other exclusion (`tests/test_paper_results.py` checks this): + - scenario_114 Virginia: 45 rows; + - scenario_114 federal: 45 rows, if the standalone record is used. + + Reword the two case notes above. +3. **Pinned counts.** Each output carries 46 model rows, and none is an exact match. + + | Count | With #191 adopted | Without #191 | + |---|---|---| + | exclusions | 59 → 60 | 56 → 58 | + | unlisted-input exclusions | 31 → 32 | 28 → 30 | + | households with an exclusion | stays 40 | 39 → 40 | + | scored outputs per model | 1,925 → 1,924 | 1,928 → 1,926 | + | 2.15.17-era records | 7 → 8 | 4 → 6 | + | adjudications | 72 → 73 | 69 → 71 | + | audited rows | 7,722 → 7,676 | 7,860 → 7,768 | + | exact-match misses | 7,718 → 7,672 | 7,856 → 7,764 | + +4. **Paper.** Add the Medicare enrollment and Part B premium input to the paper's unlisted-input list and sweep narrative, and re-render the abstract and tables. diff --git a/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json b/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json new file mode 100644 index 00000000..d6602f52 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json @@ -0,0 +1,121 @@ +{ + "schema_version": 1, + "status": "proposed 2026-10-05; changes published scores, so it waits for Max's ruling (cos decision). decided_on is the draft date; a release sets it to the ruling's date.", + "basis": "policybench/reference_exclusions.py: an output is excluded for every model when its reference depends on an engine input the prompt never listed and a careful reader could take the stated facts the other way.", + "salt_proposal": { + "pr": "PolicyEngine/policybench#191", + "head": "8af912a062dd7ae1373de4c043ae2727d3406930", + "decision": "d963", + "outputs": [ + "scenario_022/federal_income_tax_before_refundable_credits", + "scenario_081/federal_income_tax_before_refundable_credits", + "scenario_114/federal_income_tax_before_refundable_credits" + ] + }, + "exclusions": [ + { + "scenario_id": "scenario_114", + "variable": "state_income_tax_before_refundable_credits", + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it, and Virginia's itemized deductions, which start from the federal itemized deductions (va_itemized_deductions), carry it too. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", + "frozen_value": 3514.14501953125, + "alternative_value": 3654.14599609375, + "engine_version": "policyengine-us 2.15.17", + "decided_on": "2026-10-05", + "decided_by": "developer", + "unlisted_input": "Medicare enrollment and a Medicare Part B premium paid during 2026, which the medical expense deductions count; policyengine-us treats every Medicare-eligible person as enrolled (takes_up_medicare_if_eligible defaults to true, so medicare_enrolled follows is_medicare_eligible) and adds the modeled premium (medicare_part_b_premium) to medical expenses, and the household data never set either", + "note": "Found 2026-10-05 by sweeping every reference with each person's Medicare Part B premium at 0, and again with Medicare enrollment off (reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py, on policyengine-us 2.15.17 with latest_final; its baseline reproduces all 1,928 scored references). Of 39 Medicare-eligible people in 33 households, the engine charges the premium in 30 (Medicare Savings Program coverage pays it for the 4 eligible people in the other 3); only this household's two tax outputs move, under either reading. The federal medical expense deduction is $4,441.86 in the reference ($12,434.80 of expenses less 7.5% of $106,572.56 AGI) and $2,007.06 without the premium; the household itemizes under both. The output also depends on the state income tax in the federal SALT deduction, which PolicyEngine/policybench#191 proposes to treat as an unlisted input (decision d963), through the federal itemization election, which Virginia follows (va_deductions reads tax_unit_itemizes). Without the premium the output is $3,654.15 under both the withholding estimate and the liability; with no state income tax paid it is $4,127.67, with or without the local sales tax estimate, because the household then takes the federal and Virginia standard deductions. With the premium it is $3,514.15 under all four readings. The premium is the standard premium: the engine reads the income-related adjustment from MAGI two years earlier, which no household sets, and at this household's 2026 MAGI ($106,572.56) none would apply. GPT-6 Astra ($3,654.16) and GPT-6.1 Sol ($3,654.66) answered within $1 of the alternative value, and no model within $1 of the frozen value." + } + ], + "conditional_on_salt_decision": [ + { + "record": { + "scenario_id": "scenario_114", + "variable": "federal_income_tax_before_refundable_credits", + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", + "frozen_value": 10729.6123046875, + "alternative_value": 11265.267578125, + "engine_version": "policyengine-us 2.15.17", + "decided_on": "2026-10-05", + "decided_by": "developer", + "unlisted_input": "Medicare enrollment and a Medicare Part B premium paid during 2026, which the medical expense deductions count; policyengine-us treats every Medicare-eligible person as enrolled (takes_up_medicare_if_eligible defaults to true, so medicare_enrolled follows is_medicare_eligible) and adds the modeled premium (medicare_part_b_premium) to medical expenses, and the household data never set either", + "note": "Found 2026-10-05 by sweeping every reference with each person's Medicare Part B premium at 0, and again with Medicare enrollment off (reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py, on policyengine-us 2.15.17 with latest_final; its baseline reproduces all 1,928 scored references). Of 39 Medicare-eligible people in 33 households, the engine charges the premium in 30 (Medicare Savings Program coverage pays it for the 4 eligible people in the other 3); only this household's two tax outputs move, under either reading. The federal medical expense deduction is $4,441.86 in the reference ($12,434.80 of expenses less 7.5% of $106,572.56 AGI) and $2,007.06 without the premium; the household itemizes under both. Under the readings of the state income tax in SALT that PolicyEngine/policybench#191 sets out, without the premium the output is $11,642.16 under the liability and $11,969.80 with no state income tax paid (the household then takes the standard deduction). The premium is the standard premium: the engine reads the income-related adjustment from MAGI two years earlier, which no household sets, and at this household's 2026 MAGI ($106,572.56) none would apply. No model answered within $1 of the alternative value, and no model within $1 of the frozen value. GPT-6.1 Sol ($11,642.09) and GPT-6 Astra ($11,642.20) answered within $1 of the value with no premium and the liability, and Kimi K3 ($11,969.85) and GPT-5.6 Sol ($11,970.00) within $1 of the value with no premium and no state income tax paid." + }, + "readings": { + "salt_by_part_b": { + "modeled": { + "estimate": 10729.6123046875, + "liability": 11137.306640625, + "zero": 11758.08984375, + "zero_no_local_sales": 11783.4775390625 + }, + "no_part_b": { + "estimate": 11265.267578125, + "liability": 11642.162109375, + "zero": 11969.8037109375, + "zero_no_local_sales": 11969.8037109375 + } + }, + "itemizes": { + "modeled|estimate": true, + "modeled|liability": true, + "modeled|zero": true, + "modeled|zero_no_local_sales": true, + "no_part_b|estimate": true, + "no_part_b|liability": true, + "no_part_b|zero": false, + "no_part_b|zero_no_local_sales": false, + "not_enrolled|estimate": true, + "not_enrolled|liability": true, + "not_enrolled|zero": false, + "not_enrolled|zero_no_local_sales": false + }, + "not_enrolled": 11265.267578125 + }, + "install_if": "PolicyEngine/policybench#191's record for this output (decision d963) is not adopted. The output then still rests on the Part B premium and needs this record.", + "if_salt_record_adopted": "PolicyEngine/policybench#191's record already excludes the output, and its note already gives the values without the premium (it quotes $11,265.27, $11,642.16 and $11,969.80). A release adds the unlisted input to that record's unlisted_input text: '; also Medicare enrollment and a Medicare Part B premium paid during 2026, which the medical expense deductions count; policyengine-us treats every Medicare-eligible person as enrolled (takes_up_medicare_if_eligible defaults to true, so medicare_enrolled follows is_medicare_eligible) and adds the modeled premium (medicare_part_b_premium) to medical expenses, and the household data never set either'.", + "salt_record_frozen_value": 10729.6123046875, + "salt_record_alternative_value": 11137.306640625 + } + ], + "already_excluded": [], + "readings": { + "scenario_114/state_income_tax_before_refundable_credits": { + "salt_by_part_b": { + "modeled": { + "estimate": 3514.14501953125, + "liability": 3514.14501953125, + "zero": 3514.14501953125, + "zero_no_local_sales": 3514.14501953125 + }, + "no_part_b": { + "estimate": 3654.14599609375, + "liability": 3654.14599609375, + "zero": 4127.66650390625, + "zero_no_local_sales": 4127.66650390625 + } + }, + "itemizes": { + "modeled|estimate": true, + "modeled|liability": true, + "modeled|zero": true, + "modeled|zero_no_local_sales": true, + "no_part_b|estimate": true, + "no_part_b|liability": true, + "no_part_b|zero": false, + "no_part_b|zero_no_local_sales": false, + "not_enrolled|estimate": true, + "not_enrolled|liability": true, + "not_enrolled|zero": false, + "not_enrolled|zero_no_local_sales": false + }, + "not_enrolled": 3654.14599609375 + } + }, + "readings_moving_no_further_output": [ + "not_enrolled", + "not_enrolled_direct", + "irmaa_from_2026_income" + ] +} diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/explain_households.py b/reference_audit/2026-10-05-medicare-part-b/scripts/explain_households.py new file mode 100644 index 00000000..309f8a04 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/explain_households.py @@ -0,0 +1,103 @@ +"""Tabulate, for every household with a Medicare-eligible person, where the premium goes. + +Reads verification/sweep_part_b_households.json and writes +verification/part_b_households.csv: one row per household with the premium the engine +charges, the federal itemization election and medical expense deduction with and +without it, the household's federal, state and SNAP outputs, the state-tax variables +the ``no_part_b`` propagation trace shows moving, and whether any output moved. + + python reference_audit/2026-10-05-medicare-part-b/scripts/explain_households.py +""" + +from __future__ import annotations + +import json +import re +from pathlib import Path + +import pandas as pd + +HERE = Path(__file__).resolve().parents[1] +HOUSEHOLDS = HERE / "verification/sweep_part_b_households.json" +OUT = HERE / "verification/part_b_households.csv" +FEDERAL = "federal_income_tax_before_refundable_credits" +STATE = "state_income_tax_before_refundable_credits" +STATE_PREFIX = re.compile(r"^[a-z]{2}_") +# Engine variables with a two-letter prefix that are not state programs. +NOT_STATE = ("tax_", "is_", "ca_oc_") + + +def main() -> None: + households = json.loads(HOUSEHOLDS.read_text())["households"] + rows = [] + for h in households: + if not h["medicare_eligible"]: + continue + person = h["baseline_person"] + base, no_b = h["baseline_units"], h["readings"]["no_part_b"]["units"] + outputs = h["baseline"] + no_b_outputs = h["readings"]["no_part_b"]["outputs"] + trace = h["readings"]["no_part_b"]["trace"] + state_vars = sorted( + r["variable"] + for r in trace + if STATE_PREFIX.match(r["variable"]) + and not r["variable"].startswith(NOT_STATE) + ) + itemizes_moved = any(r["variable"] == "tax_unit_itemizes" for r in trace) + moved = sorted( + v + for v in outputs + if ( + round(no_b_outputs[v]) != round(outputs[v]) + if v.endswith("_eligible") + else abs(no_b_outputs[v] - outputs[v]) > 1.0 + ) + ) + rows.append( + { + "scenario_id": h["scenario_id"], + "state": h["state"], + "medicare_eligible_people": sum( + person["is_medicare_eligible"].values() + ), + "part_b_premium": sum(person["medicare_part_b_premium"].values()), + "msp_part_b_coverage": sum( + person["msp_part_b_premium_coverage"].values() + ), + "federal_itemizes": bool(base["tax_unit_itemizes"]), + "federal_itemizes_without_part_b": ( + (not bool(base["tax_unit_itemizes"])) + if itemizes_moved + else bool(base["tax_unit_itemizes"]) + ), + "medical_expense_deduction": base["medical_expense_deduction"], + "medical_expense_deduction_without_part_b": no_b[ + "medical_expense_deduction" + ], + "federal_output": outputs.get(FEDERAL), + "state_output": outputs.get(STATE), + "snap": outputs.get("snap"), + "snap_without_part_b": no_b_outputs.get("snap"), + "state_variables_moving": ";".join(state_vars), + "outputs_moved": ";".join(moved), + } + ) + table = pd.DataFrame(rows) + table.to_csv(OUT, index=False) + with pd.option_context("display.width", 250, "display.max_rows", 100): + print( + table.drop(columns=["state_variables_moving"]) + .round(2) + .to_string(index=False) + ) + print() + print( + table[["scenario_id", "state", "state_variables_moving"]].to_string( + index=False + ) + ) + + +if __name__ == "__main__": + main() diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py b/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py new file mode 100644 index 00000000..1ae17b9d --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py @@ -0,0 +1,404 @@ +"""Score the frozen run under each combination of the Part B and SALT proposals. + +Copies the frozen run (predictions, references, scenarios, exclusions) to scratch +directories and never writes the snapshot. Each copy is scored with ``python -m +policybench.cli analyze``, the command the freeze runs, and the dashboard payloads are +compared. The cases: + +``published`` + The exclusion record unchanged. It must reproduce the published payload's scoring: + every modelStats field except the cost and latency fields the freeze overlays, and + programStats, heatmap, globalWeights and failureModes exactly. +``part_b`` + This audit's records if PolicyEngine/policybench#191 is not adopted: scenario_114's + Virginia output and the standalone Part B record for its federal output. +``salt`` + #191's three records alone (022, 081 and 114 federal), as #191 scored them. +``salt_and_part_b`` + #191's three records and this audit's Virginia record. + +``--weights-check`` also scores a copy whose eligibility impact weights are the ones +policyengine-us 2.15.17 computes (verification/sweep_part_b.csv) instead of the +published ones, which the 2026-09-29 rebuild did not recompute, and reports whether any +compared payload value changes. + + PYTHONPATH= /.venv-pe21517/bin/python \\ + reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py \\ + --scratch --weights-check +""" + +from __future__ import annotations + +import argparse +import gzip +import hashlib +import json +import os +import shutil +import subprocess +import sys +from pathlib import Path + +import pandas as pd + +HERE = Path(__file__).resolve().parents[1] +ROOT = HERE.parents[1] +RUN = ( + ROOT + / "paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace" +) +PROPOSED = HERE / "proposed_exclusions.json" +SWEEP = HERE / "verification/sweep_part_b.csv" +OUT_DIR = HERE / "verification" +SALT_HEAD = "8af912a062dd7ae1373de4c043ae2727d3406930" +SALT_PATH = "reference_audit/2026-10-05/proposed_exclusions.json" +RUN_FILES = ( + "predictions.csv.gz", + "reference_outputs.csv", + "reference_outputs.csv.meta.json", + "reference_exclusions.json", + "scenarios.csv", + "scenarios.csv.meta.json", +) +# The freeze overlays these from the published payload; the analyze CLI does not. +OVERLAID = {"costUsd", "costPerHousehold", "totalTokens", "latencySeconds"} +METRICS = ( + "exact", + "within1pct", + "within5pct", + "within10pct", + "score", + "outputGroupScore", +) +EXACT_PAYLOAD_KEYS = ("programStats", "heatmap", "globalWeights", "failureModes") +RANKED = ("exact", "within1pct", "score") + + +def always_zero(run_dir: Path) -> dict[str, float]: + """The always-zero baseline, as paper_results._always_zero_weighted_rates scores it.""" + import numpy as np + + from policybench.analysis import weighted_hit_rate_scores_by_model + from policybench.reference_exclusions import scored_reference_for + from policybench.spec import get_output_ids, output_group_id + + truth, _ = scored_reference_for(run_dir / "reference_outputs.csv") + headline = set(get_output_ids("us", "headline")) + truth = truth[truth["variable"].map(output_group_id).isin(headline)] + truth = truth.reset_index(drop=True) + truth["scenario_id"] = truth["scenario_id"].astype(str) + scenarios = pd.read_csv(run_dir / "scenarios.csv") + market = dict( + zip( + scenarios["scenario_id"].astype(str), + pd.to_numeric(scenarios["total_income"], errors="coerce").fillna(0.0), + ) + ) + predictions = truth[["scenario_id", "variable"]].copy() + predictions["model"] = "Always zero" + predictions["prediction"] = np.zeros(len(truth)) + scored = weighted_hit_rate_scores_by_model(truth, predictions, market, country="us") + return { + "exact": float(scored["weighted_exact"].mean()) * 100, + "within1pct": float(scored["weighted_within_1pct"].mean()) * 100, + } + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def salt_records() -> list[dict]: + text = subprocess.run( + ["git", "show", f"{SALT_HEAD}:{SALT_PATH}"], + cwd=ROOT, + check=True, + capture_output=True, + text=True, + ).stdout + return json.loads(text)["exclusions"] + + +def stage(target: Path, extra: list[dict], weights: pd.DataFrame | None = None) -> Path: + if target.exists(): + shutil.rmtree(target) + target.mkdir(parents=True) + for name in RUN_FILES: + shutil.copy2(RUN / name, target / name) + if extra: + record = json.loads((target / "reference_exclusions.json").read_text()) + record["exclusions"].extend(extra) + (target / "reference_exclusions.json").write_text( + json.dumps(record, indent=2) + "\n" + ) + if weights is not None: + reference = pd.read_csv(target / "reference_outputs.csv") + keyed = weights.set_index(["scenario_id", "variable"])["baseline_impact_weight"] + keys = list(zip(reference["scenario_id"], reference["variable"])) + has = reference["impact_weight"].notna() + reference.loc[has, "impact_weight"] = [ + float(keyed[k]) for k, h in zip(keys, has) if h + ] + reference.to_csv(target / "reference_outputs.csv", index=False) + # The analyzer checks the CSV against the digest its sidecar pins; repin the + # scratch copy's sidecar to the rewritten CSV. + meta_path = target / "reference_outputs.csv.meta.json" + meta = json.loads(meta_path.read_text()) + meta["reference_csv_sha256"] = sha256(target / "reference_outputs.csv") + meta_path.write_text(json.dumps(meta, indent=2) + "\n") + return target + + +def analyze(run_dir: Path) -> dict: + out = run_dir / "analysis" + dashboard = run_dir / "dashboard.json" + env = dict(os.environ, PYTHONPATH=str(ROOT), PYTHONDONTWRITEBYTECODE="1") + subprocess.run( + [ + sys.executable, + "-m", + "policybench.cli", + "analyze", + "-g", + str(run_dir / "reference_outputs.csv"), + "-p", + str(run_dir / "predictions.csv.gz"), + "-s", + str(run_dir / "scenarios.csv"), + "-o", + str(out), + "--app-data-output", + str(dashboard), + "--reference-digest", + sha256(run_dir / "reference_outputs.csv"), + ], + cwd=ROOT, + env=env, + check=True, + stdout=subprocess.DEVNULL, + ) + return json.loads(dashboard.read_text())["countries"]["us"] + + +def payload_differences(a: dict, b: dict) -> list[str]: + """Compared payload values that differ (modelStats less the overlaid fields).""" + out = [key for key in EXACT_PAYLOAD_KEYS if a[key] != b[key]] + x = {m["model"]: m for m in a["modelStats"]} + y = {m["model"]: m for m in b["modelStats"]} + if set(x) != set(y): + return out + ["modelStats: model sets differ"] + for model, row in x.items(): + for field, value in row.items(): + if field not in OVERLAID and value != y[model].get(field): + out.append(f"modelStats.{model}.{field}") + return out + + +def check_reproduces_published(base: dict) -> None: + published = json.loads(gzip.decompress((RUN / "data.json.gz").read_bytes())) + diffs = payload_differences(published, base) + if diffs: + raise SystemExit( + f"unchanged copy does not reproduce the published payload: {diffs[:10]}" + ) + + +def ranks(frame: pd.DataFrame, metric: str) -> pd.Series: + return frame[metric].rank(ascending=False, method="min").astype(int) + + +def compare(base: dict, case: dict) -> pd.DataFrame: + a = pd.DataFrame(base["modelStats"]).set_index("model") + b = pd.DataFrame(case["modelStats"]).set_index("model") + rows = pd.DataFrame(index=a.index) + for metric in METRICS: + rows[f"{metric}_published"] = a[metric] + rows[f"{metric}_case"] = b[metric] + rows[f"{metric}_delta"] = b[metric] - a[metric] + for metric in RANKED: + rows[f"{metric}_rank_published"] = ranks(a, metric) + rows[f"{metric}_rank_case"] = ranks(b, metric) + rows["n_published"] = a["n"] + rows["n_case"] = b["n"] + return rows.sort_values("exact_rank_published") + + +def program_rows(base: dict, case: dict) -> pd.DataFrame: + a = pd.DataFrame(base["programStats"]).set_index("variable") + b = pd.DataFrame(case["programStats"]).set_index("variable") + rows = pd.DataFrame(index=a.index) + for metric in ("exact", "score", "n"): + rows[f"{metric}_published"] = a[metric] + rows[f"{metric}_case"] = b[metric] + rows[f"{metric}_delta"] = b[metric] - a[metric] + return rows[(rows.filter(like="_delta").abs() > 1e-9).any(axis=1)] + + +def rank_changes(models: pd.DataFrame, metric: str) -> dict: + return { + model: { + "published": int(row[f"{metric}_rank_published"]), + "case": int(row[f"{metric}_rank_case"]), + } + for model, row in models.iterrows() + if row[f"{metric}_rank_published"] != row[f"{metric}_rank_case"] + } + + +def summarize( + name: str, records: list[dict], base: dict, case: dict, zero: dict +) -> dict: + models = compare(base, case) + leader = models.sort_values("exact_rank_case").index[0] + return { + "case": name, + "exclusions_added": [f"{r['scenario_id']}/{r['variable']}" for r in records], + "scored_outputs": { + "published": int(models["n_published"].iloc[0]), + "case": int(models["n_case"].iloc[0]), + }, + "exact_delta_pp": { + "min": float(models["exact_delta"].min()), + "max": float(models["exact_delta"].max()), + "mean": float(models["exact_delta"].mean()), + }, + "within1pct_delta_pp": { + "min": float(models["within1pct_delta"].min()), + "max": float(models["within1pct_delta"].max()), + }, + "score_delta": { + "min": float(models["score_delta"].min()), + "max": float(models["score_delta"].max()), + }, + **{f"{m}_rank_changes": rank_changes(models, m) for m in RANKED}, + "always_zero_baseline": zero, + "leader": leader, + "leader_exact": { + "published": float(models.loc[leader, "exact_published"]), + "case": float(models.loc[leader, "exact_case"]), + }, + "leader_margin_over_always_zero_exact": { + "published": float( + models.loc[leader, "exact_published"] - zero["published"]["exact"] + ), + "case": float(models.loc[leader, "exact_case"] - zero["case"]["exact"]), + }, + "programs": program_rows(base, case).round(6).reset_index().to_dict("records"), + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--scratch", required=True) + parser.add_argument("--weights-check", action="store_true") + args = parser.parse_args() + scratch = Path(args.scratch) + + proposal = json.loads(PROPOSED.read_text()) + va = proposal["exclusions"] + federal = [c["record"] for c in proposal["conditional_on_salt_decision"]] + salt = salt_records() + cases = { + "part_b": va + federal, + "salt": salt, + "salt_and_part_b": salt + va, + } + + base_dir = stage(scratch / "published", []) + base = analyze(base_dir) + check_reproduces_published(base) + zero_published = always_zero(base_dir) + + summaries, tables = [], {} + for name, records in cases.items(): + run_dir = stage(scratch / name, records) + case = analyze(run_dir) + zero = {"published": zero_published, "case": always_zero(run_dir)} + summaries.append(summarize(name, records, base, case, zero)) + tables[name] = compare(base, case) + + # The marginal effect of the Virginia record on top of #191's records. + salt_models = tables["salt"] + both = tables["salt_and_part_b"] + marginal = pd.DataFrame( + { + "exact_salt": salt_models["exact_case"], + "exact_salt_and_part_b": both["exact_case"], + "exact_delta": both["exact_case"] - salt_models["exact_case"], + "rank_salt": salt_models["exact_rank_case"], + "rank_salt_and_part_b": both["exact_rank_case"], + "score_delta": both["score_case"] - salt_models["score_case"], + } + ) + marginal_summary = { + "exact_delta_pp": { + "min": float(marginal["exact_delta"].min()), + "max": float(marginal["exact_delta"].max()), + }, + "exact_rank_changes": { + model: { + "salt": int(r.rank_salt), + "salt_and_part_b": int(r.rank_salt_and_part_b), + } + for model, r in marginal.iterrows() + if r.rank_salt != r.rank_salt_and_part_b + }, + } + + weights_check = None + if args.weights_check: + sweep = pd.read_csv(SWEEP) + run_dir = stage(scratch / "weights_2_15_17", [], weights=sweep) + moved = int( + ( + sweep["reference_impact_weight"].notna() + & ( + ( + sweep["reference_impact_weight"] + - sweep["baseline_impact_weight"] + ).abs() + > 1e-3 + ) + ).sum() + ) + diffs = payload_differences(base, analyze(run_dir)) + weights_check = { + "impact_weights_replaced": moved, + "payload_differences": diffs, + } + + OUT_DIR.mkdir(parents=True, exist_ok=True) + wide = pd.concat( + { + name: table[ + ["exact_case", "exact_rank_case", "score_case", "score_rank_case"] + ] + for name, table in tables.items() + }, + axis=1, + ) + wide.columns = [f"{case}_{col.removesuffix('_case')}" for case, col in wide.columns] + first = tables["part_b"] + wide.insert(0, "exact_published", first["exact_published"]) + wide.insert(1, "exact_rank_published", first["exact_rank_published"]) + wide.insert(2, "score_published", first["score_published"]) + wide.insert(3, "score_rank_published", first["score_rank_published"]) + wide.to_csv(OUT_DIR / "leaderboard_impact_models.csv") + marginal.to_csv(OUT_DIR / "leaderboard_impact_marginal.csv") + result = { + "published_reproduced": True, + "cases": summaries, + "virginia_record_on_top_of_salt": marginal_summary, + "impact_weights_check": weights_check, + } + (OUT_DIR / "leaderboard_impact.json").write_text( + json.dumps(result, indent=2) + "\n" + ) + with pd.option_context("display.width", 250, "display.max_rows", 100): + print(wide.round(4)) + print(marginal.round(4)) + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py b/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py new file mode 100644 index 00000000..14e75341 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py @@ -0,0 +1,384 @@ +"""Draft unlisted-input exclusions for outputs that rest on the engine's assumed Medicare Part B premium. + +Reads the sweep (sweep_part_b.py) and the frozen run's predictions and writes +``proposed_exclusions.json``: one ``reference_depends_on_unlisted_input`` record, in the +format of the frozen run's ``reference_exclusions.json``, for every scored output that +the ``no_part_b`` reading moves by more than the $1 exact-match tolerance (a binary +output, if it flips). + +An output that PolicyEngine/policybench#191 (the state income tax in SALT audit, +reference_audit/2026-10-05 on its branch) already proposes to exclude cannot take a +second record: the loader refuses duplicate keys. For such an output the file gives a +standalone Part B record to install only if #191's record is not adopted. + +The records are a proposal. They change published scores, so they wait for Max's +ruling; ``decided_on`` is the date the proposal was drafted and a release sets it to +the ruling's date. + + PYTHONPATH= /.venv-pe21517/bin/python \\ + reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py +""" + +from __future__ import annotations + +import json +import re +import subprocess +from pathlib import Path + +import pandas as pd + +HERE = Path(__file__).resolve().parents[1] +ROOT = HERE.parents[1] +RUN = ( + ROOT + / "paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace" +) +SUMMARY = HERE / "verification/sweep_part_b_summary.json" +HOUSEHOLDS = HERE / "verification/sweep_part_b_households.json" +OUT = HERE / "proposed_exclusions.json" +ANSWERS = HERE / "verification/model_answers.csv" +MODEL_META = ROOT / "app/src/modelMeta.ts" +# PolicyEngine/policybench#191 at the head this audit read. +SALT_PR = "PolicyEngine/policybench#191" +SALT_HEAD = "8af912a062dd7ae1373de4c043ae2727d3406930" +SALT_PATH = "reference_audit/2026-10-05/proposed_exclusions.json" +SALT_DECISION = "d963" +DRAFTED_ON = "2026-10-05" +ENGINE = "policyengine-us 2.15.17" +FEDERAL = "federal_income_tax_before_refundable_credits" +STATE = "state_income_tax_before_refundable_credits" +TOLERANCE = 1.0 +UNLISTED_INPUT = ( + "Medicare enrollment and a Medicare Part B premium paid during 2026, which the " + "medical expense deductions count; policyengine-us treats every Medicare-eligible " + "person as enrolled (takes_up_medicare_if_eligible defaults to true, so " + "medicare_enrolled follows is_medicare_eligible) and adds the modeled premium " + "(medicare_part_b_premium) to medical expenses, and the household data never set " + "either" +) +SALT_READINGS = { + "estimate": "the withholding estimate the reference uses", + "liability": "the household's state liability", + "zero": "no state income tax paid", + "zero_no_local_sales": "no state income tax paid and no local sales tax estimate", +} + + +def money(value: float) -> str: + return f"${value:,.2f}" + + +def listed(value: float) -> str: + """A listed input, as the prompt prints it (whole dollars).""" + return f"${value:,.0f}" + + +def model_labels() -> dict[str, str]: + """The dashboard's display names (app/src/modelMeta.ts MODEL_LABELS).""" + text = MODEL_META.read_text() + block = text.split("export const MODEL_LABELS", 1)[1].split("};", 1)[0] + return dict(re.findall(r'"([^"]+)":\s*"([^"]+)"', block)) + + +def salt_proposal() -> dict: + text = subprocess.run( + ["git", "show", f"{SALT_HEAD}:{SALT_PATH}"], + cwd=ROOT, + check=True, + capture_output=True, + text=True, + ).stdout + return json.loads(text) + + +def model_answers(rows: list[dict], household: dict) -> pd.DataFrame: + """Every model's answer on each moved output, tagged with the readings it matches.""" + predictions = pd.read_csv(RUN / "predictions.csv.gz", low_memory=False) + out = [] + for row in rows: + sid, variable = row["scenario_id"], row["variable"] + values = {"frozen": row["reference"]} + for key, cell in household[sid]["grid"].items(): + values[key] = cell["outputs"][variable] + sub = predictions[ + (predictions["scenario_id"] == sid) & (predictions["variable"] == variable) + ] + for answer in sub.itertuples(): + matches = ( + [] + if pd.isna(answer.prediction) + else [ + key + for key, value in values.items() + if abs(float(answer.prediction) - value) <= TOLERANCE + ] + ) + out.append( + { + "scenario_id": sid, + "variable": variable, + "model": answer.model, + "prediction": answer.prediction, + "within_1_of": ";".join(matches), + } + ) + return pd.DataFrame(out).sort_values(["scenario_id", "variable", "prediction"]) + + +def within(answers: pd.DataFrame, sid: str, variable: str, reading: str) -> list[dict]: + sub = answers[ + (answers["scenario_id"] == sid) + & (answers["variable"] == variable) + & answers["within_1_of"].str.split(";").apply(lambda keys: reading in keys) + ] + return [ + {"model": r.model, "prediction": float(r.prediction)} for r in sub.itertuples() + ] + + +def answer_text(models: list[dict]) -> str: + if not models: + return "no model" + labels = model_labels() + return " and ".join( + f"{labels[m['model']]} ({money(m['prediction'])})" for m in models + ) + + +def record(row: dict, household: dict, summary: dict, answers: pd.DataFrame) -> dict: + sid, variable = row["scenario_id"], row["variable"] + base = household["baseline_units"] + person = household["baseline_person"] + no_b = household["readings"]["no_part_b"]["units"] + grid = household["grid"] + head_age = int(person["age"]["head"]) + premium = sum(person["medicare_part_b_premium"].values()) + other = sum(person["other_medical_expenses"].values()) + agi = base["adjusted_gross_income"] + is_state = variable == STATE + alternative = grid["no_part_b|estimate"]["outputs"][variable] + not_enrolled = household["readings"]["not_enrolled"]["outputs"][variable] + assert abs(alternative - row["value"]) < 1e-6 + assert abs(not_enrolled - alternative) < 1e-6 + # The note says no income-related amount would apply at 2026 MAGI. + irmaa = household["readings"]["irmaa_from_2026_income"]["person"] + assert ( + irmaa["gross_medicare_part_b_premium"] + == person["gross_medicare_part_b_premium"] + ) + assert person["gross_medicare_part_b_premium"] == person["base_part_b_premium"] + salt_values = { + part_b: { + salt: grid[f"{part_b}|{salt}"]["outputs"][variable] + for salt in SALT_READINGS + } + for part_b in ("modeled", "no_part_b") + } + itemizes = {key: bool(cell["itemizes"]) for key, cell in grid.items()} + where = ( + ", and Virginia's itemized deductions, which start from the federal itemized " + "deductions (va_itemized_deductions), carry it too" + if is_state + else "" + ) + alternative_reading = ( + "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its " + "rules say to treat any unlisted numeric input as 0 and not to infer unlisted " + "expenses or health coverage. The reference counts a modeled Part B premium as a " + "medical expense: policyengine-us treats the head (age " + f"{head_age}, so Medicare-eligible) as enrolled because " + "takes_up_medicare_if_eligible defaults to true, and " + "medical_expense_health_insurance_premiums adds medicare_part_b_premium, the " + f"2026 standard premium of $202.90 a month, {money(premium)}, to the listed " + f"{listed(other)} of other medical expenses. The household itemizes, so the " + "federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of " + f"AGI) counts it{where}. Read with no Part B premium paid, the " + f"deduction falls by {money(premium)} and the output is the alternative value." + ) + msp_households = sorted( + set(summary["medicare_households"]) - set(summary["part_b_households"]) + ) + assert {sid for sid, _ in summary["msp_part_b_coverage_people"]} == set( + msp_households + ) + note = ( + "Found 2026-10-05 by sweeping every reference with each person's Medicare Part B " + "premium at 0, and again with Medicare enrollment off " + "(reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py, on " + "policyengine-us 2.15.17 with latest_final; its baseline reproduces all " + f"{summary['scored_outputs']:,} scored references). Of " + f"{summary['medicare_eligible_people']} Medicare-eligible people in " + f"{len(summary['medicare_households'])} households, the engine charges the " + f"premium in {len(summary['part_b_households'])} (Medicare Savings Program " + f"coverage pays it for the {len(summary['msp_part_b_coverage_people'])} eligible " + f"people in the other {len(msp_households)}); only this household's two tax " + "outputs move, under either reading. The " + f"federal medical expense deduction is {money(base['medical_expense_deduction'])} " + f"in the reference ({money(base['itemized_medical_expenses'])} of expenses less " + f"7.5% of {money(agi)} AGI) and {money(no_b['medical_expense_deduction'])} " + "without the premium; the household itemizes under both. " + ) + no_b = salt_values["no_part_b"] + if is_state: + assert abs(no_b["estimate"] - no_b["liability"]) < 1e-6 + assert abs(no_b["zero"] - no_b["zero_no_local_sales"]) < 1e-6 + assert len({round(v, 6) for v in salt_values["modeled"].values()}) == 1 + note += ( + "The output also depends on the state income tax in the federal SALT " + f"deduction, which {SALT_PR} proposes to treat as an unlisted input " + f"(decision {SALT_DECISION}), through the federal itemization election, which " + "Virginia follows (va_deductions reads tax_unit_itemizes). Without the " + f"premium the output is {money(no_b['estimate'])} under both the " + "withholding estimate and the liability; with no state income tax paid it " + f"is {money(no_b['zero'])}, with or without the local sales " + "tax estimate, because the household then takes the federal and Virginia " + "standard deductions. With the premium it is " + f"{money(salt_values['modeled']['estimate'])} under all four readings. " + ) + else: + note += ( + "Under the readings of the state income tax in SALT that " + f"{SALT_PR} sets out, without the premium the output is " + f"{money(salt_values['no_part_b']['liability'])} under the liability and " + f"{money(salt_values['no_part_b']['zero'])} with no state income tax paid " + "(the household then takes the standard deduction). " + ) + note += ( + "The premium is the standard premium: the engine reads the income-related " + "adjustment from MAGI two years earlier, which no household sets, and at this " + f"household's 2026 MAGI ({money(agi + base['tax_exempt_interest_income'])}) none " + "would apply. " + ) + alt_models = within(answers, sid, variable, "no_part_b|estimate") + frozen_models = within(answers, sid, variable, "frozen") + sentence = ( + f"{answer_text(alt_models)} answered within $1 of the alternative value, and " + f"{answer_text(frozen_models)} within $1 of the frozen value." + ) + note += sentence[0].upper() + sentence[1:] + if not is_state: + liability_models = within(answers, sid, variable, "no_part_b|liability") + literal_models = within(answers, sid, variable, "no_part_b|zero") + note += ( + f" {answer_text(liability_models)} answered within $1 of the value with no " + "premium and the liability, and " + f"{answer_text(literal_models)} within $1 of the value with no premium and " + "no state income tax paid." + ) + return { + "scenario_id": sid, + "variable": variable, + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": alternative_reading, + "frozen_value": row["reference"], + "alternative_value": alternative, + "engine_version": ENGINE, + "decided_on": DRAFTED_ON, + "decided_by": "developer", + "unlisted_input": UNLISTED_INPUT, + "note": note, + "_readings": { + "salt_by_part_b": salt_values, + "itemizes": itemizes, + "not_enrolled": not_enrolled, + }, + } + + +def main() -> None: + summary = json.loads(SUMMARY.read_text()) + households = { + h["scenario_id"]: h for h in json.loads(HOUSEHOLDS.read_text())["households"] + } + if summary["scored_reference_mismatches"]: + raise SystemExit("the sweep's baseline does not reproduce the references") + frozen = json.loads((RUN / "reference_exclusions.json").read_text())["exclusions"] + frozen_keys = {(e["scenario_id"], e["variable"]) for e in frozen} + salt = salt_proposal() + salt_keys = {(e["scenario_id"], e["variable"]): e for e in salt["exclusions"]} + + moved = summary["moved"]["no_part_b"] + for reading in ("not_enrolled", "not_enrolled_direct"): + other = {(m["scenario_id"], m["variable"]) for m in summary["moved"][reading]} + if other != {(m["scenario_id"], m["variable"]) for m in moved}: + raise SystemExit(f"{reading} moves a different set of outputs") + answers = model_answers(moved, households) + answers.to_csv(ANSWERS, index=False) + + exclusions, conditional, already = [], [], [] + for row in moved: + key = (row["scenario_id"], row["variable"]) + if key in frozen_keys: + already.append(key) + continue + entry = record(row, households[row["scenario_id"]], summary, answers) + readings = entry.pop("_readings") + if key in salt_keys: + salt_record = salt_keys[key] + conditional.append( + { + "record": entry, + "readings": readings, + "install_if": ( + f"{SALT_PR}'s record for this output (decision {SALT_DECISION}) " + "is not adopted. The output then still rests on the Part B " + "premium and needs this record." + ), + "if_salt_record_adopted": ( + f"{SALT_PR}'s record already excludes the output, and its note " + "already gives the values without the premium (it quotes " + f"{money(readings['salt_by_part_b']['no_part_b']['estimate'])}, " + f"{money(readings['salt_by_part_b']['no_part_b']['liability'])} " + f"and {money(readings['salt_by_part_b']['no_part_b']['zero'])}). " + "A release adds the unlisted input to that record's " + "unlisted_input text: '; also " + UNLISTED_INPUT + "'." + ), + "salt_record_frozen_value": salt_record["frozen_value"], + "salt_record_alternative_value": salt_record["alternative_value"], + } + ) + else: + entry["_readings"] = readings + exclusions.append(entry) + if already: + raise SystemExit(f"already-excluded outputs moved; add note text: {already}") + + readings = {} + for entry in exclusions: + readings[f"{entry['scenario_id']}/{entry['variable']}"] = entry.pop("_readings") + payload = { + "schema_version": 1, + "status": ( + f"proposed {DRAFTED_ON}; changes published scores, so it waits for Max's " + "ruling (cos decision). decided_on is the draft date; a release sets it to " + "the ruling's date." + ), + "basis": ( + "policybench/reference_exclusions.py: an output is excluded for every model " + "when its reference depends on an engine input the prompt never listed and " + "a careful reader could take the stated facts the other way." + ), + "salt_proposal": { + "pr": SALT_PR, + "head": SALT_HEAD, + "decision": SALT_DECISION, + "outputs": [f"{s}/{v}" for s, v in salt_keys], + }, + "exclusions": exclusions, + "conditional_on_salt_decision": conditional, + "already_excluded": [], + "readings": readings, + "readings_moving_no_further_output": [ + "not_enrolled", + "not_enrolled_direct", + "irmaa_from_2026_income", + ], + } + OUT.write_text(json.dumps(payload, indent=1) + "\n") + print(json.dumps(payload, indent=1)) + print(answers.to_string(index=False)) + + +if __name__ == "__main__": + main() diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py b/reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py new file mode 100644 index 00000000..484f09a3 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py @@ -0,0 +1,624 @@ +"""Recompute every PolicyBench reference without the engine's assumed Medicare enrollment. + +policyengine-us 2.15.17 counts a Medicare Part B premium in medical expenses for every +person it treats as enrolled in Medicare. ``medical_expense_health_insurance_premiums`` +uses ``health_insurance_premiums`` when that input is set; otherwise it adds +``health_insurance_premiums_without_medicare_part_b`` and ``medicare_part_b_premium * +medicare_enrolled``. ``medicare_enrolled`` adds ``takes_up_medicare_if_eligible``, whose +default is True, for everyone ``is_medicare_eligible`` (age 65, or 24 months of Social +Security disability benefits). ``medicare_part_b_premium`` is the gross premium (the +$202.90 monthly base for 2026 plus an income-related amount on MAGI from two years +earlier) less Medicare Savings Program coverage. No PolicyBench household sets any of +these inputs, and no prompt states Medicare enrollment or a Part B premium. + +The reference system is the one that built the published references: policyengine-us +2.15.17 plus ``latest_final`` (reference_audit/2026-09-28/fixes), on households built by +``Scenario.to_pe_household``. The script first recomputes every output and impact weight +and checks them against the published reference CSV. It then reruns every household +under each reading: + +``no_part_b`` + Each person's ``medicare_part_b_premium`` is 0: no Part B premium is paid, the + prompt's "treat any unlisted numeric input as 0". Enrollment itself is unchanged. +``not_enrolled`` + Each person's ``takes_up_medicare_if_eligible`` is False, so nobody is enrolled: + the prompt's "do not infer ... health coverage". This removes every channel that + reads enrollment, not only the Part B premium. +``not_enrolled_direct`` + Each person's ``medicare_enrolled`` is False. A differential check on + ``not_enrolled`` through the other input; the two must agree on every output. +``irmaa_from_2026_income`` + The income-related Part B amount read on the household's 2026 MAGI (AGI plus + tax-exempt interest) instead of the unset 2024 MAGI, which the engine computes as 0. + A sensitivity check on the premium's size, not a reading the prompt supports. + +For every household with a Medicare-eligible person it also records each person's +Medicare variables and a propagation trace: every engine variable, at every 2026 +period the simulation computed, whose value differs between the reference and the +``no_part_b`` or ``not_enrolled`` simulation. The trace lists the consumers that fire, +which the static list of formulas that read the premium cannot show. + +Households with an output that moves are also run on a grid of the Part B readings and +the four readings of the state income tax in the federal SALT deduction that +reference_audit/2026-10-05 (PolicyEngine/policybench#191) proposes: the engine's +withholding estimate, the state liability (iterated to a fixed point), none paid, and +none paid without the engine's local sales tax estimate. + +Run from a policybench checkout with the policyengine-us 2.15.17 venv: + + PYTHONDONTWRITEBYTECODE=1 OPENBLAS_NUM_THREADS=1 PYTHONPATH= \\ + /.venv-pe21517/bin/python \\ + reference_audit/2026-10-05-medicare-part-b/scripts/sweep_part_b.py \\ + --out-dir reference_audit/2026-10-05-medicare-part-b/verification +""" + +from __future__ import annotations + +import argparse +import copy +import importlib.util +import json +import shutil +import sys +import tempfile +from concurrent.futures import ProcessPoolExecutor +from importlib.metadata import version +from pathlib import Path + +import numpy as np +import pandas as pd + +ROOT = Path(__file__).resolve().parents[3] +RUN = ( + ROOT + / "paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace" +) +AUDIT_0928 = ROOT / "reference_audit/2026-09-28" +AUDIT_0922 = ROOT / "reference_audit/2026-09-22" +YEAR = 2026 +PERIOD = str(YEAR) +BINARY_SUFFIXES = ("_eligible",) +TOLERANCE = 1.0 +TRACE_TOLERANCE = 1e-6 +# sweep.py's rename for the 1.755.4 harness; sweep_latest.py kept it on 2.15.17. +RENAME = {"partnership_se_income": "partnership_self_employment_net_earnings"} +MAX_ITERATIONS = 25 +CONVERGED = 0.005 +FEDERAL = "federal_income_tax_before_refundable_credits" +STATE = "state_income_tax_before_refundable_credits" + +READINGS = { + "no_part_b": {"people": {"medicare_part_b_premium": 0.0}}, + "not_enrolled": {"people": {"takes_up_medicare_if_eligible": False}}, + "not_enrolled_direct": {"people": {"medicare_enrolled": False}}, + "irmaa_from_2026_income": {}, # tax-unit input set per household below +} +TRACED_READINGS = ("no_part_b", "not_enrolled") +PART_B_GRID = ("modeled", "no_part_b", "not_enrolled") +SALT_GRID = ("estimate", "liability", "zero", "zero_no_local_sales") + +PERSON_DIAGNOSTICS = ( + "age", + "is_medicare_eligible", + "takes_up_medicare_if_eligible", + "medicare_enrolled", + "base_part_b_premium", + "gross_medicare_part_b_premium", + "msp_part_b_premium_coverage", + "medicare_part_b_premium", + "health_insurance_premiums", + "health_insurance_premiums_without_medicare_part_b", + "medical_expense_health_insurance_premiums", + "other_medical_expenses", + "base_part_a_premium", + "medicare_part_a_premium", + "income_adjusted_part_d_premium_surcharge", + "medicare_cost", + "is_usda_elderly", + "is_usda_disabled", + "social_security_disability", + "months_receiving_social_security_disability", +) +TAX_UNIT_DIAGNOSTICS = ( + "medicare_irmaa_magi_two_years_prior", + "adjusted_gross_income", + "tax_exempt_interest_income", + "itemized_medical_expenses", + "medical_expense_deduction", + "tax_unit_itemizes", + "standard_deduction", + "itemized_taxable_income_deductions", + "salt_deduction", + "state_withheld_income_tax", + "local_sales_tax", +) +SPM_UNIT_DIAGNOSTICS = ( + "snap", + "snap_excess_medical_expense_deduction", + "snap_net_income", + "snap_gross_income", +) + +_SYSTEM = None + + +def _assemble_fixes() -> Path: + """latest_final and its parts, plus the sales tax table the IRS module reads.""" + target = Path(tempfile.mkdtemp(prefix="part_b_fixes_")) + for path in (AUDIT_0928 / "fixes").glob("*.py"): + shutil.copy2(path, target / path.name) + shutil.copy2(AUDIT_0922 / "fixes/r19_irs_sales_tax_2025.json", target) + return target + + +def _system(): + global _SYSTEM + if _SYSTEM is None: + from policyengine_us import CountryTaxBenefitSystem + + path = _assemble_fixes() / "latest_final.py" + spec = importlib.util.spec_from_file_location("latest_final", path) + module = importlib.util.module_from_spec(spec) + sys.modules["latest_final"] = module + spec.loader.exec_module(module) + _SYSTEM = CountryTaxBenefitSystem(reform=module.reform) + return _SYSTEM + + +def build_situation(scenario) -> dict: + situation = scenario.to_pe_household() + for person in situation["people"].values(): + for old, new in RENAME.items(): + if old in person: + person[new] = person.pop(old) + return situation + + +def _simulate(situation, *, people=None, tax_unit=None): + from policyengine_us import Simulation + + situation = copy.deepcopy(situation) + for person in situation["people"].values(): + for name, value in (people or {}).items(): + person[name] = {PERIOD: value} + tu = situation["tax_units"]["tax_unit"] + for name, value in (tax_unit or {}).items(): + tu[name] = {PERIOD: value} + return Simulation(tax_benefit_system=_system(), situation=situation) + + +def _total(sim, variable: str) -> float: + return float(np.asarray(sim.calculate(variable, YEAR)).sum()) + + +def _outputs(sim, scenario, variables) -> tuple[dict, dict]: + """Every output and, where the output has one, its impact weight.""" + from policybench.ground_truth import ( + _extract_person_impact_weight, + _extract_person_value, + _impact_weight_variable_for_output, + _pe_variable_for_output, + ) + + values, weights = {}, {} + for variable in variables: + result = sim.calculate(_pe_variable_for_output(variable, "us"), YEAR) + values[variable] = float(_extract_person_value(result, scenario, variable)) + weight_variable = _impact_weight_variable_for_output(variable, "us") + if weight_variable is not None: + weights[variable] = float( + _extract_person_impact_weight( + result, sim.calculate(weight_variable, YEAR), scenario, variable + ) + ) + return values, weights + + +def _person_diagnostics(sim, names) -> dict: + out = {} + for variable in PERSON_DIAGNOSTICS: + values = np.asarray(sim.calculate(variable, YEAR)) + out[variable] = { + name: ( + bool(v) + if values.dtype == bool + else (float(v) if np.issubdtype(values.dtype, np.number) else str(v)) + ) + for name, v in zip(names, values) + } + return out + + +def _unit_diagnostics(sim) -> dict: + out = {variable: _total(sim, variable) for variable in TAX_UNIT_DIAGNOSTICS} + for variable in SPM_UNIT_DIAGNOSTICS: + # Monthly SNAP variables sum to the year when asked for a year. + out[variable] = _total(sim, variable) + return out + + +def _cached(sim) -> dict: + """Every value the simulation computed or was given for a 2026 period.""" + values = {} + for name in sim.tax_benefit_system.variables: + holder = sim.get_holder(name) + for period in holder.get_known_periods(): + if period.start.year != YEAR: + continue + array = holder.get_array(period) + if array is not None: + values[(name, str(period))] = np.asarray(array) + return values + + +def _trace(base: dict, other: dict) -> list[dict]: + """Variables whose 2026 values differ between two simulations.""" + rows = {} + for key in sorted(set(base) | set(other)): + name, period = key + if key not in base or key not in other: + row = rows.setdefault(name, {"variable": name, "max_abs_diff": 0.0}) + row.setdefault("computed_in_one_only", []).append(period) + continue + a, b = base[key], other[key] + if a.shape != b.shape: + continue + if np.issubdtype(a.dtype, np.number) and np.issubdtype(b.dtype, np.number): + diff = np.abs(a.astype(float) - b.astype(float)) + if diff.size and diff.max() > TRACE_TOLERANCE: + row = rows.setdefault(name, {"variable": name, "max_abs_diff": 0.0}) + row["max_abs_diff"] = max(row["max_abs_diff"], float(diff.max())) + row.setdefault("periods", []).append(period) + elif not np.array_equal(a.astype(str), b.astype(str)): + row = rows.setdefault(name, {"variable": name, "max_abs_diff": 0.0}) + row["categorical"] = True + row.setdefault("periods", []).append(period) + return [rows[name] for name in sorted(rows)] + + +def _salt_liability(sim) -> float: + """The SALT audit's liability reading: state plus Maryland county, less NYC.""" + state = _total(sim, STATE) + md_county = _total(sim, "md_local_income_tax_before_refundable_credits") + nyc = _total(sim, "nyc_income_tax_before_refundable_credits") + return max(0.0, state + md_county - nyc) + + +def _grid_cell(situation, scenario, variables, part_b: str, salt: str) -> dict: + people = { + "modeled": {}, + "no_part_b": READINGS["no_part_b"]["people"], + "not_enrolled": READINGS["not_enrolled"]["people"], + }[part_b] + tax_unit = {} + trace = [] + if salt in ("zero", "zero_no_local_sales"): + tax_unit["state_withheld_income_tax"] = 0.0 + if salt == "zero_no_local_sales": + tax_unit["local_sales_tax"] = 0.0 + sim = _simulate(situation, people=people, tax_unit=tax_unit) + if salt == "liability": + withheld = _salt_liability(sim) + for _ in range(MAX_ITERATIONS): + tax_unit["state_withheld_income_tax"] = withheld + sim = _simulate(situation, people=people, tax_unit=tax_unit) + implied = _salt_liability(sim) + trace.append({"withheld": withheld, "implied": implied}) + if abs(implied - withheld) < CONVERGED: + break + withheld = implied + else: + raise RuntimeError(f"{scenario.id} {part_b}: liability did not converge") + values, _ = _outputs(sim, scenario, variables) + return { + "outputs": values, + "itemizes": _total(sim, "tax_unit_itemizes"), + "salt_deduction": _total(sim, "salt_deduction"), + "medical_expense_deduction": _total(sim, "medical_expense_deduction"), + "state_withheld_income_tax": _total(sim, "state_withheld_income_tax"), + "fixed_point_trace": trace, + } + + +def sweep_scenario(job) -> dict: + from policybench.scenarios import scenario_from_dict + + scenario_json, variables = job + scenario = scenario_from_dict(json.loads(scenario_json)) + situation = build_situation(scenario) + names = list(situation["people"]) + + base = _simulate(situation) + base_values, base_weights = _outputs(base, scenario, variables) + eligible = bool(np.asarray(base.calculate("is_medicare_eligible", YEAR)).any()) + result = { + "scenario_id": scenario.id, + "state": scenario.state, + "people": names, + "medicare_eligible": eligible, + "baseline": base_values, + "baseline_weights": base_weights, + "baseline_person": _person_diagnostics(base, names), + "baseline_units": _unit_diagnostics(base), + "readings": {}, + } + base_cache = _cached(base) if eligible else None + magi_2026 = _total(base, "adjusted_gross_income") + _total( + base, "tax_exempt_interest_income" + ) + for reading, spec in READINGS.items(): + tax_unit = ( + {"medicare_irmaa_magi_two_years_prior": magi_2026} + if reading == "irmaa_from_2026_income" + else {} + ) + sim = _simulate(situation, people=spec.get("people"), tax_unit=tax_unit) + values, weights = _outputs(sim, scenario, variables) + entry = {"outputs": values, "weights": weights} + if eligible: + entry["person"] = _person_diagnostics(sim, names) + entry["units"] = _unit_diagnostics(sim) + if reading in TRACED_READINGS: + entry["trace"] = _trace(base_cache, _cached(sim)) + result["readings"][reading] = entry + + moved = any( + _moved(variable, base_values[variable], r["outputs"][variable]) + for r in result["readings"].values() + for variable in variables + ) + if moved: + result["grid"] = { + f"{part_b}|{salt}": _grid_cell(situation, scenario, variables, part_b, salt) + for part_b in PART_B_GRID + for salt in SALT_GRID + } + return result + + +def _moved(variable: str, base: float, value: float) -> bool: + if variable.endswith(BINARY_SUFFIXES): + return round(value) != round(base) + return abs(value - base) > TOLERANCE + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out-dir", required=True) + parser.add_argument("--scenarios", nargs="*") + parser.add_argument("--workers", type=int, default=8) + args = parser.parse_args() + + from policybench.scenarios import scenario_from_dict + from policybench.spec import expand_programs_for_scenario + + out_dir = Path(args.out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + meta = json.loads((RUN / "reference_outputs.csv.meta.json").read_text()) + reference = pd.read_csv(RUN / "reference_outputs.csv") + indexed = reference.set_index(["scenario_id", "variable"]) + exclusions = json.loads((RUN / "reference_exclusions.json").read_text())[ + "exclusions" + ] + excluded = {(e["scenario_id"], e["variable"]): e for e in exclusions} + scenarios = pd.read_csv(RUN / "scenarios.csv") + if args.scenarios: + scenarios = scenarios[scenarios["scenario_id"].isin(args.scenarios)] + jobs = [] + for _, row in scenarios.iterrows(): + scenario = scenario_from_dict(json.loads(row["scenario_json"])) + variables = [ + v + for v in expand_programs_for_scenario(meta["programs"], scenario) + if (scenario.id, v) in indexed.index + ] + jobs.append((row["scenario_json"], variables)) + + engine = version("policyengine-us") + print(f"policyengine-us {engine}; {len(jobs)} households", flush=True) + with ProcessPoolExecutor(max_workers=args.workers) as pool: + results = sorted(pool.map(sweep_scenario, jobs), key=lambda r: r["scenario_id"]) + + rows = [] + for result in results: + sid = result["scenario_id"] + for variable, base in result["baseline"].items(): + ref = float(indexed.loc[(sid, variable), "value"]) + ref_weight = indexed.loc[(sid, variable), "impact_weight"] + entry = excluded.get((sid, variable)) + binary = variable.endswith(BINARY_SUFFIXES) + row = { + "scenario_id": sid, + "state": result["state"], + "variable": variable, + "medicare_household": result["medicare_eligible"], + "reference": ref, + "baseline": base, + "baseline_matches_reference": ( + round(base) == round(ref) if binary else abs(base - ref) <= 1e-3 + ), + "reference_impact_weight": ( + float(ref_weight) if pd.notna(ref_weight) else None + ), + "baseline_impact_weight": result["baseline_weights"].get(variable), + "excluded": entry is not None, + "excluded_reason": entry["reason_code"] if entry else "", + } + for reading in READINGS: + r = result["readings"][reading] + value = r["outputs"][variable] + row[reading] = value + row[f"{reading}_delta"] = value - base + row[f"{reading}_moved"] = _moved(variable, base, value) + row[f"{reading}_impact_weight"] = r["weights"].get(variable) + rows.append(row) + table = pd.DataFrame(rows) + table.to_csv(out_dir / "sweep_part_b.csv", index=False) + + weight_mismatch = table[ + table["reference_impact_weight"].notna() + & ( + (table["reference_impact_weight"] - table["baseline_impact_weight"]).abs() + > 1e-3 + ) + ] + mismatched = table[~table["baseline_matches_reference"]] + scored_mismatched = mismatched[~mismatched["excluded"]] + medicare_households = [r["scenario_id"] for r in results if r["medicare_eligible"]] + part_b_households = [ + r["scenario_id"] + for r in results + if sum(r["baseline_person"]["medicare_part_b_premium"].values()) > 0 + ] + msp_people = [ + (r["scenario_id"], name) + for r in results + for name, v in r["baseline_person"]["msp_part_b_premium_coverage"].items() + if v > 0 + ] + eligible_people = [ + (r["scenario_id"], name) + for r in results + for name, v in r["baseline_person"]["is_medicare_eligible"].items() + if v + ] + enrollment_matches_eligibility = all( + r["baseline_person"]["medicare_enrolled"][name] + == r["baseline_person"]["is_medicare_eligible"][name] + for r in results + for name in r["people"] + ) + snap_or_eligibility = table[ + table["medicare_household"] + & ( + (table["variable"] == "snap") + | table["variable"].str.endswith(BINARY_SUFFIXES) + ) + ] + differential = table[ + (table["not_enrolled"] - table["not_enrolled_direct"]).abs() > 1e-6 + ] + + def moved_rows(reading): + moved = table[table[f"{reading}_moved"]] + return [ + { + "scenario_id": r.scenario_id, + "state": r.state, + "variable": r.variable, + "reference": r.baseline, + "value": getattr(r, reading), + "delta": getattr(r, f"{reading}_delta"), + "excluded": bool(r.excluded), + } + for r in moved.itertuples() + ] + + def weight_moves(reading): + col = f"{reading}_impact_weight" + sub = table[table["baseline_impact_weight"].notna()] + sub = sub[(sub[col] - sub["baseline_impact_weight"]).abs() > 1e-3] + return [ + { + "scenario_id": r.scenario_id, + "variable": r.variable, + "baseline_impact_weight": r.baseline_impact_weight, + "impact_weight": getattr(r, col), + "excluded": bool(r.excluded), + } + for r in sub.itertuples() + ] + + summary = { + "policyengine_us": engine, + "households": len(results), + "outputs": len(table), + "scored_outputs": int((~table["excluded"]).sum()), + "scored_reference_mismatches": scored_mismatched[ + ["scenario_id", "variable", "reference", "baseline"] + ].to_dict("records"), + "excluded_outputs_differing_from_frozen_value": int( + mismatched["excluded"].sum() + ), + "impact_weight_mismatches": weight_mismatch[ + [ + "scenario_id", + "variable", + "reference_impact_weight", + "baseline_impact_weight", + ] + ].to_dict("records"), + "medicare_eligible_people": len(eligible_people), + "medicare_households": medicare_households, + "part_b_households": part_b_households, + "msp_part_b_coverage_people": msp_people, + "enrollment_equals_eligibility_in_reference": enrollment_matches_eligibility, + "not_enrolled_vs_direct_differences": differential[ + ["scenario_id", "variable", "not_enrolled", "not_enrolled_direct"] + ].to_dict("records"), + "moved": {reading: moved_rows(reading) for reading in READINGS}, + "impact_weight_moves": {reading: weight_moves(reading) for reading in READINGS}, + "snap_and_eligibility_outputs_in_medicare_households": snap_or_eligibility[ + ["scenario_id", "state", "variable", "excluded", "baseline"] + + [reading for reading in READINGS] + ].to_dict("records"), + } + (out_dir / "sweep_part_b_summary.json").write_text( + json.dumps(summary, indent=1) + "\n" + ) + (out_dir / "sweep_part_b_households.json").write_text( + json.dumps({"policyengine_us": engine, "households": results}, indent=1) + "\n" + ) + + print( + f"baseline: {len(table)} outputs, {summary['scored_outputs']} scored; " + f"{len(scored_mismatched)} scored outputs differ from the published reference; " + f"{len(weight_mismatch)} impact weights differ" + ) + print( + f"Medicare-eligible: {len(eligible_people)} people in " + f"{len(medicare_households)} households; Part B premium in " + f"{len(part_b_households)} households; MSP Part B coverage for " + f"{len(msp_people)} people; enrollment equals eligibility: " + f"{enrollment_matches_eligibility}" + ) + print(f"not_enrolled vs not_enrolled_direct differences: {len(differential)}") + with pd.option_context("display.width", 220, "display.max_rows", 500): + for reading in READINGS: + moved = summary["moved"][reading] + scored = sum(not m["excluded"] for m in moved) + print(f"\n{reading}: {len(moved)} outputs move ({scored} scored)") + for m in moved: + print( + f" {m['scenario_id']} {m['state']} {m['variable']}: " + f"{m['reference']:,.2f} -> {m['value']:,.2f} " + f"({m['delta']:+,.2f}){' [excluded]' if m['excluded'] else ''}" + ) + weights = summary["impact_weight_moves"][reading] + print(f" impact weights moved: {len(weights)}") + print("\nSNAP and eligibility outputs in Medicare households (any change):") + changed = snap_or_eligibility[ + np.column_stack( + [ + ( + snap_or_eligibility[reading] - snap_or_eligibility["baseline"] + ).abs() + > 1e-6 + for reading in READINGS + ] + ).any(axis=1) + ] + print(changed.to_string(index=False) if len(changed) else " none") + for result in results: + if "grid" in result: + print(f"\n{result['scenario_id']} grid (federal, state, itemizes):") + for key, cell in result["grid"].items(): + o = cell["outputs"] + print( + f" {key:32s} {o.get(FEDERAL, float('nan')):>12,.2f} " + f"{o.get(STATE, float('nan')):>10,.2f} {cell['itemizes']:.0f}" + ) + + +if __name__ == "__main__": + main() diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json new file mode 100644 index 00000000..cfff4bde --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json @@ -0,0 +1,358 @@ +{ + "published_reproduced": true, + "cases": [ + { + "case": "part_b", + "exclusions_added": [ + "scenario_114/state_income_tax_before_refundable_credits", + "scenario_114/federal_income_tax_before_refundable_credits" + ], + "scored_outputs": { + "published": 1928, + "case": 1926 + }, + "exact_delta_pp": { + "min": 0.18083569721511594, + "max": 0.26272047738837045, + "mean": 0.25396770360331294 + }, + "within1pct_delta_pp": { + "min": 0.18083569721511594, + "max": 0.26272047738837045 + }, + "score_delta": { + "min": 0.009521744180730707, + "max": 0.26140701322196946 + }, + "exact_rank_changes": {}, + "within1pct_rank_changes": { + "claude-opus-4.8": { + "published": 40, + "case": 39 + }, + "minimax-m3": { + "published": 39, + "case": 40 + } + }, + "score_rank_changes": {}, + "always_zero_baseline": { + "published": { + "exact": 69.97959457368849, + "within1pct": 69.97959457368849 + }, + "case": { + "exact": 70.14407633139312, + "within1pct": 70.14407633139312 + } + }, + "leader": "gpt-6-sol", + "leader_exact": { + "published": 95.00349913571871, + "case": 95.26621961310708 + }, + "leader_margin_over_always_zero_exact": { + "published": 25.023904562030225, + "case": 25.12214328171396 + }, + "programs": [ + { + "variable": "federal_income_tax_before_refundable_credits", + "exact_published": 61.611877, + "exact_case": 62.372517, + "exact_delta": 0.76064, + "score_published": 82.701144, + "score_case": 82.823625, + "score_delta": 0.122481, + "n_published": 3772, + "n_case": 3726, + "n_delta": -46 + }, + { + "variable": "state_income_tax_before_refundable_credits", + "exact_published": 64.307381, + "exact_case": 65.063939, + "exact_delta": 0.756557, + "score_published": 84.205086, + "score_case": 84.358237, + "score_delta": 0.15315, + "n_published": 3956, + "n_case": 3910, + "n_delta": -46 + } + ] + }, + { + "case": "salt", + "exclusions_added": [ + "scenario_022/federal_income_tax_before_refundable_credits", + "scenario_081/federal_income_tax_before_refundable_credits", + "scenario_114/federal_income_tax_before_refundable_credits" + ], + "scored_outputs": { + "published": 1928, + "case": 1925 + }, + "exact_delta_pp": { + "min": 0.41983119224074983, + "max": 0.6110860229821071, + "mean": 0.5239506378619773 + }, + "within1pct_delta_pp": { + "min": 0.29760904363327256, + "max": 0.6056314194315462 + }, + "score_delta": { + "min": -0.0017724385748465465, + "max": 0.555965927062843 + }, + "exact_rank_changes": { + "gpt-6-luna": { + "published": 4, + "case": 5 + }, + "claude-sonnet-5.5": { + "published": 5, + "case": 4 + } + }, + "within1pct_rank_changes": { + "gpt-6.1-sol": { + "published": 9, + "case": 10 + }, + "grok-4.7": { + "published": 10, + "case": 9 + }, + "gemini-3.5-flash-lite": { + "published": 27, + "case": 26 + }, + "glm-5.3": { + "published": 26, + "case": 27 + }, + "claude-opus-4.8": { + "published": 40, + "case": 39 + }, + "minimax-m3": { + "published": 39, + "case": 40 + } + }, + "score_rank_changes": { + "gpt-5.5": { + "published": 14, + "case": 13 + }, + "gpt-5.6-terra": { + "published": 13, + "case": 14 + }, + "claude-opus-5": { + "published": 24, + "case": 23 + }, + "deepseek-v4-flash-0731": { + "published": 23, + "case": 24 + }, + "minimax-m3": { + "published": 43, + "case": 42 + }, + "claude-sonnet-5": { + "published": 42, + "case": 43 + } + }, + "always_zero_baseline": { + "published": { + "exact": 69.97959457368849, + "within1pct": 69.97959457368849 + }, + "case": { + "exact": 70.39464856524052, + "within1pct": 70.39464856524052 + } + }, + "leader": "gpt-6-sol", + "leader_exact": { + "published": 95.00349913571871, + "case": 95.61458515870082 + }, + "leader_margin_over_always_zero_exact": { + "published": 25.023904562030225, + "case": 25.219936593460304 + }, + "programs": [ + { + "variable": "federal_income_tax_before_refundable_credits", + "exact_published": 61.611877, + "exact_case": 63.951569, + "exact_delta": 2.339692, + "score_published": 82.701144, + "score_case": 82.911359, + "score_delta": 0.210214, + "n_published": 3772, + "n_case": 3634, + "n_delta": -138 + } + ] + }, + { + "case": "salt_and_part_b", + "exclusions_added": [ + "scenario_022/federal_income_tax_before_refundable_credits", + "scenario_081/federal_income_tax_before_refundable_credits", + "scenario_114/federal_income_tax_before_refundable_credits", + "scenario_114/state_income_tax_before_refundable_credits" + ], + "scored_outputs": { + "published": 1928, + "case": 1924 + }, + "exact_delta_pp": { + "min": 0.47379612177722663, + "max": 0.6894869817993765, + "mean": 0.5997395967929741 + }, + "within1pct_delta_pp": { + "min": 0.31874381430527876, + "max": 0.6817122433458565 + }, + "score_delta": { + "min": 0.046595059158718755, + "max": 0.6339749223337492 + }, + "exact_rank_changes": { + "gpt-6-luna": { + "published": 4, + "case": 5 + }, + "claude-sonnet-5.5": { + "published": 5, + "case": 4 + } + }, + "within1pct_rank_changes": { + "gpt-6.1-sol": { + "published": 9, + "case": 10 + }, + "grok-4.7": { + "published": 10, + "case": 9 + }, + "gemini-3.5-flash-lite": { + "published": 27, + "case": 26 + }, + "glm-5.3": { + "published": 26, + "case": 27 + }, + "claude-opus-4.8": { + "published": 40, + "case": 39 + }, + "minimax-m3": { + "published": 39, + "case": 40 + } + }, + "score_rank_changes": { + "gpt-5.5": { + "published": 14, + "case": 13 + }, + "gpt-5.6-terra": { + "published": 13, + "case": 14 + }, + "claude-opus-5": { + "published": 24, + "case": 23 + }, + "deepseek-v4-flash-0731": { + "published": 23, + "case": 24 + }, + "grok-4.3": { + "published": 36, + "case": 35 + }, + "gemini-3.1-flash-lite-preview": { + "published": 35, + "case": 36 + }, + "minimax-m3": { + "published": 43, + "case": 42 + }, + "claude-sonnet-5": { + "published": 42, 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"n_published": 3956, + "n_case": 3910, + "n_delta": -46 + } + ] + } + ], + "virginia_record_on_top_of_salt": { + "exact_delta_pp": { + "min": 0.0539649295364768, + "max": 0.07840095881726938 + }, + "exact_rank_changes": {} + }, + "impact_weights_check": { + "impact_weights_replaced": 90, + "payload_differences": [] + } +} diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact_marginal.csv b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact_marginal.csv new file mode 100644 index 00000000..71c34c0a --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact_marginal.csv @@ -0,0 +1,47 @@ +model,exact_salt,exact_salt_and_part_b,exact_delta,rank_salt,rank_salt_and_part_b,score_delta +gpt-6-sol,95.61458515870082,95.69298611751809,0.07840095881726938,1,1,0.01050772668648392 +claude-opus-5.5,94.26125665356861,94.33965761238588,0.07840095881726938,2,2,0.000900323528568947 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a/reference_audit/2026-10-05-medicare-part-b/verification/part_b_households.csv b/reference_audit/2026-10-05-medicare-part-b/verification/part_b_households.csv new file mode 100644 index 00000000..451f1751 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/part_b_households.csv @@ -0,0 +1,34 @@ +scenario_id,state,medicare_eligible_people,part_b_premium,msp_part_b_coverage,federal_itemizes,federal_itemizes_without_part_b,medical_expense_deduction,medical_expense_deduction_without_part_b,federal_output,state_output,snap,snap_without_part_b,state_variables_moving,outputs_moved +scenario_000,TX,1,2434.800048828125,0.0,False,False,0.0,0.0,2906.45361328125,0.0,0.0,0.0,, +scenario_001,VA,2,0.0,4869.60009765625,False,False,0.0,0.0,0.0,0.0,0.0,0.0,, +scenario_002,WA,2,4869.60009765625,0.0,False,False,3134.85009765625,0.0,0.0,0.0,0.0,0.0,, +scenario_013,AZ,1,2434.800048828125,0.0,False,False,2222.199951171875,0.0,0.0,0.0,240.0,240.0,az_itemized_deductions, +scenario_021,MO,1,2434.800048828125,0.0,False,False,2331.699951171875,0.0,0.0,0.0,0.0,0.0,mo_itemized_deductions, +scenario_022,CA,1,2434.800048828125,0.0,True,True,0.0,0.0,11113.5703125,2505.8701171875,0.0,0.0,, +scenario_027,CT,1,2434.800048828125,0.0,False,False,2914.175048828125,479.375,0.0,0.0,288.0,288.0,, +scenario_031,CA,1,2434.800048828125,0.0,False,False,1441.574951171875,0.0,0.0,0.0,0.0,0.0,ca_itemized_deductions;ca_itemized_deductions_pre_limitation, +scenario_033,SD,2,4869.60009765625,0.0,False,False,0.0,0.0,3818.148193359375,0.0,0.0,0.0,, +scenario_040,AZ,2,4869.60009765625,0.0,False,False,2447.10009765625,0.0,0.0,0.0,0.0,0.0,az_itemized_deductions, +scenario_042,WI,1,2434.800048828125,0.0,False,False,0.0,0.0,2361.96484375,464.18280029296875,0.0,0.0,, +scenario_043,CO,1,0.0,2434.800048828125,False,False,4153.0947265625,4153.0947265625,0.0,0.0,3576.0,3576.0,, +scenario_044,KS,1,2434.800048828125,0.0,False,False,10134.7998046875,7700.0,0.0,0.0,0.0,0.0,ks_itemized_deductions, +scenario_048,OH,1,2434.800048828125,0.0,False,False,2160.60009765625,0.0,0.0,0.0,0.0,0.0,oh_agi;oh_agi_person;oh_deductions;oh_insured_unreimbursed_medical_care_expense_amount;oh_insured_unreimbursed_medical_care_expenses;oh_joint_filing_credit_qualifying_income;oh_modified_agi;oh_taxable_income;oh_unreimbursed_medical_care_expense_deduction_person, +scenario_055,FL,1,2434.800048828125,0.0,False,False,0.0,0.0,25238.578125,0.0,0.0,0.0,, +scenario_059,FL,2,4869.60009765625,0.0,False,False,3089.91015625,0.0,0.0,0.0,0.0,0.0,, +scenario_060,TX,1,2434.800048828125,0.0,False,False,14204.1748046875,11769.375,0.0,0.0,0.0,0.0,, +scenario_062,FL,1,2434.800048828125,0.0,False,False,5135.5810546875,2700.781494140625,0.0,0.0,0.0,0.0,, +scenario_071,NY,1,2434.800048828125,0.0,False,False,2133.949951171875,0.0,0.0,0.0,0.0,0.0,ny_itemized_deductions;ny_itemized_deductions_max, +scenario_072,MI,2,4869.60009765625,0.0,True,False,37152.046875,32282.4453125,0.0,0.0,0.0,0.0,, +scenario_085,PA,1,2434.800048828125,0.0,False,False,2716.82470703125,282.02490234375,2322.760009765625,0.0,0.0,0.0,pa_ccw_medical_expenses, +scenario_092,AL,1,2434.800048828125,0.0,False,False,1311.300048828125,0.0,0.0,0.0,0.0,0.0,al_deductions;al_itemized_deductions;al_medical_expense_deduction, +scenario_095,NJ,1,2434.800048828125,0.0,False,False,1688.2835693359375,0.0,0.0,0.0,0.0,0.0,nj_main_income_tax;nj_medical_expense_deduction;nj_taxable_income;nj_taxable_income_before_property_tax_deduction;nj_total_deductions, +scenario_098,IL,1,2434.800048828125,0.0,False,False,2537.800048828125,103.0,0.0,0.0,0.0,0.0,, +scenario_104,NY,1,2434.800048828125,0.0,False,False,2734.800048828125,300.0,0.0,0.0,0.0,0.0,ny_itemized_deductions;ny_itemized_deductions_max, +scenario_107,OH,1,2434.800048828125,0.0,False,False,1276.8187255859375,0.0,0.0,0.0,0.0,0.0,oh_agi;oh_agi_person;oh_deductions;oh_insured_unreimbursed_medical_care_expense_amount;oh_insured_unreimbursed_medical_care_expenses;oh_joint_filing_credit_qualifying_income;oh_modified_agi;oh_taxable_income;oh_unreimbursed_medical_care_expense_deduction_person, +scenario_108,WI,1,2434.800048828125,0.0,False,False,2360.60009765625,0.0,0.0,0.0,288.0,288.0,, +scenario_111,WA,1,2434.800048828125,0.0,False,False,1566.800048828125,0.0,0.0,0.0,0.0,0.0,, +scenario_114,VA,1,2434.800048828125,0.0,True,True,4441.857421875,2007.0576171875,10729.6123046875,3514.14501953125,0.0,0.0,va_deductions;va_income_tax_before_non_refundable_credits;va_income_tax_before_refundable_credits;va_income_tax_if_claiming_non_refundable_eitc;va_income_tax_if_claiming_refundable_eitc;va_itemized_deductions;va_reduced_itemized_deductions;va_taxable_income,federal_income_tax_before_refundable_credits;state_income_tax_before_refundable_credits +scenario_115,AL,1,2434.800048828125,0.0,False,False,2584.800048828125,150.0,0.0,4.679999828338623,0.0,0.0,al_itemized_deductions;al_medical_expense_deduction, +scenario_118,NY,1,0.0,2434.800048828125,False,False,29.0,29.0,0.0,0.0,2868.0,2868.0,, +scenario_120,CT,1,2434.800048828125,0.0,True,True,0.0,0.0,40416.66796875,11917.18359375,0.0,0.0,, +scenario_122,MN,1,2434.800048828125,0.0,False,False,0.0,0.0,4746.66162109375,1025.54150390625,0.0,0.0,, diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b.csv b/reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b.csv new file mode 100644 index 00000000..23f00029 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/sweep_part_b.csv @@ -0,0 +1,1985 @@ +scenario_id,state,variable,medicare_household,reference,baseline,baseline_matches_reference,reference_impact_weight,baseline_impact_weight,excluded,excluded_reason,no_part_b,no_part_b_delta,no_part_b_moved,no_part_b_impact_weight,not_enrolled,not_enrolled_delta,not_enrolled_moved,not_enrolled_impact_weight,not_enrolled_direct,not_enrolled_direct_delta,not_enrolled_direct_moved,not_enrolled_direct_impact_weight,irmaa_from_2026_income,irmaa_from_2026_income_delta,irmaa_from_2026_income_moved,irmaa_from_2026_income_impact_weight 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"excluded": false, + "baseline": 1.0, + "no_part_b": 1.0, + "not_enrolled": 1.0, + "not_enrolled_direct": 1.0, + "irmaa_from_2026_income": 1.0 + }, + { + "scenario_id": "scenario_122", + "state": "MN", + "variable": "head_wic_eligible", + "excluded": false, + "baseline": 0.0, + "no_part_b": 0.0, + "not_enrolled": 0.0, + "not_enrolled_direct": 0.0, + "irmaa_from_2026_income": 0.0 + }, + { + "scenario_id": "scenario_122", + "state": "MN", + "variable": "reduced_price_school_meals_eligible", + "excluded": false, + "baseline": 0.0, + "no_part_b": 0.0, + "not_enrolled": 0.0, + "not_enrolled_direct": 0.0, + "irmaa_from_2026_income": 0.0 + }, + { + "scenario_id": "scenario_122", + "state": "MN", + "variable": "snap", + "excluded": false, + "baseline": 0.0, + "no_part_b": 0.0, + "not_enrolled": 0.0, + "not_enrolled_direct": 0.0, + "irmaa_from_2026_income": 0.0 + } + ] +} From 7bbe8b7ba5474b78464f55af6185be6d5eb99d72 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 5 Oct 2026 13:55:53 -0400 Subject: [PATCH 2/3] Answer the take-up reading in the Part B records; add the legacy impact summary and independent reviews - State the prompt's "assume program take-up" sentence in both records and the README, and why it does not settle Medicare enrollment (the benchmark's DEFAULT_TAKEUP_INPUTS has no Medicare entry; the request asks eligibility only). - Name both ACA coverage tests in the mechanism section. - leaderboard_impact.py recomputes the freeze's legacy impact_summary_by_model.csv in every case (the unchanged copy reproduces the frozen file). The stale-weight check now reports that file, which reorders 10 of 46 models with 2.15.17's weights, so the finding is narrowed to "no change in the analyze payload". - Read #191's records from a sha256-checked copy at its head 8af912a0, so this directory does not depend on #191's branch. - Add the four workflow reviewers' reports (verification/independent_reviews.json). Co-Authored-By: Claude Opus 5.5 --- .../2026-10-05-medicare-part-b/README.md | 45 ++- .../proposed_exclusions.json | 4 +- .../scripts/leaderboard_impact.py | 81 +++- .../scripts/propose_exclusions.py | 31 +- .../verification/independent_reviews.json | 355 ++++++++++++++++++ .../inputs/pr191_proposed_exclusions.json | 74 ++++ .../verification/leaderboard_impact.json | 109 +++++- 7 files changed, 659 insertions(+), 40 deletions(-) create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/independent_reviews.json create mode 100644 reference_audit/2026-10-05-medicare-part-b/verification/inputs/pr191_proposed_exclusions.json diff --git a/reference_audit/2026-10-05-medicare-part-b/README.md b/reference_audit/2026-10-05-medicare-part-b/README.md index dc022afb..f0ac3b56 100644 --- a/reference_audit/2026-10-05-medicare-part-b/README.md +++ b/reference_audit/2026-10-05-medicare-part-b/README.md @@ -23,16 +23,27 @@ These files hold for policyengine-us 2.15.17, the version that built the publish - `medicare_cost`, the impact weight of the Medicare eligibility outputs; - the ACA "at interview" coverage list and CHIP's disqualifying-coverage list. - The ACA premium tax credit tests `is_medicare_eligible`, not enrollment. + The ACA premium tax credit treats Medicare eligibility as disqualifying (`ineligible_coverage` lists `is_medicare_eligible`), so enrollment, which it also reads through the "at interview" list, adds nothing. No output reads the credit: it is not in the federal refundable-credit output. ## The prompts -No PolicyBench household sets `medicare_enrolled`, `takes_up_medicare_if_eligible`, `medicare_part_b_premium` or `health_insurance_premiums`, so no prompt states Medicare enrollment or a Part B premium. `policybench/scenarios.py` would also hide the first two: `medicare_enrolled` is in `EXCLUDED_INPUT_VARIABLES`, and `takes_up_` is an excluded prefix. The prompt's rules (`policybench/prompts.py`, `TASK_PREFACE`) say: +No PolicyBench household sets `medicare_enrolled`, `takes_up_medicare_if_eligible`, `medicare_part_b_premium` or `health_insurance_premiums`, so no prompt states Medicare enrollment or a Part B premium. `policybench/scenarios.py` would also hide the first two: `medicare_enrolled` is in `EXCLUDED_INPUT_VARIABLES`, and `takes_up_` is an excluded prefix. The prompt's rules (`policybench/prompts.py`, `TASK_PREFACE`, unchanged since before the June run) say: -- "Treat any unlisted numeric input as 0" -- "Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage" +- "Treat any unlisted numeric input as 0 and any other unlisted household fact, boolean, or status input as false." +- "Assume tax filing and program take-up when required." +- "Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage." -Sixty-one people carry `health_insurance_premiums_without_medicare_part_b`, which the prompt labels "health insurance premiums excluding Medicare Part B". That line states the person's other premiums; it states no Part B amount. scenario_114's prompt has no premium line. It lists a single head, age 69, with Social Security retirement income, $10,000 of other medical expenses and $2,000 of over-the-counter health expenses. The engine leaves over-the-counter purchases out of itemized medical expenses. +The take-up sentence is the strongest case for the frozen reading: a reader may take a 69-year-old with Social Security retirement income to be enrolled in Part B and paying the standard premium. Three things point the other way: + +- The benchmark sets take-up only for the programs its household data list. `DEFAULT_TAKEUP_INPUTS` covers Medicaid, SSI, the ACA credit, the DC property tax credit, the EITC, SNAP and tax filing, not Medicare; the engine's own default supplies Medicare enrollment. +- The Medicare request asks only whether a person is eligible. +- The more specific rules treat unlisted numbers as 0 and forbid inferring expenses or health coverage. + +So a careful reader could take the facts either way, which is the case `reference_depends_on_unlisted_input` covers. The drafted records state both sides. + +Sixty-one people in 53 households carry `health_insurance_premiums_without_medicare_part_b`, which the prompt labels "health insurance premiums excluding Medicare Part B". That line states the person's other premiums; it states no Part B amount. Ten of the 30 households charged the premium carry it, always on a charged person (12 of the 35). The label can itself suggest Medicare coverage. In scenario_073, Inkling answered that the head is Medicare-eligible partly because of "the presence of Medicare Part B premium exclusions in the health premiums". No output in those 10 households turns on the premium (below). + +scenario_114's prompt has no premium or coverage line. It lists a single head, age 69, with Social Security retirement income, $10,000 of other medical expenses and $2,000 of over-the-counter health expenses. The engine leaves over-the-counter purchases out of itemized medical expenses. ## Method @@ -44,14 +55,20 @@ Sixty-one people carry `health_insurance_premiums_without_medicare_part_b`, whic For each household with a Medicare-eligible person it records each person's Medicare variables and a propagation trace. The trace lists every engine variable, at every 2026 period the simulation computed, whose value differs between the reference and the `no_part_b` or `not_enrolled` simulation. Households where an output moves also run on a grid: the Part B readings crossed with the four readings of the state income tax in SALT from #191 (withholding estimate, liability at a fixed point, none paid, none paid without the local sales tax estimate). Outputs: `verification/sweep_part_b.csv` (every output and impact weight under every reading), `verification/sweep_part_b_summary.json`, `verification/sweep_part_b_households.json` and `verification/sweep_part_b.log`. 2. **Explain.** `scripts/explain_households.py` writes `verification/part_b_households.csv`. Each of the 33 Medicare households gets a row with the premium, the federal itemization election and medical deduction with and without it, its federal, state and SNAP outputs, and the state variables its trace shows moving. -3. **Propose.** `scripts/propose_exclusions.py` writes `proposed_exclusions.json`. It holds a `reference_depends_on_unlisted_input` record, in the format of the frozen run's `reference_exclusions.json`, for every scored output that `no_part_b` moves by more than the $1 exact-match tolerance. An output #191 already proposes to exclude gets a standalone record under `conditional_on_salt_decision` instead, to install only if #191's record is not adopted. The script also writes `verification/model_answers.csv`, which tags every model's answer on the moved outputs with the readings it lands within $1 of. +3. **Propose.** `scripts/propose_exclusions.py` writes `proposed_exclusions.json`. It holds a `reference_depends_on_unlisted_input` record, in the format of the frozen run's `reference_exclusions.json`, for every scored output that `no_part_b` moves by more than the $1 exact-match tolerance. An output #191 already proposes to exclude gets a standalone record under `conditional_on_salt_decision` instead, to install only if #191's record is not adopted. The script also writes `verification/model_answers.csv`, which tags every model's answer on the moved outputs with the readings it lands within $1 of. It reads #191's records from `verification/inputs/pr191_proposed_exclusions.json`, a copy of #191's `reference_audit/2026-10-05/proposed_exclusions.json` at head 8af912a0 that the script checks against its sha256, so this directory does not depend on #191's branch. 4. **Impact.** `scripts/leaderboard_impact.py` copies the frozen run to scratch directories and never writes the snapshot. It scores each copy with `python -m policybench.cli analyze`, the command the freeze runs. The unchanged copy reproduces every compared value of the published payload. It then scores three cases: - this audit's two records alone, as if #191 is not adopted; - #191's three records alone, which reproduces #191's figures exactly; - #191's three plus this audit's Virginia record. - Outputs: `verification/leaderboard_impact.json`, `leaderboard_impact_models.csv`, `leaderboard_impact_marginal.csv` and `leaderboard_impact.log`. -5. **Verify.** Independent reviewers checked the work. Their reports are in `verification/independent_reviews.json`, and the adversarial review is in `verification/reviews/`. + Each case also recomputes the legacy household impact summary, `impact_summary_by_model.csv`. The freeze writes it beside the analyze output with `scripts/freeze_snapshot.household_impact_summary_by_model`, and the snapshot manifest pins it. The unchanged copy reproduces the frozen file. Outputs: `verification/leaderboard_impact.json`, `leaderboard_impact_models.csv`, `leaderboard_impact_marginal.csv` and `leaderboard_impact.log`. +5. **Verify.** Four independent reviewers checked the work in a Claude Code workflow on 2026-10-05, each in its own scratch directory: + - **Mechanism.** One read the engine source, built a static graph of every reader of the premium, recomputed scenario_114 to the cent from engine intermediates, and reran all 100 households. + - **Differential.** One reran the no-premium sweep two other ways: a parameter reform setting the 2026 base premium to 0, and a structural reform cutting `medical_expense_health_insurance_premiums` to its non-Medicare branch. Both were bit-identical to `no_part_b` on all 1,984 outputs. + - **Prompts and answers.** One rendered all 100 prompts, matched them to the frozen payload, read the matching models' explanations, and judged the record against the exclusion rule. + - **Completeness.** One looked for missed channels and script bugs, validated the records against the loader, and checked every quoted number. + + Their reports are in `verification/independent_reviews.json`; their scratch paths are not kept. The reviewers raised three should-fix points, all fixed here: the records now state the take-up argument and its answer, the ACA sentence now names both tests, and the stale-weight finding below is narrowed. An adversarial review on a Subfleet review lane is in `verification/reviews/`. ## What moves @@ -130,9 +147,18 @@ From `verification/leaderboard_impact.json`. Scored outputs per model are 1,928 Every model missed both scenario_114 outputs, so excluding them raises every model's rate. +The legacy household impact summary (`impact_summary_by_model.csv`) moves too. In every case, Claude Sonnet 4.6 and Claude Opus 4.7 swap at #24/#25 on `mean_impact_score`; no other model changes rank there. #191's README does not report this file. + ## Related findings -- **Stale impact weights, with no effect on scores.** The published `reference_outputs.csv` carries impact weights for 90 eligibility outputs that differ from those 2.15.17 computes. They comprise 39 Medicare, 37 Medicaid, 9 school-meal and 5 WIC outputs. For example, `head_medicare_eligible` is $5,285.20 published and $12,065.20 on 2.15.17, a gap of $6,780.00. The references' 2026-09-29 rebuild (`../2026-09-28/scripts/build_references_latest.py`) rewrote the `value` column only. `leaderboard_impact.py --weights-check` scores a copy with 2.15.17's weights, and no compared payload value changes. +- **Stale impact weights.** The published `reference_outputs.csv` carries impact weights for 90 eligibility outputs that differ from those 2.15.17 computes. They comprise 39 Medicare, 37 Medicaid, 9 school-meal and 5 WIC outputs. For example, `head_medicare_eligible` is $5,285.20 published and $12,065.20 on 2.15.17, a gap of $6,780.00. The references' 2026-09-29 rebuild (`../2026-09-28/scripts/build_references_latest.py`) rewrote the `value` column only. + + `leaderboard_impact.py --weights-check` scores a copy with 2.15.17's weights: + - No value in the analyze payload changes. That payload holds the dashboard's model, program, heatmap, weight and failure-mode figures. + - The legacy household impact summary reads the row weights, so it changes. Every model's `mean_impact_score` rises by 0.0012 to 0.0128, and 10 of 46 models change rank (five adjacent pairs swap). + - Nothing in the app, the paper or the package reads that file; the freeze writes it and the manifest pins it. + + A separate task covers the fix. - **Enrollment changes only weights.** `not_enrolled` sets `medicare_cost`, the impact weight of the 39 Medicare eligibility outputs, to 0. It changes no output. - **scenario_031's Medicaid annotations name the wrong mechanism.** They explain the reference (eligible, through California's optional senior-or-disabled pathway) by subtracting the Part B premium from income, and 43 row annotations fault models for not doing so. - On 2.15.17, `medicaid_optional_senior_or_disabled_countable_income` subtracts no premium. It applies SSI income rules with California's $230 monthly disregard: $23,853.47 less $2,760.00 is $21,093.47, under the $22,024.80 limit (138% of the 2026 poverty guideline). @@ -164,3 +190,4 @@ Two sets of counts follow, one if d963 adopts #191's three records and one if it | exact-match misses | 7,718 → 7,672 | 7,856 → 7,764 | 4. **Paper.** Add the Medicare enrollment and Part B premium input to the paper's unlisted-input list and sweep narrative, and re-render the abstract and tables. +5. **Legacy impact summary.** The freeze regenerates `impact_summary_by_model.csv` from the scored reference; its pin in the snapshot manifest changes with the records (see "Leaderboard impact"). diff --git a/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json b/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json index d6602f52..9bd2e9a1 100644 --- a/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json +++ b/reference_audit/2026-10-05-medicare-part-b/proposed_exclusions.json @@ -17,7 +17,7 @@ "scenario_id": "scenario_114", "variable": "state_income_tax_before_refundable_credits", "reason_code": "reference_depends_on_unlisted_input", - "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it, and Virginia's itemized deductions, which start from the federal itemized deductions (va_itemized_deductions), carry it too. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", + "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it, and Virginia's itemized deductions, which start from the federal itemized deductions (va_itemized_deductions), carry it too. The prompt also says to assume program take-up when required, and a reader who takes a 69-year-old with Social Security retirement income to be enrolled in Part B and paying the standard premium gets the frozen value. But the benchmark sets take-up only for the programs its household data list (Medicaid, SSI, the ACA credit, the DC property tax credit, the EITC, SNAP and tax filing; policybench.scenarios.DEFAULT_TAKEUP_INPUTS), and Medicare is not among them; the Medicare request asks only whether the head is eligible; and the more specific rules against inferring unlisted expenses or health coverage point the other way. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", "frozen_value": 3514.14501953125, "alternative_value": 3654.14599609375, "engine_version": "policyengine-us 2.15.17", @@ -33,7 +33,7 @@ "scenario_id": "scenario_114", "variable": "federal_income_tax_before_refundable_credits", "reason_code": "reference_depends_on_unlisted_input", - "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", + "alternative_reading": "The prompt lists no Medicare enrollment and no Medicare Part B premium, and its rules say to treat any unlisted numeric input as 0 and not to infer unlisted expenses or health coverage. The reference counts a modeled Part B premium as a medical expense: policyengine-us treats the head (age 69, so Medicare-eligible) as enrolled because takes_up_medicare_if_eligible defaults to true, and medical_expense_health_insurance_premiums adds medicare_part_b_premium, the 2026 standard premium of $202.90 a month, $2,434.80, to the listed $10,000 of other medical expenses. The household itemizes, so the federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of AGI) counts it. The prompt also says to assume program take-up when required, and a reader who takes a 69-year-old with Social Security retirement income to be enrolled in Part B and paying the standard premium gets the frozen value. But the benchmark sets take-up only for the programs its household data list (Medicaid, SSI, the ACA credit, the DC property tax credit, the EITC, SNAP and tax filing; policybench.scenarios.DEFAULT_TAKEUP_INPUTS), and Medicare is not among them; the Medicare request asks only whether the head is eligible; and the more specific rules against inferring unlisted expenses or health coverage point the other way. Read with no Part B premium paid, the deduction falls by $2,434.80 and the output is the alternative value.", "frozen_value": 10729.6123046875, "alternative_value": 11265.267578125, "engine_version": "policyengine-us 2.15.17", diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py b/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py index 1ae17b9d..12d050d4 100644 --- a/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py @@ -17,10 +17,19 @@ ``salt_and_part_b`` #191's three records and this audit's Virginia record. +Each case also recomputes the legacy household impact summary that the freeze writes +beside the analyze output (``impact_summary_by_model.csv``, pinned in the snapshot +manifest), with ``scripts/freeze_snapshot.household_impact_summary_by_model`` on the +scored reference; the published case must reproduce the frozen file. + ``--weights-check`` also scores a copy whose eligibility impact weights are the ones policyengine-us 2.15.17 computes (verification/sweep_part_b.csv) instead of the -published ones, which the 2026-09-29 rebuild did not recompute, and reports whether any -compared payload value changes. +published ones, which the 2026-09-29 rebuild did not recompute. It reports whether any +compared payload value changes and how the legacy impact summary moves. + +#191's records are read from ``verification/inputs/pr191_proposed_exclusions.json``, a +copy of ``reference_audit/2026-10-05/proposed_exclusions.json`` at #191's head +8af912a0, checked against its sha256. PYTHONPATH= /.venv-pe21517/bin/python \\ reference_audit/2026-10-05-medicare-part-b/scripts/leaderboard_impact.py \\ @@ -32,6 +41,7 @@ import argparse import gzip import hashlib +import importlib.util import json import os import shutil @@ -50,8 +60,9 @@ PROPOSED = HERE / "proposed_exclusions.json" SWEEP = HERE / "verification/sweep_part_b.csv" OUT_DIR = HERE / "verification" -SALT_HEAD = "8af912a062dd7ae1373de4c043ae2727d3406930" -SALT_PATH = "reference_audit/2026-10-05/proposed_exclusions.json" +SALT_COPY = HERE / "verification/inputs/pr191_proposed_exclusions.json" +SALT_SHA256 = "3c330177762c46fa5c52b02f9f943e9d5a65e15855280b64e9b462ab27413272" +FROZEN_IMPACT = RUN / "analysis/impact_summary_by_model.csv" RUN_FILES = ( "predictions.csv.gz", "reference_outputs.csv", @@ -109,14 +120,41 @@ def sha256(path: Path) -> str: def salt_records() -> list[dict]: - text = subprocess.run( - ["git", "show", f"{SALT_HEAD}:{SALT_PATH}"], - cwd=ROOT, - check=True, - capture_output=True, - text=True, - ).stdout - return json.loads(text)["exclusions"] + if sha256(SALT_COPY) != SALT_SHA256: + raise SystemExit(f"{SALT_COPY} does not match #191's file at 8af912a0") + return json.loads(SALT_COPY.read_text())["exclusions"] + + +def legacy_impact_summary(run_dir: Path) -> pd.DataFrame: + """The freeze's impact_summary_by_model.csv, computed on a staged copy.""" + from policybench.reference_exclusions import scored_reference_for + + path = ROOT / "scripts/freeze_snapshot.py" + spec = importlib.util.spec_from_file_location("freeze_snapshot", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + truth, _ = scored_reference_for(run_dir / "reference_outputs.csv") + predictions = pd.read_csv(run_dir / "predictions.csv.gz", low_memory=False) + return module.household_impact_summary_by_model(truth, predictions) + + +def legacy_impact_change(base: pd.DataFrame, case: pd.DataFrame) -> dict: + a = base.set_index("model")["mean_impact_score"] + b = case.set_index("model")["mean_impact_score"].reindex(a.index) + rank_a = a.rank(ascending=False, method="min") + rank_b = b.rank(ascending=False, method="min") + moved = rank_a != rank_b + return { + "mean_impact_score_delta": { + "min": float((b - a).min()), + "max": float((b - a).max()), + }, + "models_changing_rank": int(moved.sum()), + "rank_changes": { + model: {"published": int(rank_a[model]), "case": int(rank_b[model])} + for model in a.index[moved] + }, + } def stage(target: Path, extra: list[dict], weights: pd.DataFrame | None = None) -> Path: @@ -308,13 +346,26 @@ def main() -> None: base = analyze(base_dir) check_reproduces_published(base) zero_published = always_zero(base_dir) + impact_base = legacy_impact_summary(base_dir) + frozen_impact = pd.read_csv(FROZEN_IMPACT) + pd.testing.assert_frame_equal( + impact_base.reset_index(drop=True), + frozen_impact, + check_exact=False, + rtol=0, + atol=1e-12, + ) summaries, tables = [], {} for name, records in cases.items(): run_dir = stage(scratch / name, records) case = analyze(run_dir) zero = {"published": zero_published, "case": always_zero(run_dir)} - summaries.append(summarize(name, records, base, case, zero)) + summary = summarize(name, records, base, case, zero) + summary["legacy_impact_summary"] = legacy_impact_change( + impact_base, legacy_impact_summary(run_dir) + ) + summaries.append(summary) tables[name] = compare(base, case) # The marginal effect of the Virginia record on top of #191's records. @@ -365,6 +416,9 @@ def main() -> None: weights_check = { "impact_weights_replaced": moved, "payload_differences": diffs, + "legacy_impact_summary": legacy_impact_change( + impact_base, legacy_impact_summary(run_dir) + ), } OUT_DIR.mkdir(parents=True, exist_ok=True) @@ -387,6 +441,7 @@ def main() -> None: marginal.to_csv(OUT_DIR / "leaderboard_impact_marginal.csv") result = { "published_reproduced": True, + "legacy_impact_summary_reproduced": True, "cases": summaries, "virginia_record_on_top_of_salt": marginal_summary, "impact_weights_check": weights_check, diff --git a/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py b/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py index 14e75341..2533b939 100644 --- a/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py +++ b/reference_audit/2026-10-05-medicare-part-b/scripts/propose_exclusions.py @@ -21,9 +21,9 @@ from __future__ import annotations +import hashlib import json import re -import subprocess from pathlib import Path import pandas as pd @@ -42,7 +42,10 @@ # PolicyEngine/policybench#191 at the head this audit read. SALT_PR = "PolicyEngine/policybench#191" SALT_HEAD = "8af912a062dd7ae1373de4c043ae2727d3406930" -SALT_PATH = "reference_audit/2026-10-05/proposed_exclusions.json" +# reference_audit/2026-10-05/proposed_exclusions.json at SALT_HEAD, kept here so the +# script does not depend on #191's branch. +SALT_COPY = HERE / "verification/inputs/pr191_proposed_exclusions.json" +SALT_SHA256 = "3c330177762c46fa5c52b02f9f943e9d5a65e15855280b64e9b462ab27413272" SALT_DECISION = "d963" DRAFTED_ON = "2026-10-05" ENGINE = "policyengine-us 2.15.17" @@ -82,14 +85,9 @@ def model_labels() -> dict[str, str]: def salt_proposal() -> dict: - text = subprocess.run( - ["git", "show", f"{SALT_HEAD}:{SALT_PATH}"], - cwd=ROOT, - check=True, - capture_output=True, - text=True, - ).stdout - return json.loads(text) + if hashlib.sha256(SALT_COPY.read_bytes()).hexdigest() != SALT_SHA256: + raise SystemExit(f"{SALT_COPY} does not match #191's file at {SALT_HEAD}") + return json.loads(SALT_COPY.read_text()) def model_answers(rows: list[dict], household: dict) -> pd.DataFrame: @@ -193,8 +191,17 @@ def record(row: dict, household: dict, summary: dict, answers: pd.DataFrame) -> f"2026 standard premium of $202.90 a month, {money(premium)}, to the listed " f"{listed(other)} of other medical expenses. The household itemizes, so the " "federal medical expense deduction (26 U.S.C. 213(a): expenses above 7.5% of " - f"AGI) counts it{where}. Read with no Part B premium paid, the " - f"deduction falls by {money(premium)} and the output is the alternative value." + f"AGI) counts it{where}. The prompt also says to assume program take-up when " + "required, and a reader who takes a 69-year-old with Social Security " + "retirement income to be enrolled in Part B and paying the standard premium " + "gets the frozen value. But the benchmark sets take-up only for the programs " + "its household data list (Medicaid, SSI, the ACA credit, the DC property tax " + "credit, the EITC, SNAP and tax filing; policybench.scenarios." + "DEFAULT_TAKEUP_INPUTS), and Medicare is not among them; the Medicare request " + "asks only whether the head is eligible; and the more specific rules against " + "inferring unlisted expenses or health coverage point the other way. Read with " + f"no Part B premium paid, the deduction falls by {money(premium)} and the " + "output is the alternative value." ) msp_households = sorted( set(summary["medicare_households"]) - set(summary["part_b_households"]) diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/independent_reviews.json b/reference_audit/2026-10-05-medicare-part-b/verification/independent_reviews.json new file mode 100644 index 00000000..162e551c --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/independent_reviews.json @@ -0,0 +1,355 @@ +{ + "workflow_run": "wf_2be49071-45a", + "run_on": "2026-10-05", + "reviewed_head": "f3dbb02001c9c65c457e8b03edecc90f161b8799", + "note": "Four independent reviewers in a Claude Code workflow (Opus 5.5), each with its own scratch directory under /private/tmp, which is not kept. Each read the engine source and recomputed independently. The structured reports are verbatim; the README's 'Verify' step says how each should_fix finding was handled.", + "reviews": [ + { + "key": "mechanism", + "verdict": "confirmed_with_issues", + "summary": "I read the 2.15.17 source and confirmed every link of the claimed mechanism. medical_expense_health_insurance_premiums returns where(health_insurance_premiums != 0, health_insurance_premiums, health_insurance_premiums_without_medicare_part_b + medicare_part_b_premium * medicare_enrolled). health_insurance_premiums has no formula (input only, with an uprating parameter), and no scenario sets it. medicare_enrolled has defined_for = is_medicare_eligible and adds = [takes_up_medicare_if_eligible]. takes_up_medicare_if_eligible has default_value = True, and PolicyBench's DEFAULT_TAKEUP_INPUTS does not set it. is_medicare_eligible is true at age 65 or over (min_age 65), or with SSDI and enough months on it. base_part_b_premium = $202.90 (base_premium.yaml for 2026-01-01) x 12 = $2,434.80. gross_medicare_part_b_premium = base + 12 x IRMAA.calc(medicare_irmaa_magi_two_years_prior). That MAGI is AGI plus tax-exempt interest for period.offset(-2) = 2024. Every scenario's inputs are keyed '2026' only, so computed 2024 AGI is 0 for all households, and the first IRMAA bracket for every filing status is threshold 0, amount 0. msp_part_b_premium_coverage pays the monthly standard premium when enrolled and MSP income- and asset-eligible. medicare_part_b_premium (defined_for medicare_enrolled) = max(gross - MSP, 0).\n\nlatest_final is latest_conventions (nine parameter-only modules) plus latest_md_local_output_scope (the state-output list). None of it touches any medical, Medicare, premium or SPM-premium variable. The SNAP hold covers standard, shelter and utility amounts, not excess_medical_expense.\n\nStatic enumeration: I grepped the package (variables, parameters and reforms, tests excluded) and built a consumer graph. Directly or downstream, the seeds reach these outputs: federal income tax and refundable credits (via tax_unit_itemizes), many states' income taxes, NYC local tax, SNAP, Medicaid eligibility (medically needy), CHIP eligibility (disqualifying coverage), and SNAP-categorical eligibility (WIC, school meals, Head Start). The SPM premium aggregates, the Part A and Part D premiums, the WA property tax exemption, the ND renters refund, PR, and the ACA PTC (not in the federal refundable-credits list) reach none.\n\nI recomputed scenario_114 to the cent from engine intermediates and parameters:\n- Federal, with the premium: itemized medical expenses $12,434.80 less the floor (7.5% x $106,572.56 = $7,992.94) gives a deduction of $4,441.86; total itemized $24,787.23 against a standard deduction of $18,150; taxable income $72,807.33; tax $10,729.61 (22% bracket, top $105,700).\n- Federal, without it: deduction $2,007.06; itemized $22,352.43, still itemizing ($11,265.27 if itemizing vs $11,969.80 if not); taxable income $75,242.13; tax $11,265.27. Delta $535.66 = 0.22 x $2,434.80.\n- Virginia: VA AGI $86,743.77; itemized deductions $19,419.94 vs $16,985.14; exemptions $930 + $800; taxable income $65,593.83 vs $68,028.63; tax 720 + 5.75% x (TI - 17,000) = $3,514.15 vs $3,654.15. Delta $140.00 = 0.0575 x $2,434.80.\n- va_deductions = where(tax_unit_itemizes, va_itemized_deductions, va_standard_deduction): Virginia itemizes only when the federal return does.\n\nI also ran an independent rerun of all 100 households. It reproduces every non-excluded frozen reference and every count in the claim: 39 Medicare-eligible people (all aged 65+, none via SSDI, none under 19 or pregnant) in 33 households; a premium charged in 30; MSP paying it for 4 people in scenario_001, 043 and 118; IRMAA MAGI 0 everywhere. Under no_part_b, not_enrolled and not_enrolled_direct, exactly two outputs move: scenario_114 federal and VA. No other output changes by any amount, and not_enrolled agrees with not_enrolled_direct on every output.\n\nThe one issue is a wording error in the audit README's peripheral ACA sentence (below). The core claim stands.", + "findings": [ + { + "severity": "should_fix", + "claim": "README line 26, \"The ACA premium tax credit tests `is_medicare_eligible`, not enrollment\", is inaccurate as a mechanism statement. PTC eligibility reads both eligibility and enrollment. The enrollment test is redundant and no output is affected, but the sentence should say both.", + "evidence": "pays_aca_premium.py:36 computes is_coverage_eligible = add(person, period, p.ineligible_coverage) == 0. parameters/gov/aca/ineligible_coverage.yaml lists both is_medicare_eligible and has_qualifying_non_marketplace_health_coverage_at_interview. That variable is qualifying_non_marketplace_health_coverage_type_count_at_interview > 0, whose adds list gov.aca.qualifying_non_marketplace_health_coverage includes medicare_enrolled. is_aca_ptc_eligible.py:23 reads pays_aca_premium. Because enrolled implies eligible, not_enrolled cannot change PTC eligibility. PTC is also absent from gov.irs.credits.refundable (2022+), so it reaches no scored output. Suggested wording: 'The ACA premium tax credit treats Medicare eligibility (and, redundantly, enrollment) as disqualifying; it is not in the federal refundable-credit output.'" + }, + { + "severity": "note", + "claim": "All mechanism links are confirmed by reading the 2.15.17 source.", + "evidence": "Files in policyengine_us/variables:\n- household/expense/health/medical_expense_health_insurance_premiums.py: where(direct != 0, direct, non_medicare + medicare_part_b_premium * medicare_enrolled).\n- household/expense/health/health_insurance_premiums.py: no formula, uprating calibration.gov.hhs.cms.moop_per_capita.\n- gov/hhs/medicare/costs/medicare_enrolled.py: defined_for is_medicare_eligible, adds [takes_up_medicare_if_eligible].\n- gov/hhs/medicare/takes_up_medicare_if_eligible.py: default_value True.\n- gov/hhs/medicare/eligibility/is_medicare_eligible.py: age >= 65, or SSDI > 0 with months >= the minimum.\n- part_b/base_part_b_premium.py: base_premium x 12; base_premium.yaml 2026-01-01: 202.9.\n- part_b/gross_medicare_part_b_premium.py: base + 12 x irmaa[status].calc(magi).\n- medicare_irmaa_magi_two_years_prior.py: add(adjusted_gross_income, tax_exempt_interest_income) at period.offset(-2), i.e. 2024, since 2024 >= FIRST_MODELED_YEAR 2015.\n- savings_programs/msp_part_b_premium_coverage.py: sum over months of enrolled & msp_income_eligible & msp_asset_eligible times the monthly standard premium.\n- part_b/medicare_part_b_premium.py: defined_for medicare_enrolled, max(gross - msp, 0).\nThe `* medicare_enrolled` factor in medical_expense_health_insurance_premiums is redundant with medicare_part_b_premium's defined_for, and harmless." + }, + { + "severity": "note", + "claim": "The latest_final reform touches none of the audited variables.", + "evidence": "latest_final.py composes latest_conventions (CA, IRS sales tax, WI, ID, MN, MD, MI, MO holds plus the SNAP FY2026 hold) with latest_md_local_output_scope. Every part only calls modify_parameters. A grep for medic|premium|moop|health_insurance across all fixes/*.py found only docstring and comment text. The SNAP hold nodes are standard, excess_shelter_expense, utility, max/min allotment and asset limits; excess_medical_expense is not among them. The CA hold changes CA's itemized-limitation AGI threshold, which is unrelated to the medical variables." + }, + { + "severity": "note", + "claim": "Direct readers found by static enumeration, and whether each can reach a PolicyBench output (from a grep of variables, parameters and reforms excluding tests, plus a consumer-graph BFS).", + "evidence": "medical_expense_health_insurance_premiums, read by:\n- itemized_medical_expenses: reaches federal, state, local and refundable outputs; live only in scenario_114.\n- snap_allowable_medical_expenses: reaches SNAP, then WIC, school meals and Head Start categorical eligibility and the Medicaid work-requirement pass-through.\n- medicaid_medically_needy_medical_expenses: reaches is_medicaid_eligible through is_medically_needy_for_medicaid and medicaid_category.\n- hud_medical_expenses: hud_adjusted_income, hud_hap, housing_assistance (take-up defaults True), rent, housing_cost, then the SNAP shelter deduction and CA renter credit; reaches outputs.\n- pa_ccw_medical_expenses (PA): pa_ccw, child_care_subsidies, childcare_expenses, then CDCC and the SNAP dependent-care deduction; reaches outputs.\n- mt_medical_expense_deduction_indiv and oh_insured_unreimbursed_medical_care_expense_amount: reach state tax.\n- The WA PTE sources.yaml mention is a comment only.\n\nitemized_medical_expenses, read by:\n- medical_expense_deduction (federal).\n- AL, AR (individual and joint), AZ, HI, MO qualified health insurance premiums, MT joint, NJ, NM (deduction, exemption, and credit to state_refundable_credits): reach state outputs.\n- nd_renters_refund_income and pr_medical_expense_deduction: no output.\n\nmedical_expense_deduction, read by:\n- total_itemized_taxable_income_deductions and itemized_deductions_less_salt, both via gov.irs.deductions.itemized_deductions.\n- State variables: AZ, CA itemized limitation and AMTI, CO addback, DC, IA, KY, LA, MA Part B exemption, ME, MN itemized and AMT, MO, NC, NY max and phase-out (also NYC local), OK, UT taxpayer credit, VA reduced-itemized applicable and reducible lists, VT. All reach state outputs.\n- state_itemized_deductions: no output.\n- Two contrib reforms (MA, MN) that are not applied.\n\nmedicare_part_b_premium, read by:\n- medical_expense_health_insurance_premiums.\n- spm_unit_health_insurance_premiums: SPM MOOP, spm_unit_spm_expenses, spm_unit_net_income, then poverty measures only; no output.\n- WA PTE combined disposable income: no output.\n\nmedicare_enrolled, read by:\n- medicare_part_b_premium and msp_part_b_premium_coverage: path above.\n- medicare_part_a_premium, msp_part_a_premium_coverage and income_adjusted_part_d_premium_surcharge: SPM only, no output.\n- gross_medicare_part_b_premium_if_enrolled and medicare_cost: the impact weight of *_medicare_eligible only.\n- medicare_gross_cost: CBO benefits, no output.\n- ACA at-interview list: PTC, which is not a scored output.\n- CHIP disqualifying-coverage list: is_chip_eligible_child and ca_cchip_eligible (age < 19), and is_chip_fcep_eligible_person (CA pregnant). Reaches is_chip_eligible, but no Medicare-eligible PolicyBench person is under 19 or pregnant.\n\nsnap_allowable_medical_expenses, read by snap_excess_medical_expense_deduction only." + }, + { + "severity": "note", + "claim": "Several channels are statically live but dormant in this run, and my own propagation trace shows each one stopping before an output.", + "evidence": "Own trace of every 2026 variable that differs between reference and no_part_b, over the 33 Medicare households:\n- hud_adjusted_income moves in 10 households (031, 044, 059, 060, 072, 085, 095, 111, 114, 115); housing_assistance moves in none.\n- snap_excess_medical_expense_deduction moves in every premium household. SNAP is the $24/month minimum both ways in 013, 027 and 108 (expected contribution above the maximum allotment) and $0 elsewhere.\n- State taxable incomes move in AZ 013/040, MO 021, CA 031, KS 044, OH 048/107 (e.g. 048 OH taxable income 3,095.40 vs 5,256.00), NY 071/104, AL 092/115 and NJ 095 (nj_main_income_tax 0.00 vs 26.35). Every state output stays the same: $0, or $4.68 in AL 115.\n- scenario_072 flips tax_unit_itemizes (1 to 0) with federal taxable income $0 either way.\n- pa_ccw_medical_expenses moves in 085; pa_ccw does not.\n- No is_medicaid_eligible flips." + }, + { + "severity": "note", + "claim": "Impact weights are affected only under the enrollment readings, and the audit already documents this.", + "evidence": "medicare_cost (defined_for medicare_enrolled) is the impact weight of *_medicare_eligible. not_enrolled and not_enrolled_direct set all 39 to 0. no_part_b changes none, because medicare_cost reads gross_medicare_part_b_premium_if_enrolled, not medicare_part_b_premium. My scenario_114 medicare_cost is $12,065.20 against a published weight of $5,285.20; the sweep summary already lists this stale-weight mismatch, and README line 135 explains it." + }, + { + "severity": "note", + "claim": "The prompt facts hold for scenario_114.", + "evidence": "policybench/prompts.py TASK_PREFACE contains \"Treat any unlisted numeric input as 0\" and \"Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage\". No scenario sets medicare_enrolled, takes_up_medicare_if_eligible, medicare_part_b_premium(s) or health_insurance_premiums. scenario_114's inputs have no premium field: age 69, Social Security retirement $23,328, other medical expenses $10,000, OTC $2,000. Some other Medicare households carry health_insurance_premiums_without_medicare_part_b, which the prompt labels \"health insurance premiums excluding Medicare Part B\". That names Part B but states no amount, and none of those outputs move." + } + ], + "numbers_checked": [ + "base_part_b_premium 2026: 202.90/month x 12 = 2,434.80 (parameter and engine)", + "medicare_irmaa_magi_two_years_prior = 0.00 for all 100 households; 2024 AGI of scenario_114 = 0.00; IRMAA first bracket threshold 0 / amount 0 for all five filing statuses", + "gross_medicare_part_b_premium = 2,434.80 for all 39 eligible people; net 0.00 for the 4 MSP-covered (scenario_001 x2, 043, 118)", + "39 Medicare-eligible people / 33 households / 30 with a premium charged (independent rerun)", + "scenario_114 AGI 106,572.56; floor 0.075 x AGI = 7,992.94", + "scenario_114 itemized medical expenses 12,434.80 vs 10,000.00; medical_expense_deduction 4,441.86 vs 2,007.06", + "scenario_114 charitable 10,913.78; SALT 9,431.59 (real estate 4,064.29 + state withholding estimate 5,367.30); interest 0.00", + "scenario_114 total itemized 24,787.23 vs 22,352.43; standard 18,150.00; itemizes both (liability 10,729.61 / 11,265.27 vs 11,969.80 if not itemizing)", + "scenario_114 QBID 4,872.35; additional senior deduction 4,105.65; deductions if itemizing 33,765.23 vs 31,330.43", + "scenario_114 federal taxable income 72,807.33 vs 75,242.13 (22% bracket 50,400-105,700)", + "scenario_114 federal income tax before refundable credits 10,729.61 vs 11,265.27 (hand 10,729.6124 / 11,265.2684); delta 535.66 = 0.22 x 2,434.80", + "scenario_114 VA AGI 86,743.77; VA itemized 19,419.94 vs 16,985.14; exemptions 930 + 800", + "scenario_114 VA taxable income 65,593.83 vs 68,028.63; VA brackets 2%/3%/5%/5.75% at 0/3,000/5,000/17,000", + "scenario_114 VA income tax before refundable credits 3,514.15 vs 3,654.15 (hand 3,514.1452 / 3,654.1462); delta 140.00 = 0.0575 x 2,434.80", + "proposed_exclusions.json values: VA frozen 3514.14501953125 / alt 3654.14599609375; federal frozen 10729.6123046875 / alt 11265.267578125 (match engine)", + "independent rerun: 0 non-excluded mismatches vs frozen references; exactly 2 outputs move under no_part_b, not_enrolled and not_enrolled_direct; 0 other outputs differ by any amount", + "not_enrolled zeroes 39 medicare_eligible impact weights (12,065.20 to 0); no_part_b moves none" + ], + "scratch_dir": "/private/tmp/claude-501/pb-partb-verify (my files: static_graph.py/.json/.out, readers.txt, paths1.txt, s114.py/.json, hand.py/.json, mini_sweep.py/.json/.log, mech-verify-7363/; other agents share this directory)" + }, + { + "key": "differential", + "verdict": "confirmed", + "summary": "I re-ran the no-Part-B sweep two ways, neither of which uses the audit's person input medicare_part_b_premium = 0. (b) is a parameter reform on top of latest_final that sets gov.hhs.medicare.part_b.base_premium to 0 for 2026 only (2025 stays $185.00, 2027 stays $202.90). (c) is a structural reform that cuts medical_expense_health_insurance_premiums down to its non-Medicare branch: where(direct != 0, direct, health_insurance_premiums_without_medicare_part_b). I computed all 1,984 outputs for all 100 households with 8 worker processes. My expanded output set is identical to the 1,984 rows in reference_outputs.csv and in the audit CSV.\n\nThe unmodified system (a) reproduces all 1,928 scored references exactly, with a maximum absolute difference of 0.0. Nineteen excluded outputs differ from their frozen values, the same count as the audit's summary. (a) is bit-identical to the audit's baseline column on all 1,984 outputs.\n\nBoth (b) and (c) are bit-identical to the audit's no_part_b column on all 1,984 outputs: maximum absolute difference 0.0, none above $0.01, none above 1e-6. Each moves exactly two outputs versus (a), and no other output changes by any amount:\n- scenario_114 federal_income_tax_before_refundable_credits: 10,729.61 -> 11,265.27 (+535.66)\n- scenario_114 VA state_income_tax_before_refundable_credits: 3,514.15 -> 3,654.15 (+140.00)\n\nNo SNAP or eligibility output changes by any amount.\n\nThe diagnostics confirm the rest of the claim:\n- 39 Medicare-eligible people in 33 households; medicare_enrolled equals is_medicare_eligible for every person under (a).\n- base_part_b_premium is $2,434.80 for every eligible person.\n- medicare_irmaa_magi_two_years_prior is 0 for every tax unit, and gross equals base for every eligible person, so no IRMAA applies.\n- Under (b), gross_medicare_part_b_premium is 0 for every person, so IRMAA contributes nothing.\n- The premium is charged in 30 households. MSP coverage of $2,434.80 brings the premium to 0 for 4 people: scenario_001 head and spouse, scenario_043 head, scenario_118 head.\n- For scenario_114: AGI is $106,572.56; the medical expense deduction falls from $4,441.86 to $2,007.06; the household itemizes under both.\n\nI read the engine code this session and it matches the stated mechanism: the medical_expense_health_insurance_premiums formula, medicare_enrolled (adds takes_up_medicare_if_eligible, default True, defined_for is_medicare_eligible), the base_premium.yaml 2026 value of 202.9, and gross_medicare_part_b_premium reading medicare_irmaa_magi_two_years_prior, which is AGI plus tax-exempt interest from period offset -2. The prompt rule text is in policybench/prompts.py lines 19-22.\n\nScripts and outputs are in /private/tmp/claude-501/pb-partb-verify/diff-param-struct-7363adc3/: differential_no_part_b.py, analyze.py, results.json, differential_outputs.csv, analyze.log.", + "findings": [ + { + "severity": "note", + "claim": "Two different reform mechanisms both reproduce the audit's no_part_b column exactly. This shows that, for these 100 households, the Part B premium reaches outputs only through medical_expense_health_insurance_premiums.", + "evidence": "The structural reform (c) removes only that channel and leaves the spm_unit_health_insurance_premiums path (which adds medicare_part_b_premium) intact. The parameter reform (b) also zeroes base_part_b_premium in msp_part_b_premium_coverage, msp_benefit_value, il_smib_person and medicare_cost. Both match the audit's person-input override on all 1,984 outputs with max abs diff 0.0. So the SPM premium, MSP, IL SMIB and medicare_cost channels change none of the 1,984 outputs." + }, + { + "severity": "note", + "claim": "The claim that no prompt states Medicare enrollment or a Part B premium holds, but 53 of 100 rendered prompts name Medicare Part B as an exclusion.", + "evidence": "I rendered every prompt with policybench.prompts.describe_household. The only Medicare text in any prompt is the line 'health insurance premiums excluding Medicare Part B: $X', from the input health_insurance_premiums_without_medicare_part_b. It appears in 53 households, including Medicare households 001, 013, 021, 022, 033, 044, 062, 072, 085, 098 and 120. No prompt sets medicare_enrolled or a Part B premium amount. scenario_114's prompt has no Medicare line at all. A reader could take 'excluding Medicare Part B' as a hint that a separate Part B premium exists. This does not touch the one household that moves." + }, + { + "severity": "note", + "claim": "The parameter reform is limited to 2026, and only IRMAA's zero MAGI keeps the IRMAA amount at 0. Zeroing the base premium alone would not remove IRMAA for a household that set 2024 MAGI.", + "evidence": "Under the reform the base premium reads 0.0 at 2026-01-01 and 2026-12-01, while 2025-06-01 stays 185.0 and 2027-06-01 stays 202.9. gross_medicare_part_b_premium is 0 for every person under (b) only because medicare_irmaa_magi_two_years_prior is 0 for every tax unit (verified), so p..calc(0) hits the first bracket, whose amount is 0." + }, + { + "severity": "note", + "claim": "The scratch directory /private/tmp/claude-501/pb-partb-verify is shared with other verification agents.", + "evidence": "Other agents' files were already in the directory, including fixes/, mechanism/, differential-reform-b/ and analyze_extra.py. My copies of the fixes files were byte-identical to the sources (checked with cmp), and the exact reproduction rules out any corrupted load. I moved my outputs into the subdirectory diff-param-struct-7363adc3/ so they don't collide with the others." + } + ], + "numbers_checked": [ + "outputs: 1,984 computed = 1,984 reference rows = 1,984 audit rows (identical key sets)", + "scored outputs 1,928; excluded 56; (a) vs scored references: 0 mismatches, max abs diff 0.0", + "excluded outputs where (a) differs from frozen: 19 (audit summary also 19)", + "(a) vs audit baseline column: max abs diff 0.0 over 1,984", + "(b) parameter reform vs audit no_part_b: max abs diff 0.0, 0 outputs > $0.01, 0 > 1e-6", + "(c) structural reform vs audit no_part_b: max abs diff 0.0, 0 outputs > $0.01, 0 > 1e-6", + "outputs moving > $1 under (b) and (c): 2; outputs changing by any amount (>1e-6): 2", + "scenario_114 federal_income_tax_before_refundable_credits 10,729.61 -> 11,265.27 (+535.66)", + "scenario_114 VA state_income_tax_before_refundable_credits 3,514.15 -> 3,654.15 (+140.00)", + "SNAP and eligibility outputs changing under (b)/(c): 0", + "Medicare-eligible people 39 in 33 households; enrolled == eligible for all people under (a)", + "households with medicare_part_b_premium > 0: 30; eligible households with no premium: scenario_001, scenario_043, scenario_118", + "MSP Part B coverage 2,434.80 each for scenario_001 head, scenario_001 spouse, scenario_043 head, scenario_118 head (net premium 0.00)", + "base_part_b_premium 2,434.80 (202.90 x 12) for every eligible person; base_premium param 2026 = 202.9", + "medicare_irmaa_magi_two_years_prior = 0 for all 100 tax units; gross == base for every eligible person (no IRMAA)", + "under (b) gross_medicare_part_b_premium = 0.00 for every person; base_premium 2025 185.0 and 2027 202.9 unchanged", + "scenario_114 AGI 106,572.56; medical_expense_deduction 4,441.86 (ref) -> 2,007.06 (no Part B); itemizes 1 under both", + "4,441.86 = 12,434.80 - 0.075 x 106,572.56; 2,007.06 = 10,000.00 - 7,992.94" + ], + "scratch_dir": "/private/tmp/claude-501/pb-partb-verify/diff-param-struct-7363adc3" + }, + { + "key": "prompts_answers", + "verdict": "confirmed", + "summary": "I checked the prompt and answer side of the claim and it holds, with nothing blocking.\n\n**Prompts.** All 100 frozen scenarios were rebuilt with scenario_from_dict and rendered with make_no_tools_batch_prompt. No prompt states Medicare enrollment, Medicare coverage or a Part B premium amount.\n- No scenario JSON sets medicare_enrolled, takes_up_medicare_if_eligible, medicare_part_b_premium or health_insurance_premiums. The only Medicare-named key is health_insurance_premiums_without_medicare_part_b: 61 people in 53 scenarios.\n- to_pe_household's DEFAULT_TAKEUP_INPUTS has no Medicare take-up flag.\n- The prompt code would hide both enrollment inputs even if the data carried them: medicare_enrolled is in EXCLUDED_INPUT_VARIABLES, and takes_up_ is an excluded prefix.\n- 10 of the 30 Part B households carry the line \"health insurance premiums excluding Medicare Part B\" (12 people, all aged 65 or over): 013, 021, 022, 033, 044, 062, 072, 085, 098 and 120. scenario_114 does not. Its prompt lists a head aged 69 with $10,000 other medical expenses, $2,000 over-the-counter health expenses and $23,328 Social Security retirement income, and no coverage line of any kind.\n- The prompt text in force at answer time matches what I rendered. TASK_PREFACE is unchanged since f032f4b8 (2026-05-14), before the 2026-06-12 run. The only later prompts.py change (8f3c44cb) touches the explanation-repair prompt. No scenarios.py change since June touches the exclusion lists.\n\n**Engine recompute.** I recomputed scenario_114 myself on 2.15.17 with latest_final, using copies of the fixes in my scratch directory.\n\n| Value | Reference | no_part_b, not_enrolled, not_enrolled_direct |\n|---|---:|---:|\n| Federal income tax before refundable credits | 10,729.61 | 11,265.27 |\n| Virginia income tax before refundable credits | 3,514.15 | 3,654.15 |\n| Itemized medical expenses | 12,434.80 | 10,000.00 |\n| Medical expense deduction | 4,441.86 | 2,007.06 |\n\n- The base premium and medicare_part_b_premium are both 2,434.80. The parameter for 2026-01-01 is 202.9 a month.\n- The IRMAA MAGI from two years prior is 0.00. AGI is 106,572.56. The household itemizes under every reading.\n- The deltas match the premium taxed at each rate: federal 535.66 = 2,434.80 \u00d7 22%; Virginia 140.00 = 2,434.80 \u00d7 5.75%.\n\n**Answers.** 46 models answered scenario_114; kimi-k2.6 left both outputs blank.\n- Within $1 of the readings: Virginia 3,654.15 is matched by gpt-6-astra (3,654.16) and gpt-6.1-sol (3,654.66). Federal 11,642.16 is matched by gpt-6.1-sol (11,642.09) and gpt-6-astra (11,642.20). Federal 11,969.80 is matched by kimi-k3 (11,969.85) and gpt-5.6-sol (11,970.00).\n- No model is within $1 of the frozen values. The nearest federal answer is claude-haiku-4.5 at 10,268.00 ($461.61 off). The nearest Virginia answer is grok-4.6 at 3,509.00 ($5.15 off).\n- None of the four matching models' explanations mentions Medicare, Part B or medical expenses at all. So none says it left the premium out because it was unlisted. That is shown only by arithmetic, which I recomputed:\n - Astra's federal itemized total reconciles exactly to medical deductions on $10,000 alone.\n - Kimi K3's itemized total of 17,518 is 11,447 + 4,064 + 2,007. With the premium it would be 19,952.84, above the 18,150 standard deduction, so Kimi would have itemized.\n- No model's explanation in any of the 100 scenarios adds a Part B premium as an expense.\n\n**Exclusion test.** Under the reference_exclusions.py test, the Virginia record qualifies.\n- The reference depends on an engine default that no household input carries and that the prompt code would hide anyway.\n- A careful reader could take the stated facts the other way; in fact the prompt's own rules favour the other way.\n- With the premium, Virginia is 3,514.15 under all four SALT readings, so the record stands whatever d963 decides about #191.", + "findings": [ + { + "severity": "note", + "claim": "No scenario sets any of the four Medicare/premium inputs, and no prompt states Medicare enrollment, Medicare coverage or a Part B amount.", + "evidence": "render_prompts.py walks every key of all 100 scenario_json payloads: zero hits for medicare_enrolled, takes_up_medicare_if_eligible, medicare_part_b_premium or health_insurance_premiums. Every Medicare-named key is health_insurance_premiums_without_medicare_part_b (61 people, 53 scenarios). DEFAULT_TAKEUP_INPUTS['person'] holds only takes_up_medicaid_if_eligible and takes_up_ssi_if_eligible. is_excluded_prompt_input_name would hide medicare_enrolled (it is in EXCLUDED_INPUT_VARIABLES) and takes_up_medicare_if_eligible (the takes_up_ prefix). The only hidden keys actually present in the data are is_tax_unit_head and is_tax_unit_spouse." + }, + { + "severity": "note", + "claim": "Every prompt line that mentions Medicare, premium, coverage or insurance, and what each states. None asserts enrollment or a premium paid.", + "evidence": "In TASK_PREFACE (all 100 prompts): health coverage facts are to be treated as constant; 'Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage'.\nIn the requested outputs (all 100): one '_medicare_eligible: whether is eligible for Medicare (1 if yes, 0 if no)' line per person. payroll_tax and self_employment_tax mention 'Medicare tax' and 'Additional Medicare Tax' (payroll taxes). federal_refundable_credits says 'exclude the ACA Premium Tax Credit'.\nIn household facts:\n- 'has employer-sponsored insurance': 92 people, 53 scenarios; Part B households 000, 044, 085, 104, 120.\n- 'health insurance premiums excluding Medicare Part B: $N': 61 people, 53 scenarios.\n- 'other health insurance premiums: $N': 56 people, 49 scenarios.\n- 'employer sponsored insurance premiums: $N': 49 people, 42 scenarios.\n- 'fsla overtime premium: $N': 11 people (a wage premium).\n- 'has indian health service coverage at interview': 079 and 108.\n- 'has other means tested health coverage at interview': 066.\n- 'has champva health coverage at interview': 116." + }, + { + "severity": "note", + "claim": "10 of the 30 Part B households carry 'health insurance premiums excluding Medicare Part B'; scenario_114 does not.", + "evidence": "The households are scenario_013 ($298), 021 ($210), 022 ($2,416), 033 ($220 and $180, both people), 044 ($3,600, the 66-year-old head), 062 ($4,000), 072 ($16,000 each, both people), 085 ($300), 098 ($336) and 120 ($960). That is 12 people, all aged 65 or over. scenario_001 also carries it ($20 each), but MSP covers its premium, so it is not in part_b_households. The other 20 Part B households, including scenario_114, have no premium line. scenario_114's facts: head age 69, wages $22,700, other medical expenses $10,000, over-the-counter health expenses $2,000, Social Security retirement income $23,328. It has no coverage or insurance line." + }, + { + "severity": "note", + "claim": "Recomputing scenario_114 independently on 2.15.17 with latest_final reproduces every number in the claim.", + "evidence": "s114_engine.py:\n- Reference: is_medicare_eligible 1, medicare_enrolled 1, base_part_b_premium 2,434.80, medicare_part_b_premium 2,434.80, medicare_irmaa_magi_two_years_prior 0.00, AGI 106,572.56, itemized_medical_expenses 12,434.80 (over-the-counter excluded), medical_expense_deduction 4,441.86, itemizes, federal 10,729.61, Virginia 3,514.15.\n- no_part_b, not_enrolled and not_enrolled_direct agree exactly: itemized medical 10,000.00, deduction 2,007.06, itemizes, federal 11,265.27, Virginia 3,654.15.\n- base_premium.yaml has 2026-01-01: 202.9.\n- Source read this session: medical_expense_health_insurance_premiums returns where(direct != 0, direct, non_medicare + medicare_part_b_premium * medicare_enrolled); medicare_enrolled adds takes_up_medicare_if_eligible (default_value True) with defined_for is_medicare_eligible; va_deductions selects va_itemized_deductions when tax_unit_itemizes.\n- Frozen reference_outputs.csv: 10729.6123046875 and 3514.14501953125. Neither output appears in the frozen reference_exclusions.json." + }, + { + "severity": "note", + "claim": "The four expected models are the only ones within $1 of the alternative readings. Their explanations never mention Medicare, Part B or medical expenses, so the premium's omission is shown by arithmetic, not stated.", + "evidence": "Verbatim excerpts:\n- gpt-6-astra federal 11,642.20: 'estimated itemized deductions are $20,639.33 after the charitable floor, the senior deduction is $4,105.63, and the qualified business income deduction is $4,872.40.'\n- gpt-6-astra Virginia 3,654.16: 'Estimated allowable itemized deductions are $16,985.18 and personal and age exemptions total $1,730.'\n- gpt-6.1-sol federal 11,642.09: 'After itemized deductions, the phased-down senior deduction and a $4,872.40 qualified-business-income deduction, applying the 2026 brackets produces this tax'\n- gpt-6.1-sol Virginia 3,654.66: 'Using $16,985.18 of itemized deductions and $1,730 of personal and age exemptions yields this tax'\n- kimi-k3 federal 11,969.85: 'standard deduction 18,150 (16,100 single + 2,050 age-65 addition, exceeding itemized deductions of 17,518)'\n- kimi-k3 Virginia 4,134.58 (not within $1 of any reading): 'standard deduction 8,500 (consistency with federal non-itemizing)'\n- gpt-5.6-sol federal 11,970.00: 'the 2026 standard and age deductions, senior deduction, non-itemizer charitable deduction, and QBI deduction produce taxable income of about 78445'\n- gpt-5.6-sol Virginia 4,174.00: 'after the 8750 standard deduction and 930 personal exemption'\nArithmetic, using the models' AGI of 106,572.80:\n- Astra federal itemized: (10,000 \u2212 7,992.96) + 4,064 + 3,654.16 + (11,447 \u2212 532.86) = 20,639.34; Astra states 20,639.33. With the premium it would be 23,074.14.\n- Astra and 6.1 Sol Virginia itemized: 20,639.34 \u2212 3,654.16 = 16,985.18.\n- Kimi: 11,447 + 4,064 + 2,007.04 = 17,518.04. With the premium it would be 19,952.84, above 18,150, which would flip Kimi to itemizing." + }, + { + "severity": "note", + "claim": "No model's explanation, in scenario_114 or any of the 100 scenarios, adds a Part B premium as an expense. No model is within $1 of either frozen value.", + "evidence": "scenario_114 has 92 rows (46 models x 2 outputs). Zero explanations or raw_responses match 'part b', '2,434' or '202.9', and no federal or Virginia explanation contains a premium-inclusive medical figure (4,441, 6,441, 12,434). The 45 non-null answers miss the frozen values by at least $461.61 federal (claude-haiku-4.5, 10,268.00) and $5.15 Virginia (grok-4.6, 3,509.00); kimi-k2.6 is null for both. Across all scenarios, 37 explanations mention Part B. They are Massachusetts 'Part B income', eligibility boilerplate, or questions about duplicated premium lines. None adds the premium." + }, + { + "severity": "note", + "claim": "The Virginia answers nearest the frozen value got there by counting the $2,000 over-the-counter expense, not the Part B premium. The engine excludes over-the-counter purchases.", + "evidence": "grok-4.6 (3,509.00) and deepseek-v4-pro-0813 (3,508.52) both use Virginia itemized 19,518 = 11,447 + 4,064 + 4,007. The 4,007 is 12,000 of medical expenses including over-the-counter, less 7.5% of AGI. grok-4.6's federal note says 'medical 4007 after 7.5% AGI floor on 12000 of medical/OTC costs'. claude-fable-5.1 (3,524.00) uses Virginia itemized of 18,985. These all fall outside the $1 band." + }, + { + "severity": "note", + "claim": "The 'excluding Medicare Part B' label is itself read by some models as a signal about Medicare status. This bears on households that carry the label, not on scenario_114.", + "evidence": "- claude-sonnet-5, scenario_072 head_chip_eligible = 0: 'is already enrolled in Medicare Part B and other coverage'.\n- inkling, scenario_073 head_medicare_eligible = 1: cites 'the presence of Medicare Part B premium exclusions in the health premiums'.\n- claude-sonnet-4.6, scenario_073 head_medicare_eligible = 0: 'no Medicare Part B premiums listed'.\nNone of these feeds a tax output." + }, + { + "severity": "note", + "claim": "Out of scope, for prompt quality: many prompts list both 'health insurance premiums excluding Medicare Part B' and 'other health insurance premiums', usually for the same amount, and models puzzle over double counting.", + "evidence": "In 2.15.17, medical_expense_health_insurance_premiums reads only health_insurance_premiums_without_medicare_part_b, not other_health_insurance_premiums. In all 10 Part B households carrying the first line, the second line appears too. claude-sonnet-4.6 explanations in scenarios 005, 022, 033, 053, 081, 084 and 121 discuss whether the two lines duplicate each other. INPUT_LABEL_OVERRIDES also keeps unused labels for medicare_enrolled (an excluded input) and medicare_part_b_premiums (no such engine input)." + }, + { + "severity": "note", + "claim": "Exclusion test, case for the frozen reading (premium paid): a careful reader could argue it. Statutory enrollment rules were not re-read this session.", + "evidence": "- The prompt says 'Assume tax filing and program take-up when required'. The engine's takes_up_medicare_if_eligible default of True mirrors that rule.\n- The head is 69 and the prompt states $23,328 of Social Security retirement income. Under the commonly cited SSA/CMS rule, people already drawing Social Security at 65 are auto-enrolled in Part B, with the premium withheld from benefits.\n- The 2026 standard premium ($202.90/month) was published before most models answered.\n- The prompt asks for head_medicare_eligible, which invites reasoning about Medicare." + }, + { + "severity": "note", + "claim": "Exclusion test, case for the alternative (no premium): the prompt's specific rules point this way, and every matching model's arithmetic follows it.", + "evidence": "- 'Treat any unlisted numeric input as 0': the premium is an unlisted number.\n- 'Do not infer unlisted income, expenses, ... or health coverage': Part B enrollment is unlisted health coverage, and the premium is an unlisted expense.\n- scenario_114 lists its medical expenses explicitly ($10,000 other, $2,000 over-the-counter) and states no coverage at all.\n- Eligibility, which is asked, is not enrollment. Part B enrollment can be declined.\n- 'Program take-up' most naturally means benefit programs. The specific health-coverage clause governs over the general take-up clause.\n- Observed: every model whose itemized arithmetic can be reconstructed used $10,000 of medical expenses, or $12,000 with over-the-counter, never the premium. None matched the frozen value.\nThe two rules conflict (take-up vs no inferred coverage). That conflict is itself what the docstring's 'could take the stated facts the other way' covers." + }, + { + "severity": "note", + "claim": "Verdict: the Virginia record meets the reference_depends_on_unlisted_input rule, and its standing does not depend on d963.", + "evidence": "- The reference depends on engine inputs the household facts never carried: takes_up_medicare_if_eligible and medicare_enrolled default to enrolled, so medicare_part_b_premium is modeled. The prompt could not list them, and the prompt code would hide them anyway.\n- A careful reader could, and per the prompt's rules should, take the facts the other way.\n- The record names the unlisted input, the alternative reading, frozen 3,514.15 and alternative 3,654.15. Both values reproduce on 2.15.17.\n- With the premium, Virginia is 3,514.15 under all four SALT readings, so the Virginia dependence on Part B stands whatever #191 decides.\n- The reason code is right: the engine applies the law correctly given its enrollment default. The fault is a defaulted unlisted input, not a misapplied rule on stated facts.\n- Optional wording improvement: the alternative_reading could acknowledge the 'program take-up' rule as the source of the frozen reading." + } + ], + "numbers_checked": [ + "scenario JSONs setting medicare_enrolled / takes_up_medicare_if_eligible / medicare_part_b_premium / health_insurance_premiums: 0 of 100", + "health_insurance_premiums_without_medicare_part_b carriers: 61 people in 53 scenarios", + "Part B households carrying the 'excluding Medicare Part B' line: 10 of 30 (013, 021, 022, 033, 044, 062, 072, 085, 098, 120); 12 people; scenario_114: no", + "base_part_b_premium 2026: 202.9 x 12 = 2,434.80; medicare_part_b_premium scenario_114 = 2,434.80", + "medicare_irmaa_magi_two_years_prior scenario_114 = 0.00", + "AGI scenario_114 = 106,572.56", + "itemized_medical_expenses 12,434.80 (reference) vs 10,000.00 (no_part_b / not_enrolled / not_enrolled_direct)", + "medical_expense_deduction 4,441.86 vs 2,007.06", + "federal 10,729.61 -> 11,265.27 (delta 535.66 = 2,434.80 x 22%)", + "VA 3,514.15 -> 3,654.15 (delta 140.00 = 2,434.80 x 5.75%)", + "frozen reference_outputs.csv: 10729.6123046875 federal, 3514.14501953125 VA", + "scenario_114: 46 models, 45 non-null answers per output (kimi-k2.6 null)", + "VA within $1 of 3,654.15: gpt-6-astra 3,654.16; gpt-6.1-sol 3,654.66", + "federal within $1 of 11,642.16: gpt-6.1-sol 11,642.09; gpt-6-astra 11,642.20", + "federal within $1 of 11,969.80: kimi-k3 11,969.85; gpt-5.6-sol 11,970.00", + "within $1 of frozen 10,729.61 or 3,514.15: none; nearest federal claude-haiku-4.5 10,268.00 (461.61 off), nearest VA grok-4.6 3,509.00 (5.15 off)", + "within $1 of 11,265.27, 11,137.31, 11,758.09, 11,783.48 or VA 4,127.67: none", + "Astra federal itemized reconstruction 20,639.34 vs stated 20,639.33; with premium 23,074.14", + "Astra / 6.1 Sol VA itemized 16,985.18 = 20,639.34 - 3,654.16", + "Kimi K3 itemized 17,518.04 = 11,447 + 4,064 + 2,007.04; with premium 19,952.84 > 18,150", + "scenario_114 explanations or raw_responses mentioning Part B: 0", + "explanations across all scenarios mentioning Part B: 37; any adding a Part B premium as an expense: 0" + ], + "scratch_dir": "/private/tmp/claude-501/pb-partb-verify" + }, + { + "key": "completeness", + "verdict": "confirmed_with_issues", + "summary": "The audit's core finding holds. I reimplemented the check independently on policyengine-us 2.15.17 with latest_final. It reproduced all 1,984 baseline outputs and impact weights exactly, and under no_part_b it changed exactly two outputs by any amount (>1e-6): scenario_114 federal (10,729.61 -> 11,265.27) and VA state (3,514.15 -> 3,654.15). So the $1 tolerance hides nothing.\n\nNo channel the readings leave unzeroed reaches an output:\n- Zeroing base_part_b_premium or gross_medicare_part_b_premium moves the same two outputs.\n- Setting medicare_quarters_of_coverage to 0 or 30 moves no output.\n- Part A premium, Part D surcharge, SPM premiums, MSP Part A, msp_benefit_value and il_smib_person are not on any output's computation path.\n- No premium variable is computed outside 2026 on that path.\n- Setting months_receiving_social_security_disability to 24 moves only the five head_medicare_eligible outputs (007/073/074/079/084), which are already excluded. That holds even though four of those heads are then charged the premium, and crossed with no_part_b or not_enrolled.\n\nThe loader and verifier accept the proposed records in every combination except #191 + VA + conditional federal, which is refused as a duplicate, as designed. Every number in the records' alternative_reading and note text checks out.\n\nTwo should_fix issues:\n1. The drafted records and the README leave out the prompt rule \"Assume tax filing and program take-up when required\", which supports the engine's assumed Medicare enrollment.\n2. The README says the stale impact weights have \"no effect on scores\". That is true for the analyze payload, but the manifest-pinned legacy impact summary written by freeze_snapshot.py uses the row-level weights. With 2.15.17's weights every model's mean_impact_score moves and 10 of 46 change rank.\n\nItem 5 is confirmed: build_references_latest.py rewrites only the value column, and the published impact_weight column is identical to the v1.1 (1.755.4) bundle's on all 1,984 rows.", + "findings": [ + { + "severity": "should_fix", + "claim": "Both drafted records' alternative_reading and the README's prompts section cite only the rules that favour the alternative reading. They leave out the TASK_PREFACE rule \"Assume tax filing and program take-up when required\", which supports the engine's reading that every eligible person is enrolled (takes_up_medicare_if_eligible = True). They also leave out the rule that unlisted status inputs are false, which supports not_enrolled.", + "evidence": "policybench/prompts.py lines 19-22 (TASK_PREFACE) reads: \"Treat any unlisted numeric input as 0 and any other unlisted household fact, boolean, or status input as false. Assume tax filing and program take-up when required. Do not infer unlisted income, expenses, assets, benefit receipt, rent, or health coverage.\" git log -L shows TASK_PREFACE last changed 2026-05-14, so this text was in force for the June run. grep finds no \"program take-up\" anywhere in reference_audit/2026-10-05-medicare-part-b. The exclusion still stands: the ambiguity runs both ways, which is what reference_depends_on_unlisted_input covers. But the record should state both sides." + }, + { + "severity": "should_fix", + "claim": "The README's related finding \"Stale impact weights, with no effect on scores\" is too broad. Row-level impact_weight values feed a manifest-pinned snapshot artifact (the legacy impact summary). With 2.15.17's weights that artifact changes for every model and reorders 10 of 46.", + "evidence": "scripts/freeze_snapshot.py household_impact_summary_by_model (lines 742-840) uses abs(impact_weight) as the row's absolute value. It is called at line 915 and writes analysis/impact_summary_by_model.csv and us_impact_summary_by_model.csv, both pinned in paper/snapshot/20260501/manifest.json (lines 70, 222) and checked by tests/test_snapshot_artifacts.py lines 1031-1052. I recomputed it on the scored reference with the sweep's baseline_impact_weight, which changes 87 scored rows (90 minus 3 excluded). mean_impact_score rises by +0.001212 (gpt-6-luna) to +0.012790 (kimi-k2.6), and 10 of 46 models change rank (e.g. gpt-5.6-sol 6->7, claude-sonnet-5.5 7->6). The frozen artifact reproduces to 1.1e-16. leaderboard_impact.py --weights-check compares only the analyze payload (modelStats, programStats, heatmap, globalWeights, failureModes), so it cannot see this file. Script: /private/tmp/claude-501/pb-partb-verify/impact_summary_check.py." + }, + { + "severity": "note", + "claim": "Item 5 confirmed: build_references_latest.py rewrites only the value column. Row-level impact_weight values matter in analysis.py only on fallback paths that the US payload never takes.", + "evidence": "build_references_latest.py: new = board.copy() (line 275). Only new.loc[idx, \"value\"] is assigned (lines 290, 297, 326). It imports _extract_person_value and _pe_variable_for_output but never an impact-weight helper. The published reference_outputs.csv impact_weight column equals the v1.1 bundle's (policyengine-us 1.755.4, results/local/newmodels/publish/.../us/reference_outputs.csv) on all 1,984 rows, while 34 values differ. head_medicare_eligible is 5,285.20 published vs 12,065.20 on 2.15.17, a gap of 6,780.00. There are 90 mismatches: 39 medicare, 37 medicaid, 8 free + 1 reduced-price school meals, 5 WIC; 3 of them are excluded. Where values enter analysis.py: _signed_contributions (lines 245-271, value*impact_weight*sign) feeds household_net_income_by_scenario (275) and bounded_global_variable_weights (369; used at 397-417 only when population weights do not cover the groups). aggregate_global_variable_weights (878; values at 900-903) has the same early return. matching_population_weight_series returns policybench/population_weights.json weights, which cover all 18 US output groups for household, aggregate and equal, so those paths are skipped. amount_accuracy_by_model (742-748) and participation_accuracy_by_model (962-970) use only impact_weight.notna()." + }, + { + "severity": "note", + "claim": "Code review of sweep_part_b.py found no bug that could hide a moved output. My independent reimplementation agrees on every baseline value and on the moved set.", + "evidence": "Baseline: 0 output and 0 weight mismatches against the audit's sweep_part_b.csv. no_part_b: 2 outputs differ by more than 1e-6, both in scenario_114, so no move between $0 and $1 is hidden. Every binary output ends in _eligible, so the round() rule covers them all. The outputs list covers all 1,984 reference rows. medicare_part_b_premium, takes_up_medicare_if_eligible and medicare_enrolled are YEAR variables, so the {2026: v} overrides apply directly. On the outputs' computation path the only non-2026 periods computed are the 2024 AGI tree (IRMAA lookback, which is 0 in all 33 Medicare households) and 2025 state/FPG in 092 and 115. No premium or medical variable is computed outside 2026, so the 2026-only override and trace filter hide nothing. The grid's liability matches #191's sweep_salt_withholding.py (state + MD county - NYC, floored at 0, CONVERGED 0.005) and converged in one pass for scenario_114. The grid omits #191's 'net' reading, but scenario_114's state_refundable_credits is 0.00, so net equals liability. #191's own readings are liability, net and zero; 'zero_no_local_sales' appears only in its record note." + }, + { + "severity": "note", + "claim": "No other channel that reads enrollment or the premium reaches an output.", + "evidence": "Extra readings across all 100 households (/private/tmp/claude-501/pb-partb-verify/extra_sweep.json):\n- base_part_b_premium = 0 and gross_medicare_part_b_premium = 0: each moves only the same two scenario_114 outputs, plus the 39 medicare_cost impact weights.\n- medicare_quarters_of_coverage = 0 or 30: 0 outputs, 39 weights.\nOn the outputs' computation path, medicare_part_a_premium, income_adjusted_part_d_premium_surcharge, spm_unit_health_insurance_premiums, msp_part_a_premium_coverage, msp_benefit_value and il_smib_person are never computed. base_part_a_premium, is_premium_free_part_a and medicare_quarters_of_coverage appear only under medicare_cost, the impact weight. Static grep agrees: il_smib_person feeds only il_smib, and msp_benefit_value feeds msp, msp_cost and msp_federal_cost. Stress tests (not readings): doubling the premium moves scenario_000 federal (-62.88) and scenario_115 AL state (-4.68); IRMAA at MAGI 1e7 moves 12 outputs in 8 households. Neither is supported by the prompt rules: an unlisted 2024 income reads as 0, and at 2026 MAGI no output moves." + }, + { + "severity": "note", + "claim": "Other unlisted Medicare inputs: months_receiving_social_security_disability, crossed with the Part B channel, moves nothing beyond the 5 outputs already excluded. medicare_quarters_of_coverage reaches no output. I recommend the audit record these sweeps, but they call for no new exclusion.", + "evidence": "Six SSDI recipients: 007 ID, 073 MI, 074 LA, 079 AZ and 084 NC (under 65), and 027 CT (68, already eligible). With months = 24 they become eligible and enrolled. The 2,434.80 premium is charged to the heads of 007, 073, 074 and 084; MSP covers 079's head. Only head_medicare_eligible moves 0 -> 1 in 007/073/074/079/084, all already excluded for this input. The same holds with no_part_b or not_enrolled added. months = 12 moves nothing. medicare_quarters_of_coverage (default 40) moves only medicare_cost weights. medicare_part_b_premiums_reported has no consumer in 2.15.17. ESRD/ALS entitlement has no engine input." + }, + { + "severity": "note", + "claim": "The loader and verifier accept the proposed records, and the README's pinned counts check out.", + "evidence": "load_reference_exclusions + verify_exclusions_against_reference + split_reference on copies in scratch:\n- VA only: OK, 57 exclusions, 29 unlisted, 40 households, 1,927 scored.\n- Conditional federal only: OK, 57 exclusions.\n- Both: OK, 58 exclusions, 30 unlisted, 40 households, 1,926 scored.\n- #191's 3 records: OK, 59 exclusions, 31 unlisted, 1,925 scored.\n- #191 + VA: OK, 60 exclusions, 32 unlisted, 40 households, 1,924 scored.\n- #191 + VA + conditional: ReferenceExclusionError, duplicate ('scenario_114', 'federal_income_tax_before_refundable_credits'), as the README expects.\nfrozen_value matches the CSV bit-for-bit: 3514.14501953125 and 10729.6123046875. Frozen counts: 56 exclusions, 28 unlisted, 39 households, 4 records on 2.15.17, 69 adjudications. scenario_114 has 45 llm_error rows per output. The drafted records lack judge_verdict, as do 44 of the 56 frozen records; the loader does not require it." + }, + { + "severity": "note", + "claim": "One README wording slip that changes no conclusion: scenario_013's SNAP is not the $24 monthly minimum all year.", + "evidence": "Monthly SNAP for 013 (AZ) is 0.00 in January and February and 24.00 from March to December, $240.00 a year, unchanged under no_part_b. 027 and 108 are 24.00 every month ($288.00). The README says all three pay \"the $24 monthly minimum either way\"." + } + ], + "numbers_checked": [ + "base premium parameter 2026-01-01: 202.9/month; x12 = 2,434.80 (base_part_b_premium 2434.800048828125)", + "scenario_114 age 69; other_medical_expenses 10,000.00; OTC 2,000.00 excluded (itemized_medical_expenses 12,434.80 = 10,000 + 2,434.80); prompt has no premium line", + "AGI 106,572.56; 7.5% floor 7,992.94; medical_expense_deduction 4,441.86 with premium, 2,007.06 without", + "federal 10,729.61 -> 11,265.27 (+535.66 = 2,434.80 x 0.22); VA 3,514.15 -> 3,654.15 (+140.00 = 2,434.80 x 0.0575)", + "grid federal: modeled 10,729.61 / 11,137.31 / 11,758.09 / 11,783.48; no premium 11,265.27 / 11,642.16 / 11,969.80 / 11,969.80", + "grid VA: modeled 3,514.15 in all four; no premium 3,654.15 / 3,654.15 / 4,127.67 / 4,127.67; itemizes 0 only in no_part_b|zero and zero_no_local_sales", + "not_enrolled = no_part_b in every grid cell; not_enrolled_direct vs not_enrolled differences: 0", + "IRMAA single 2026 first threshold 109,001 > MAGI 106,572.56; irmaa reading gross premium 2,434.80; medicare_irmaa_magi_two_years_prior 0.00 in all 33 Medicare households", + "39 eligible people (all age >= 65) in 33 households; 35 charged in 30 households; MSP covers 4 (001 head and spouse, 043 head, 118 head)", + "1,984 outputs; 1,928 scored; 0 scored reference mismatches; 19 excluded outputs differ from frozen value", + "model answers: VA within $1 of 3,654.15: gpt-6-astra 3,654.16, gpt-6.1-sol 3,654.66; none within $1 of 3,514.15 or 4,127.67", + "federal within $1: 11,642.16 -> gpt-6-astra 11,642.20, gpt-6.1-sol 11,642.09; 11,969.80 -> kimi-k3 11,969.85, gpt-5.6-sol 11,970.00; none within $1 of 10,729.61, 11,265.27, 11,137.31, 11,758.09 or 11,783.48", + "#191 record for scenario_114 federal: frozen 10,729.6123046875, alternative 11,137.306640625; its note quotes 11,265.27, 11,642.16, 11,969.80", + "household categories: below floor 7 (000, 022, 033, 042, 055, 120, 122; 022 and 120 itemize), standard deduction both ways 21, itemizes only with premium 1 (072), itemizes both ways 1 (114), MSP 3", + "state-prefixed variables move in 14 households incl. 114 (13 others); nj_main_income_tax +26.35 in 095; AL 115 state stays 4.68", + "SNAP: 30/30 charged households move snap_excess_medical_expense_deduction; 27 at 0.00 both ways; 027 and 108 288.00, 013 240.00 (0 in Jan-Feb)", + "impact weights: 90 mismatches (39 Medicare, 37 Medicaid, 9 school meals, 5 WIC; 3 excluded); head_medicare_eligible 5,285.20 vs 12,065.20, gap 6,780.00; published weights = v1.1 bundle weights on all 1,984 rows", + "irmaa_from_2026_income weight moves: 022 12,065.20 -> 11,090.80; 055 -> 9,630.40; 120 -> 6,709.60; no output moves", + "legacy mean_impact_score with 2.15.17 weights: +0.001212 to +0.012790; 10 of 46 rank changes", + "README exclusion counts: 56->58 / 59->60; unlisted 28->30 / 31->32; households 39->40 / 40; scored 1,928->1,926 / 1,925->1,924; 2.15.17 records 4 frozen; adjudications 69; 45 llm_error rows per scenario_114 output" + ], + "scratch_dir": "/private/tmp/claude-501/pb-partb-verify" + }, + { + "key": "prompts_answers_first_run", + "note": "the same prompt and answer check, from the run's first attempt before an account limit interrupted the other three reviewers; the resumed run re-ran it", + "verdict": "confirmed_with_issues", + "summary": "Everything in my scope checks out. Prompts: I rendered describe_household and make_no_tools_batch_prompt for all 100 frozen scenarios. Each description appears word for word in the frozen data.json.gz prompts (100/100 tool, 100/100 json). Of the full batch prompts, 82 are byte-identical; the other 18 differ only in the order of the requested variables. In git, describe_household and TASK_PREFACE are unchanged since 2026-05-14, and the one post-freeze edit to prompts.py touches only the explanation-repair prompt. No prompt states Medicare enrollment, Medicare coverage or a Part B premium amount. The word Medicare appears only in three places: (a) the label \"health insurance premiums excluding Medicare Part B: $X\" (61 lines in 53 scenarios); (b) the payroll_tax and self_employment_tax request lines (\"employee Medicare tax\", \"Additional Medicare Tax\"); (c) \"whether X is eligible for Medicare\" requests. No scenario JSON sets medicare_enrolled, takes_up_medicare_if_eligible, medicare_part_b_premium or health_insurance_premiums. Two of these names can never appear in a prompt: is_excluded_prompt_input_name returns True for medicare_enrolled (EXCLUDED_INPUT_VARIABLES) and for takes_up_medicare_if_eligible (the takes_up_ prefix). medicare_part_b_premium is a formula variable, so it is not a promptable input. Of the 30 Part-B households, 10 carry the \"excluding Medicare Part B\" line: 013, 021, 022, 033, 044, 062, 072, 085, 098, 120. In every one the line sits on the person charged the premium (12 of the 35 charged people). scenario_114 does not carry it; its prompt has no health-coverage or premium line at all. Answers: on scenario_114 VA, GPT-6 Astra (3,654.16) and GPT-6.1 Sol (3,654.66) are within $1 of 3,654.15. On federal, GPT-6.1 Sol (11,642.09) and GPT-6 Astra (11,642.20) are within $1 of 11,642.16, and Kimi K3 (11,969.85) and GPT-5.6 Sol (11,970.00) are within $1 of 11,969.80. No model is within $1 of the frozen 3,514.15 or 10,729.61, or of the standalone federal record's alternative, 11,265.27. None of the four models' eight explanations mentions Medicare, Part B, medical expenses, premiums or health, so no explanation says the premium was left out because it is unlisted. Their stated deduction figures do, however, rebuild exactly as medical expenses of $10,000 less 7.5% of AGI with no premium. No explanation among the 91,264 rows adds a Part B premium amount. Verdict: the VA record meets the rule in reference_exclusions.py. The frozen value needs Medicare enrollment and a Part B premium that the household data never carried and the prompt could not list. A careful reader can take the facts either way, and the prompt rules favour the no-Part-B reading. One should_fix: the record's alternative_reading does not answer the strongest argument for the frozen side, the prompt sentence \"Assume tax filing and program take-up when required\".", + "findings": [ + { + "severity": "note", + "claim": "The rendered prompts are the prompts the models received, and none states Medicare enrollment, Medicare coverage or a Part B premium amount.", + "evidence": "describe_household output for all 100 scenarios appears verbatim in data.json.gz scenarios[*].prompt.tool and .json (100/100 each). The 18 full batch prompts that differ from the payload differ only in requested-variable order (diff on scenario_008 shows only the school-meal lines moved). git log: prompts.py's only post-freeze commit, 8f3c44cb on 2026-07-09, changes make_explanation_repair_prompt only. scenarios.py commits after 06-12 do not touch the EXCLUDED_* or PROMPTABLE sets. Prompt lines matching medicare|premium|coverage|insurance|enroll: 'has employer-sponsored insurance' (92); 'health insurance premiums excluding Medicare Part B: $N' (61 in 53 scenarios); 'other health insurance premiums: $N' (56); 'employer sponsored insurance premiums: $N' (49); 'fsla overtime premium' (11); 'is enrolled at least half time for american opportunity credit' (4, education); 'has indian health service coverage at interview' (3), 'has other means tested health coverage at interview' (1), 'has champva health coverage at interview' (1). TASK_PREFACE mentions only 'health coverage' (twice). Request lines mention 'employee Medicare tax', 'Additional Medicare Tax' and 'whether X is eligible for Medicare (1 if yes, 0 if no)'." + }, + { + "severity": "note", + "claim": "No scenario sets any of the four Medicare/premium inputs, and the household data could not have put the enrollment flags into a prompt.", + "evidence": "Walking every scenario_json key found these names zero times: medicare_enrolled, takes_up_medicare_if_eligible, medicare_part_b_premium, health_insurance_premiums. is_excluded_prompt_input_name gives True for medicare_enrolled (in EXCLUDED_INPUT_VARIABLES) and for takes_up_medicare_if_eligible (EXCLUDED_INPUT_PREFIXES 'takes_up_'). medicare_part_b_premium has a formula (max_(gross - msp_coverage, 0)), so _is_promptable_input_variable rejects it. Code read in 2.15.17: medical_expense_health_insurance_premiums = where(health_insurance_premiums != 0, direct, health_insurance_premiums_without_medicare_part_b + medicare_part_b_premium * medicare_enrolled); medicare_enrolled defined_for is_medicare_eligible, adds takes_up_medicare_if_eligible; takes_up_medicare_if_eligible default_value = True; base_premium 2026-01-01 is 202.9. DEFAULT_TAKEUP_INPUTS in scenarios.py has no Medicare entry, so the True default comes from the engine, not from the benchmark." + }, + { + "severity": "note", + "claim": "10 of the 30 Part-B households carry the 'health insurance premiums excluding Medicare Part B' line, always on the charged person; scenario_114 does not carry it.", + "evidence": "Carrying the line: scenario_013 head $298, 021 head $210, 022 head $2,416, 033 head $220 and spouse $180, 044 head $3,600, 062 head $4,000, 072 head and spouse $16,000 each, 085 head $300, 098 head $336, 120 head $960. That is 12 of the 35 charged people (all charged $2,434.80). For these people the engine stacks Part B on top of the listed amount: scenario_013 head medical_expense_health_insurance_premiums is 2,732.80 = 298 + 2,434.80. scenario_001 carries the line too, but MSP covers its premiums. The full scenario_114 prompt has no premium, insurance or coverage line. It lists age 69, Social Security retirement income $23,328, other medical expenses $10,000 and over-the-counter health expenses $2,000." + }, + { + "severity": "note", + "claim": "No model answered within $1 of either frozen value. Two models hit the VA alternative exactly, and four hit federal no-Part-B grid values (none hit the standalone record's 11,265.27).", + "evidence": "VA (46 rows; kimi-k2.6 NaN): gpt-6-astra 3,654.16 (+0.01) and gpt-6.1-sol 3,654.66 (+0.51) against 3,654.15. The answers closest to frozen 3,514.15 are grok-4.6 3,509.00 (-5.15), deepseek-v4-pro-0813 3,508.52 (-5.63) and claude-fable-5.1 3,524.00 (+9.85). Federal: gpt-6.1-sol 11,642.09 and gpt-6-astra 11,642.20 against no_part_b|liability 11,642.16; kimi-k3 11,969.85 and gpt-5.6-sol 11,970.00 against no_part_b|zero 11,969.80. The answers closest to frozen 10,729.61 are claude-haiku-4.5 10,268 (-461.61) and claude-opus-4.8 11,210 (+480.39). Nothing is within $1 of 11,265.27, 11,137.31, 11,758.09 or 11,783.48. Kimi K3's VA answer is 4,134.58 and GPT-5.6 Sol's is 4,174.00; neither is within $1 of 4,127.67 or any other grid value." + }, + { + "severity": "note", + "claim": "None of the four matching models says anything about Medicare, Part B or medical expenses. Their numbers are consistent with leaving the premium out, but no explanation gives a reason.", + "evidence": "A regex for medic|premium|part b|health|insur|OTC over the 8 explanations finds 0 hits. Excerpts: Astra federal: 'estimated itemized deductions are $20,639.33 after the charitable floor, the senior deduction is $4,105.63'. Astra VA: 'Estimated allowable itemized deductions are $16,985.18 and personal and age exemptions total $1,730.' GPT-6.1 Sol federal: 'After itemized deductions, the phased-down senior deduction and a $4,872.40 qualified-business-income deduction, applying the 2026 brackets produces this tax'. GPT-6.1 Sol VA: 'Using $16,985.18 of itemized deductions and $1,730 of personal and age exemptions yields this tax'. Kimi K3 federal: 'standard deduction 18,150 (16,100 single + 2,050 age-65 addition, exceeding itemized deductions of 17,518)'. Kimi K3 VA: 'standard deduction 8,500 (consistency with federal non-itemizing)'. GPT-5.6 Sol federal: 'the 2026 standard and age deductions, senior deduction, non-itemizer charitable deduction, and QBI deduction produce taxable income of about 78445'. GPT-5.6 Sol VA: 'after the 8750 standard deduction and 930 personal exemption, taxable income is about 77064'. Rebuilding from AGI 106,572.80: medical is 10,000 - 7,992.96 = 2,007.04 without Part B and 4,441.84 with it. Astra's federal itemized = 2,007.04 + charity 10,914.14 + SALT (4,064 + 3,654.16) = 20,639.34 (stated 20,639.33; with Part B it would be 23,074.14). Astra and GPT-6.1 Sol VA itemized = 16,985.18 exactly (with Part B 19,419.98). Kimi's 17,518 = 2,007.04 + 11,447 + 4,064 = 17,518.04. With Part B that total would be 19,952.84, above the 18,150 standard deduction, so Kimi would have itemized. GPT-5.6 Sol gives no itemized figure, so its explanation cannot show whether it considered medical expenses." + }, + { + "severity": "note", + "claim": "No explanation in any of the 91,264 prediction rows adds a Part B premium amount. A few models read the Part B label as a sign of Medicare status.", + "evidence": "39 rows mention 'Part B' or 'Medicare B'. Most use the prompt label or Massachusetts 'Part B income'. The rest: claude-sonnet-5 scenario_072 head_chip_eligible answered 0 (ref 0): 'is already enrolled in Medicare Part B and other coverage', with no amount. inkling scenario_073 head_medicare_eligible answered 1 (ref 0): 'the presence of Medicare Part B premium exclusions in the health premiums, he meets Medicare disability eligibility rules'. claude-sonnet-4.6 scenario_073 answered 0: 'no Medicare Part B premiums listed'. glm-5.2 scenario_021 snap answered 0 (ref 0): 'plus Medicare premiums and other deductions', no amount. glm-5.2 scenario_003 federal answered 49,523.45: 'Medicare B premium deduction 0'. In scenario_114, 0 of 46 models mention Part B or a Medicare premium on any output. Ten models reach a larger medical deduction (about $4,007) by counting the $2,000 of over-the-counter expenses, not Part B; the engine's itemized_medical_expenses there is 12,434.80 = 10,000 + 2,434.80, with no OTC." + }, + { + "severity": "should_fix", + "claim": "The VA record's alternative_reading cites only the rules that favour the alternative. It should also name and answer the strongest textual argument for the frozen reading: TASK_PREFACE's 'Assume tax filing and program take-up when required'.", + "evidence": "proposed_exclusions.json alternative_reading cites 'treat any unlisted numeric input as 0' and 'not to infer unlisted expenses or health coverage', but does not mention the take-up sentence or the head's stated Social Security retirement income ($23,328). The frozen side: a law-literate reader could treat Medicare as a program whose take-up the prompt says to assume. Under general Medicare enrollment rules (not checked against statute text this session), Social Security recipients turning 65 are enrolled in Part B automatically, with the premium deducted from the benefit. And the $202.90 premium is set by law, not a household input. The rebuttal, from code read this session: the specific rule 'Do not infer unlisted ... expenses ... or health coverage' governs over the general take-up sentence. The benchmark's own encoding of take-up, DEFAULT_TAKEUP_INPUTS (Medicaid, SSI, ACA, DC PTC, EITC, SNAP, filing), has no Medicare entry; enrollment comes from the engine's takes_up_medicare_if_eligible default_value=True. And the Medicare request asks only about eligibility. Adding a sentence on this would make the 'careful reader could take the stated facts the other way' finding self-contained." + }, + { + "severity": "note", + "claim": "Verdict on the exclusion test: the VA record meets the rule.", + "evidence": "The docstring scores an output only when its reference follows from the facts the prompt states. The frozen 3,514.15 needs medicare_enrolled=True (from the engine's take-up default) and the modeled medicare_part_b_premium (2,434.80) counted as a medical expense. The household data never set these, and the prompt could not list them: two are hard-excluded from prompts by name or prefix, and the third is a formula variable. Arguments for the frozen side: the program take-up sentence, Social Security receipt at 69, automatic Part B enrollment, and a premium fixed by law. Arguments for the alternative: the unlisted-numeric-as-0 rule, the rule against inferring expenses or health coverage, the benchmark leaving Medicare out of its take-up list, and premiums elsewhere appearing only as explicit lines. So both readings are available, which is the case the reason code covers, and the prompt rules favour the alternative. That makes symmetric exclusion the right tool, rather than re-keying the reference or calling it an engine defect (the engine is modelling a plausible real-world default, not misapplying law to stated facts). Model evidence agrees: 0/46 within $1 of frozen, 2/46 on the alternative, and no explanation adds Part B. The record is well-formed: merged with the frozen run's 56 exclusions (none for scenario_114), load_reference_exclusions plus verify_exclusions_against_reference accept it (57 entries, frozen_value matches reference 3,514.14501953125), and adding the conditional federal record also validates (58)." + }, + { + "severity": "note", + "claim": "The 'health insurance premiums excluding Medicare Part B' label is a prompt-wording hazard outside this record. It names Part B in 61 lines and some models read it as evidence of Medicare status.", + "evidence": "In scenario_073, inkling inferred Medicare eligibility from the label (answered 1, ref 0), and in scenario_072 claude-sonnet-5 said the head 'is already enrolled in Medicare Part B'. In the 10 Part-B households carrying the label, a reader could take it either way: Part B paid but excluded from the figure (the engine's stacking), or Part B unlisted and therefore 0. Per the sweep summary (read, not recomputed), no output in those households moves by more than $1, so this does not affect the proposed record. The prompt also shows identical 'other health insurance premiums' lines; in 2.15.17 that input feeds spm_unit_health_insurance_premiums only, and health_insurance_premiums_without_medicare_part_b feeds medical_expense_health_insurance_premiums, so neither is double-counted in medical expenses, but sonnet-4.6 explanations show models puzzling over the duplicate." + } + ], + "numbers_checked": [ + "100/100 scenario descriptions appear word for word in data.json.gz prompt.tool and prompt.json; 82/100 full batch prompts byte-identical, 18 differ only in requested-variable order; scenario_114's tool prompt is identical", + "Prompt line counts: 'health insurance premiums excluding Medicare Part B' 61 lines in 53 scenarios; 'other health insurance premiums' 56; 'employer sponsored insurance premiums' 49; 'has employer-sponsored insurance' 92", + "Scenarios setting medicare_enrolled / takes_up_medicare_if_eligible / medicare_part_b_premium / health_insurance_premiums: 0 / 0 / 0 / 0", + "is_excluded_prompt_input_name: medicare_enrolled True, takes_up_medicare_if_eligible True, medicare_part_b_premium False (but it is a formula variable), health_insurance_premiums False", + "Part-B households carrying the line: 10 of 30 (013, 021, 022, 033, 044, 062, 072, 085, 098, 120); 12 of 35 charged people; scenario_114 does not carry it", + "39 Medicare-eligible people, 35 charged $2,434.80 each in 30 households, 4 covered by MSP (001 head and spouse, 043 head, 118 head)", + "2026 base_premium parameter 202.9 x 12 = 2,434.80", + "scenario_114 VA: gpt-6-astra 3,654.16 (+0.01), gpt-6.1-sol 3,654.66 (+0.51) vs 3,654.15; closest to frozen 3,514.15: grok-4.6 3,509.00 (-5.15), deepseek-v4-pro-0813 3,508.52 (-5.63), claude-fable-5.1 3,524.00 (+9.85)", + "scenario_114 federal: gpt-6.1-sol 11,642.09, gpt-6-astra 11,642.20 vs 11,642.16; kimi-k3 11,969.85, gpt-5.6-sol 11,970.00 vs 11,969.80; none within $1 of 11,265.27 or 10,729.61 (closest to frozen: claude-haiku-4.5 10,268.00, -461.61)", + "kimi-k3 VA 4,134.58 and gpt-5.6-sol VA 4,174.00: not within $1 of 4,127.67 (no_part_b|zero)", + "Medical deduction at AGI 106,572.80: 2,007.04 without Part B, 4,441.84 with it; engine values 2,007.06 and 4,441.86 at AGI 106,572.56", + "Astra federal itemized rebuilt without Part B: 20,639.34 (stated 20,639.33); with Part B it would be 23,074.14", + "Astra and GPT-6.1 Sol VA itemized rebuilt without Part B: 16,985.18 (stated 16,985.18); with Part B 19,419.98", + "Kimi itemized rebuilt without Part B: 17,518.04 (stated 17,518); with Part B 19,952.84, which exceeds the 18,150 standard deduction", + "Kimi and GPT-5.6 Sol taxable income 106,572.80 - 18,150 - 4,105.63 - 1,000 - 4,872.40 = 78,444.77", + "Engine deltas: federal 535.66 = 2,434.80 x 0.22 (535.656); VA 140.00 = 2,434.80 x 0.0575 (140.001), consistent with those marginal rates", + "Merged exclusions validate: 56 frozen + VA = 57; + conditional federal = 58; frozen_value 3,514.14501953125 matches reference_outputs.csv", + "Sweep summary (read, not recomputed): 267 SNAP/eligibility rows in Medicare households identical across all five readings", + "Explanation scan: 91,264 rows, 0 add a Part B premium amount; 0 of 46 scenario_114 models mention Part B or a Medicare premium" + ], + "scratch_dir": "/private/tmp/claude-501/pb-partb-verify/prompts-answers" + } + ] +} diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/inputs/pr191_proposed_exclusions.json b/reference_audit/2026-10-05-medicare-part-b/verification/inputs/pr191_proposed_exclusions.json new file mode 100644 index 00000000..0493ec95 --- /dev/null +++ b/reference_audit/2026-10-05-medicare-part-b/verification/inputs/pr191_proposed_exclusions.json @@ -0,0 +1,74 @@ +{ + "schema_version": 1, + "status": "proposed 2026-10-05; changes published scores, so it waits for Max's ruling (cos decision). decided_on is the draft date; a release sets it to the ruling's date.", + "basis": "policybench/reference_exclusions.py: an output is excluded for every model when its reference depends on an engine input the prompt never listed and a careful reader could take the stated facts the other way.", + "exclusions": [ + { + "scenario_id": "scenario_022", + "variable": "federal_income_tax_before_refundable_credits", + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": "The prompt lists no state income tax withheld or paid during 2026. The reference fills the state income tax part of the federal state and local tax deduction (26 U.S.C. 164(a)(3), (b)(5)) with policyengine-us's formula estimate of California income tax withheld: California's single rate schedule applied to the person's whole federal AGI (including $17,624.75 of taxable Social Security, which California exempts) less California's single standard deduction, at California's 2025 amounts (ca_withheld_income_tax, under the c_ca_hold_2025 convention), $6,058.85. The same engine computes the household's California income tax before refundable credits as $2,505.87. Read as the household paying its 2026 California income tax during the year, the deduction takes that liability and the output is the alternative value. The prompt's rule that unlisted numeric inputs are 0 gives a third reading, no state income tax paid, under which SALT takes the general sales tax (the note gives that value).", + "frozen_value": 11113.5703125, + "alternative_value": 11895.2275390625, + "engine_version": "policyengine-us 2.15.17", + "decided_on": "2026-10-05", + "decided_by": "developer", + "unlisted_input": "state income tax withheld or paid during 2026, which the federal state and local tax deduction takes as its state income tax part; policyengine-us fills it with state_withheld_income_tax, a formula estimate on federal AGI that the household data never set", + "note": "Found 2026-10-05 by sweeping every reference with the state income tax in SALT read three ways (reference_audit/2026-10-05/scripts/sweep_salt_withholding.py, on policyengine-us 2.15.17 with latest_final; its baseline reproduces all 1,928 scored references, and an independent re-implementation agreed on all 1,984 outputs). SALT is $14,887.85 in the reference and $11,334.87 under the alternative; the household itemizes under both, and the $40,400 cap binds under neither. Under the zero reading SALT takes the general sales tax, $1,155.60, and the output is $12,192.29; that sales tax includes the engine's local component of $192.60 (20% of the state table amount, an estimate for an unlisted locality), and without it the output is $12,234.66. The alternative uses the engine's California liability ($2,505.87), which this household's state output carries as an engine-defect exclusion; with the corrected liability ($1,968.81, recorded on policyengine-us 1.755.4) the output is $12,013.38. The output also moves under an engine defect: policyengine-us 2.15.17 leaves the household's California SDI contributions ($684.26) out of SALT, though they are deductible state income taxes (Trujillo v. Commissioner, 68 T.C. 670 (1977)). With them the output is $10,963.04 under the withholding estimate and $11,744.69 under the liability." + }, + { + "scenario_id": "scenario_081", + "variable": "federal_income_tax_before_refundable_credits", + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": "The prompt lists no state income tax withheld or paid during 2026. The reference fills the state income tax part of the federal state and local tax deduction (26 U.S.C. 164(a)(3), (b)(5)) with policyengine-us's formula estimate of Massachusetts income tax withheld: 5% of the person's whole federal AGI above the $4,400 single personal exemption (ma_withheld_income_tax), $8,484.41. The same engine computes the household's Massachusetts income tax before refundable credits as $8,238.41. Read as the household paying its 2026 Massachusetts income tax during the year, the deduction takes that liability and the output is the alternative value. The prompt's rule that unlisted numeric inputs are 0 gives a third reading, no state income tax paid, under which SALT takes the general sales tax (the note gives that value).", + "frozen_value": 26932.2734375, + "alternative_value": 26991.3125, + "engine_version": "policyengine-us 2.15.17", + "decided_on": "2026-10-05", + "decided_by": "developer", + "unlisted_input": "state income tax withheld or paid during 2026, which the federal state and local tax deduction takes as its state income tax part; policyengine-us fills it with state_withheld_income_tax, a formula estimate on federal AGI that the household data never set", + "note": "Found 2026-10-05 by sweeping every reference with the state income tax in SALT read three ways (reference_audit/2026-10-05/scripts/sweep_salt_withholding.py, on policyengine-us 2.15.17 with latest_final; its baseline reproduces all 1,928 scored references, and an independent re-implementation agreed on all 1,984 outputs). SALT is $14,127.41 in the reference and $13,881.41 under the alternative; the household itemizes under both, and the $40,400 cap binds under neither. Under the zero reading SALT takes the general sales tax, $853.00, and the output is $28,763.81. The alternative uses the engine's Massachusetts liability ($8,238.41), which this household's state output carries as an engine-defect exclusion; with the corrected liability ($8,232.90) the output is $26,992.64. The output also moves under an engine defect: policyengine-us 2.15.17 leaves the household's Massachusetts paid family and medical leave contributions ($805.01) out of SALT, though they are deductible state income taxes (Rev. Rul. 2025-4). With them the output is $26,739.07 under the withholding estimate and $26,798.11 under the liability." + }, + { + "scenario_id": "scenario_114", + "variable": "federal_income_tax_before_refundable_credits", + "reason_code": "reference_depends_on_unlisted_input", + "alternative_reading": "The prompt lists no state income tax withheld or paid during 2026. The reference fills the state income tax part of the federal state and local tax deduction (26 U.S.C. 164(a)(3), (b)(5)) with policyengine-us's formula estimate of Virginia income tax withheld: Virginia's rate schedule applied to the person's whole federal AGI (including $19,828.80 of taxable Social Security, which Virginia exempts) less Virginia's single standard deduction (va_withheld_income_tax), $5,367.30. The same engine computes the household's Virginia income tax before refundable credits as $3,514.15. Read as the household paying its 2026 Virginia income tax during the year, the deduction takes that liability and the output is the alternative value. The prompt's rule that unlisted numeric inputs are 0 gives a third reading, no state income tax paid, under which SALT takes the general sales tax (the note gives that value).", + "frozen_value": 10729.6123046875, + "alternative_value": 11137.306640625, + "engine_version": "policyengine-us 2.15.17", + "decided_on": "2026-10-05", + "decided_by": "developer", + "unlisted_input": "state income tax withheld or paid during 2026, which the federal state and local tax deduction takes as its state income tax part; policyengine-us fills it with state_withheld_income_tax, a formula estimate on federal AGI that the household data never set", + "note": "Found 2026-10-05 by sweeping every reference with the state income tax in SALT read three ways (reference_audit/2026-10-05/scripts/sweep_salt_withholding.py, on policyengine-us 2.15.17 with latest_final; its baseline reproduces all 1,928 scored references, and an independent re-implementation agreed on all 1,984 outputs). SALT is $9,431.59 in the reference and $7,578.44 under the alternative; the household itemizes under both, and the $40,400 cap binds under neither. Under the zero reading SALT takes the general sales tax, $692.40, and the output is $11,758.09; that sales tax includes the engine's local component of $115.40 (20% of the state table amount, an estimate for an unlisted locality), and without it the output is $11,783.48. The reference's medical deduction also includes a modeled Medicare Part B premium of $2,434.80, which the prompt does not list. Without it the output is $11,265.27 under the withholding estimate and $11,642.16 under the liability, and with no withholding, local sales tax or Part B premium it is $11,969.80 (the household then takes the standard deduction)." + } + ], + "already_excluded": [ + { + "scenario_id": "scenario_078", + "variable": "federal_income_tax_before_refundable_credits", + "existing_reason_code": "reference_depends_on_unlisted_input", + "existing_basis": "whether the prior-year deduction of the refunded state and local tax reduced federal tax (prior-year itemization, the income-versus-sales-tax election, SALT-cap headroom)", + "reference": 24164.45703125, + "engine_value_on_2_15_17": 24164.45703125, + "liability_reading_value": 24799.1640625, + "zero_reading_value": 27290.697265625, + "note_to_append": "The output also moves under the state income tax withheld reading (unlisted input): the reference's SALT uses the Maryland withholding estimate ($15,791.66); with the engine's own liability ($11,295.39) the 2.15.17 value is $24,799.16, and with no state income tax paid it is $27,290.70. The $40,400 cap that binds in the reference does not bind under the liability, so the liability value depends on the county, which the prompt does not state: $24,705.95 to $25,068.44 across Maryland's 24 county rates (the engine's default, Allegany, gives $24,799.16)." + }, + { + "scenario_id": "scenario_120", + "variable": "federal_income_tax_before_refundable_credits", + "existing_reason_code": "reference_engine_defect", + "existing_basis": "r02_ira_219g", + "reference": 40021.81640625, + "engine_value_on_2_15_17": 40416.66796875, + "liability_reading_value": 40438.953125, + "zero_reading_value": 43619.93359375, + "note_to_append": "The output also moves under the state income tax withheld reading (unlisted input): the reference's SALT uses the Connecticut withholding estimate ($12,010.05); with the engine's own liability ($11,917.18) the 2.15.17 value is $40,438.95, and with no state income tax paid it is $43,619.93. With r02's IRA fix on 2.15.17 the value is $40,423.18 under the withholding estimate, $40,445.46 under the liability and $43,629.21 with no state income tax paid. policyengine-us 2.15.17 also leaves the household's Connecticut paid leave contributions ($827.98) out of SALT (Rev. Rul. 2025-4); with them the 2.15.17 value is $40,217.95." + } + ], + "readings_moving_no_further_scored_output": [ + "net", + "zero" + ] +} diff --git a/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json index cfff4bde..3e2c9692 100644 --- a/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json +++ b/reference_audit/2026-10-05-medicare-part-b/verification/leaderboard_impact.json @@ -1,5 +1,6 @@ { "published_reproduced": true, + "legacy_impact_summary_reproduced": true, "cases": [ { "case": "part_b", @@ -80,7 +81,24 @@ "n_case": 3910, "n_delta": -46 } - ] + ], + "legacy_impact_summary": { + "mean_impact_score_delta": { + "min": 0.00017757408000151909, + "max": 0.005036894110105661 + }, + "models_changing_rank": 2, + "rank_changes": { + "claude-sonnet-4.6": { + "published": 24, + "case": 25 + }, + "claude-opus-4.7": { + "published": 25, + "case": 24 + } + } + } }, { "case": "salt", @@ -200,7 +218,24 @@ "n_case": 3634, "n_delta": -138 } - ] + ], + "legacy_impact_summary": { + "mean_impact_score_delta": { + "min": -0.0009745995548785524, + "max": 0.0063318981082231 + }, + "models_changing_rank": 2, + "rank_changes": { + "claude-sonnet-4.6": { + "published": 24, + "case": 25 + }, + "claude-opus-4.7": { + "published": 25, + "case": 24 + } + } + } }, { "case": "salt_and_part_b", @@ -341,7 +376,24 @@ "n_case": 3910, "n_delta": -46 } - ] + ], + "legacy_impact_summary": { + "mean_impact_score_delta": { + "min": 0.0008573346344286081, + "max": 0.008853340075352323 + }, + "models_changing_rank": 2, + "rank_changes": { + "claude-sonnet-4.6": { + "published": 24, + "case": 25 + }, + "claude-opus-4.7": { + "published": 25, + "case": 24 + } + } + } } ], "virginia_record_on_top_of_salt": { @@ -353,6 +405,55 @@ }, "impact_weights_check": { "impact_weights_replaced": 90, - "payload_differences": [] + "payload_differences": [], + "legacy_impact_summary": { + "mean_impact_score_delta": { + "min": 0.0012115934103250625, + "max": 0.012790050549897125 + }, + "models_changing_rank": 10, + "rank_changes": { + "gpt-5.6-sol": { + "published": 6, + "case": 7 + }, + "claude-sonnet-5.5": { + "published": 7, + "case": 6 + }, + "deepseek-v4.1-flash": { + "published": 12, + "case": 13 + }, + "gpt-5.6-terra": { + "published": 13, + "case": 12 + }, + "gemini-3.6-flash": { + "published": 16, + "case": 17 + }, + "ox-alpha": { + "published": 17, + "case": 16 + }, + "claude-opus-4.7": { + "published": 25, + "case": 26 + }, + "claude-fable-5": { + "published": 26, + "case": 25 + }, + "qwen-3.7-max": { + "published": 37, + "case": 38 + }, + "grok-4.3": { + "published": 38, + "case": 37 + } + } + } } } From 65ca4af9d1395d61d06f9d106bf134b81c6b52dd Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Mon, 5 Oct 2026 14:20:57 -0400 Subject: [PATCH 3/3] Point the Part B audit's annotation findings to #197 and the scenario_114 row check Co-Authored-By: Claude Opus 5.5 --- reference_audit/2026-10-05-medicare-part-b/README.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/reference_audit/2026-10-05-medicare-part-b/README.md b/reference_audit/2026-10-05-medicare-part-b/README.md index f0ac3b56..deb60345 100644 --- a/reference_audit/2026-10-05-medicare-part-b/README.md +++ b/reference_audit/2026-10-05-medicare-part-b/README.md @@ -162,8 +162,13 @@ The legacy household impact summary (`impact_summary_by_model.csv`) moves too. I - **Enrollment changes only weights.** `not_enrolled` sets `medicare_cost`, the impact weight of the 39 Medicare eligibility outputs, to 0. It changes no output. - **scenario_031's Medicaid annotations name the wrong mechanism.** They explain the reference (eligible, through California's optional senior-or-disabled pathway) by subtracting the Part B premium from income, and 43 row annotations fault models for not doing so. - On 2.15.17, `medicaid_optional_senior_or_disabled_countable_income` subtracts no premium. It applies SSI income rules with California's $230 monthly disregard: $23,853.47 less $2,760.00 is $21,093.47, under the $22,024.80 limit (138% of the 2026 poverty guideline). - - The values are the same under every reading here, so the reference is unaffected. The annotation text needs correcting; a separate task covers it. -- **scenario_114's case notes.** The federal note lists "missing the Part B premium" among model errors. The Virginia note says the medical deduction includes the $2,000 of over-the-counter expenses, which the engine leaves out. A release adopting the records rewords both. + - The values are the same under every reading here, so the reference is unaffected. PolicyEngine/policybench#197 rewrites the case note, the reference explanation and the 43 rows to the engine's mechanism. +- **scenario_114's annotations.** The session that made #197 checked them against the engine. Its row-by-row report and suggested wording are in a comment on this PR (PolicyEngine/policybench#193). This audit checked the counts and figures below. + - **Virginia case note.** Its error group (4) blames models for "leaving out the $2,000 over-the-counter expenses or adding the 0.5%-of-AGI charitable floor". The reference itself leaves out the over-the-counter expenses and applies the floor. What the short answers miss is the premium. + - **Virginia rows.** Six rows repeat the over-the-counter claim. About five more misstate the premium's role, among them the rows for GPT-6 Astra and GPT-6.1 Sol, whose answers match the value without the premium. + - **Federal case note.** It lists "missing the Part B premium" among model errors; that error rests on the unlisted premium. It also gives the SALT state income tax as $5,367.59 where the engine has $5,367.30, and six federal rows repeat that figure. + + Excluded outputs stay in the published payload, so a release adopting the records rewords the rows as well as the notes. - **#191's README rounding.** With its records, #191 gives GPT-6 Sol's exact rate as 95.62% and the always-zero baseline as 70.40%. Its own outputs give 95.6146 and 70.3946, and this audit's #191-alone case reproduces both, so they round to 95.61% and 70.39%. ## What a release adopting the records must also do @@ -175,7 +180,7 @@ Two sets of counts follow, one if d963 adopts #191's three records and one if it - scenario_114 Virginia: 45 rows; - scenario_114 federal: 45 rows, if the standalone record is used. - Reword the two case notes above. + Reword scenario_114's two case notes and the rows that repeat their errors (see "Related findings"). 3. **Pinned counts.** Each output carries 46 model rows, and none is an exact match. | Count | With #191 adopted | Without #191 |