From 4e0cbdafd03f08214995cbab757eab1a3157f12a Mon Sep 17 00:00:00 2001
From: Owen McGirr <o.a.mcgirr@gmail.com>
Date: Sun, 4 Oct 2026 16:10:21 +0100
Subject: [PATCH 1/3] Reserve three instant and three generated prediction
 slots

---
 .github/workflows/ci.yml                      |   4 +-
 docs/six-predictions.md                       |  36 ++
 scripts/check-packaged-prediction.mjs         |   2 +-
 scripts/fetch-prediction-neural.mjs           |  28 +-
 scripts/fetch-prediction-neural.node-test.mjs |  26 +-
 scripts/measure-neural.py                     |   4 +-
 scripts/prediction-neural.json                | 327 ++++++++++--------
 scripts/probe-neural-worker.py                |  16 +-
 src-tauri/Cargo.lock                          |   6 +-
 src-tauri/Cargo.toml                          |   4 +-
 src-tauri/src/point_workflow.rs               |   2 +-
 src-tauri/src/prediction/database.rs          |  13 +-
 src-tauri/src/prediction/mod.rs               |   4 +-
 src-tauri/src/prediction/model.rs             | 104 ++++--
 src-tauri/src/prediction/worker.rs            | 164 ++++++---
 src-tauri/src/scan_keyboard.rs                | 138 ++++++--
 16 files changed, 590 insertions(+), 288 deletions(-)
 create mode 100644 docs/six-predictions.md

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index f31bede8..5844375d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -112,7 +112,9 @@ jobs:
           toolchain: 1.97.1
           components: rustfmt, clippy
       - run: npm ci
-      - name: Fetch prediction model
+      - name: Fetch prediction model and prepared companion workers
+        env:
+          GH_TOKEN: ${{ github.token }}
         run: npm run prediction-model
       - name: Diagnose prepared ARM worker
         if: runner.os == 'macOS'
diff --git a/docs/six-predictions.md b/docs/six-predictions.md
new file mode 100644
index 00000000..c403ffff
--- /dev/null
+++ b/docs/six-predictions.md
@@ -0,0 +1,36 @@
+# Six prediction slots
+
+Slots 1–3 are the statistical predictor's first three suggestions. Slots 4–6 are
+additional SmolLM2 words. Empty slots stay empty and cannot be selected in either
+scan direction. The entire displayed row stays stable until scanning leaves it.
+
+The worker retains exact displayed/latest batch tokens and validates acceptance
+against the edit revision, generation, activity epoch and foreground target.
+Capitalization, prefix replacement and trailing spaces use the existing path.
+No focused-field capture, personal learning or new settings are introduced.
+
+The companion uses protocol 2. Model and tokenizer bytes are unchanged. Startup
+remains bounded to 30 seconds and each reply to two seconds. Failure leaves the
+instant slots available, with explicit retry or keyboard reopening required.
+
+## Prepared assets
+
+The build can consume checksum-pinned companion CI archives before publication,
+using an authenticated GitHub CLI. Verified local caches work offline. These CI
+artifacts expire and are not permanent release assets. Before merging/releasing
+the desktop, separately authorize companion artifact publication and replace
+prepared artifact sources with the permanent release URLs for the same bytes.
+No runtime network access or model setup is needed.
+
+## Validation
+
+Run the repository checks and scripts/measure-neural.py --build with explicit
+--model, --worker and --output paths. The integration fixture uses synthetic
+context and fake input/activity adapters. It measures 1,000 warmed queries,
+asserts instant-slot stability, and reports fill, latency, failures and process
+tree memory. Its overall measurement bound is 40 minutes; the application reply
+deadline remains two seconds.
+
+Use switchify-prediction scripts/generation_evaluate.py for the frozen corpus
+comparison. Existing model quality regressions remain. Neither default activation
+nor a successful performance run establishes unseen-data accuracy.
diff --git a/scripts/check-packaged-prediction.mjs b/scripts/check-packaged-prediction.mjs
index 984d1417..474870b0 100644
--- a/scripts/check-packaged-prediction.mjs
+++ b/scripts/check-packaged-prediction.mjs
@@ -16,7 +16,7 @@ if (!(await verified(neural, { files: bundlePins }))) throw new Error('Packaged
 const pin = JSON.parse(await readFile(new URL('./prediction-neural.json', import.meta.url), 'utf8'));
 if (JSON.stringify(JSON.parse(await readFile(join(neural, 'model-bundle.json'), 'utf8'))) !== JSON.stringify(pin.bundle)) throw new Error('Wrong packaged neural policy');
 const build = JSON.parse(await readFile(join(neural, 'worker-notices/BUILD.json'), 'utf8'));
-if (build.commit !== pin.revision || build.version !== '0.2.0') throw new Error('Wrong worker provenance');
+if (build.commit !== pin.revision || build.version !== '0.2.1' || build.worker_protocol !== 2) throw new Error('Wrong worker provenance');
 const workerPin = pin.workers[build.target];
 if (!workerPin) throw new Error('Unsupported packaged worker');
 const windows = build.target.includes('windows');
diff --git a/scripts/fetch-prediction-neural.mjs b/scripts/fetch-prediction-neural.mjs
index eda64403..2fbe569b 100644
--- a/scripts/fetch-prediction-neural.mjs
+++ b/scripts/fetch-prediction-neural.mjs
@@ -1,6 +1,6 @@
 // Build-time only. Installed applications never fetch models or workers.
 import { createHash } from 'node:crypto';
-import { readFile, writeFile, mkdir, copyFile, chmod } from 'node:fs/promises';
+import { readFile, writeFile, mkdir, copyFile, chmod, mkdtemp, rm } from 'node:fs/promises';
 import { join, resolve, basename } from 'node:path';
 import { fileURLToPath } from 'node:url';
 import { spawnSync } from 'node:child_process';
@@ -36,6 +36,30 @@ export async function pinnedFile(path, pin, url, fetcher = fetch) {
   await writeFile(path, bytes);
 }
 
+// Prepared companion artifacts are review inputs, not a published release.
+// Only the pinned archive and selected file hashes are trusted. A later release
+// replaces this source with its permanent public asset URL without changing bytes.
+export async function acquireWorker(workers, pin, cache, runner = run) {
+  if (!pin.prepared_artifact) return acquire(workers, pin);
+  if (await verified(workers, pin)) return false;
+  const prepared = pin.prepared_artifact;
+  if (!Number.isSafeInteger(prepared.run_id) || prepared.run_id <= 0 ||
+      !/^neural-[a-z0-9-]+$/.test(prepared.name) ||
+      basename(prepared.archive) !== prepared.archive || !prepared.archive.endsWith('.zip')) {
+    throw new Error('Invalid prepared worker source');
+  }
+  await mkdir(cache, { recursive: true });
+  const staging = await mkdtemp(join(cache, 'prepared-'));
+  try {
+    await runner('gh', ['run', 'download', String(prepared.run_id), '--repo',
+      'switchifyapp/switchify-prediction', '--name', prepared.name, '--dir', staging], root);
+    const bytes = await readFile(join(staging, prepared.archive));
+    return await acquire(workers, { ...pin, url: 'prepared-worker' }, async () => new Response(bytes));
+  } finally {
+    await rm(staging, { recursive: true, force: true });
+  }
+}
+
 export async function prepare() {
   const target = process.env.TAURI_ENV_TARGET_TRIPLE ??
     (process.platform === 'win32' ? 'x86_64-pc-windows-msvc' :
@@ -48,7 +72,7 @@ export async function prepare() {
   const binaries = join(root, 'src-tauri/binaries');
   await mkdir(bundle, { recursive: true });
   await mkdir(binaries, { recursive: true });
-  await acquire(workers, workerPin);
+  await acquireWorker(workers, workerPin, cache);
   for (const name of Object.keys(workerPin.files)) {
     const file = basename(name);
     if (file.startsWith('switchify-smol-worker')) {
diff --git a/scripts/fetch-prediction-neural.node-test.mjs b/scripts/fetch-prediction-neural.node-test.mjs
index 3cfc1019..471c8066 100644
--- a/scripts/fetch-prediction-neural.node-test.mjs
+++ b/scripts/fetch-prediction-neural.node-test.mjs
@@ -4,7 +4,8 @@ import { createHash } from 'node:crypto';
 import { mkdtemp, readFile, writeFile, rm } from 'node:fs/promises';
 import { join } from 'node:path';
 import { tmpdir } from 'node:os';
-import { pinnedFile, bundlePins } from './fetch-prediction-neural.mjs';
+import { pinnedFile, bundlePins, acquireWorker } from './fetch-prediction-neural.mjs';
+import { zipSync } from 'fflate';
 
 test('neural sources reject corrupt/oversized input and retain the previous cache', async () => {
   const dir = await mkdtemp(join(tmpdir(), 'neural-source-'));
@@ -29,3 +30,26 @@ test('installed model pins retain the approved Q8 identity and license', () => {
   assert.ok(bundlePins['MODEL_LICENSE.txt']);
   assert.ok(bundlePins['MODEL_CARD.md']);
 });
+
+test('prepared workers require pinned archive bytes and support verified offline reuse', async () => {
+  const dir = await mkdtemp(join(tmpdir(), 'neural-prepared-'));
+  const hash = bytes => createHash('sha256').update(bytes).digest('hex');
+  const bytes = Buffer.from('worker fixture');
+  const zip = zipSync({ 'bundle/worker': bytes });
+  const pin = { prepared_artifact: { run_id: 1, name: 'neural-windows-latest', archive: 'workers.zip' },
+    archive_sha256: hash(zip), files: { 'bundle/worker': { size: bytes.length, sha256: hash(bytes) } } };
+  const target = join(dir, 'workers');
+  const runner = contents => async (command, args) => {
+    assert.equal(command, 'gh');
+    assert.equal(args[3], '--repo');
+    await writeFile(join(args.at(-1), 'workers.zip'), contents);
+  };
+  try {
+    await assert.rejects(acquireWorker(target, pin, dir, runner(Buffer.from('corrupt'))));
+    await acquireWorker(target, pin, dir, runner(zip));
+    assert.equal(await readFile(join(target, 'bundle/worker'), 'utf8'), 'worker fixture');
+    await acquireWorker(target, pin, dir, () => { throw new Error('offline cache was not used'); });
+    await assert.rejects(acquireWorker(join(dir, 'other'), { ...pin,
+      prepared_artifact: { ...pin.prepared_artifact, archive: '../bad.zip' } }, dir, runner(zip)));
+  } finally { await rm(dir, { recursive: true, force: true }); }
+});
diff --git a/scripts/measure-neural.py b/scripts/measure-neural.py
index 7df6dad4..33853c28 100644
--- a/scripts/measure-neural.py
+++ b/scripts/measure-neural.py
@@ -58,12 +58,12 @@
                 peak = max(peak, rss)
             except psutil.NoSuchProcess:
                 pass
-            if time.monotonic() - start > 1200:
+            if time.monotonic() - start > 2400:
                 for child in tracked.children(recursive=True):
                     child.kill()
                 process.kill()
                 process.wait()
-                raise RuntimeError('Integration benchmark exceeded 20 minutes')
+                raise RuntimeError('Integration benchmark exceeded 40 minutes')
             time.sleep(0.02)
     if report_path.is_file():
         report = json.loads(report_path.read_bytes())
diff --git a/scripts/prediction-neural.json b/scripts/prediction-neural.json
index c03be704..844bbfee 100644
--- a/scripts/prediction-neural.json
+++ b/scripts/prediction-neural.json
@@ -1,5 +1,5 @@
 {
-  "revision": "99a3ef03cb54500006887a998547fded4ad39e0e",
+  "revision": "40c09c3f125403811b167d042730826ff6d451b7",
   "bundle": {
     "format_version": 1,
     "model_id": "smollm2-135m-q8-v1",
@@ -10,7 +10,19 @@
       "context_tokens": 64,
       "shortlist": 8,
       "max_results": 5,
-      "scoring": "whole-word log probability plus boundary probability, sequential"
+      "scoring": "whole-word log probability plus boundary probability, sequential",
+      "generation": {
+        "protocol_version": 2,
+        "beam_width": 8,
+        "max_tokens_per_word": 8,
+        "max_forward_evaluations": 64,
+        "max_results": 3,
+        "max_word_bytes": 128,
+        "reply_deadline_ms": 2000,
+        "scoring": "whole-word probability including following boundary; normalized duplicates excluded",
+        "minimum_boundary_probability": 0.5,
+        "search_cutoff_ms": 1600
+      }
     },
     "files": {
       "model.gguf": {
@@ -63,232 +75,249 @@
   },
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-      "archive_sha256": "af2c2fe55e9681522bb37478d71256f9cdb5b436945eb2034eaf1a25d90b2c36",
+      "prepared_artifact": {
+        "run_id": 37211131470,
+        "name": "neural-macos-latest",
+        "archive": "switchify-prediction-neural-0.2.1-aarch64-apple-darwin.zip"
+      },
+      "archive_sha256": "2ba7ff29b2286eb02b0435f608e44934d89d119967418d15b8f95aaf5072544c",
       "files": {
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     "x86_64-unknown-linux-gnu": {
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-      "archive_sha256": "ce3350ebd76110474f783c5b0eda352b6a2011dbc5f00f0385cb9d149f1e968e",
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+      },
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-          "size": 473,
-          "sha256": "55b6e9429ecb290fd194682406773a6f07102861529d5e1c0e51a6d2d4fc8c7e"
+        "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/QUALIFICATION.json": {
+          "sha256": "55b6e9429ecb290fd194682406773a6f07102861529d5e1c0e51a6d2d4fc8c7e",
+          "size": 473
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-        "switchify-prediction-neural-0.2.0-x86_64-unknown-linux-gnu/QUALIFICATION.md": {
-          "size": 7086,
-          "sha256": "f5d13a7406728515112cab4da093ee19642f68abbb9f30c8c8f511ae68ec8eb3"
+        "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/QUALIFICATION.md": {
+          "sha256": "f5d13a7406728515112cab4da093ee19642f68abbb9f30c8c8f511ae68ec8eb3",
+          "size": 7086
         }
       }
     }
-  }
+  },
+  "library_revision": "70fc4a869ffd22a405527e4d81d25c149aebc418"
 }
diff --git a/scripts/probe-neural-worker.py b/scripts/probe-neural-worker.py
index 6ddcee83..e7a0bb67 100644
--- a/scripts/probe-neural-worker.py
+++ b/scripts/probe-neural-worker.py
@@ -1,7 +1,7 @@
 """Text-free packaged-worker diagnostic with a fixed synthetic query.
 
 The five-second diagnostic bound measures slow workers without changing the
-application's 500 ms inference deadline. No desktop input is injected.
+application's two-second inference deadline. No desktop input is injected.
 """
 import json
 import os
@@ -39,7 +39,7 @@ def receive(process, timeout):
 def main():
     worker = Path(os.environ['SWITCHIFY_BENCHMARK_WORKER'])
     bundle = Path(os.environ['SWITCHIFY_BENCHMARK_MODEL']).parent.parent / 'prediction-neural'
-    report = {'ready': False, 'ranked': False}
+    report = {'ready': False, 'generated': False}
     start = time.monotonic()
     process = subprocess.Popen([str(worker), str(bundle)], stdin=subprocess.PIPE,
                                stdout=subprocess.PIPE, stderr=subprocess.DEVNULL,
@@ -48,19 +48,17 @@ def main():
     try:
         ready = receive(process, 30)
         report['ready_ms'] = (time.monotonic() - start) * 1000
-        report['ready'] = isinstance(ready, dict) and 'Ready' in ready
+        report['ready'] = isinstance(ready, dict) and ready.get('Ready', {}).get('version') == 2
         if report['ready']:
-            frame = json.dumps({'Predict': {'id': 1, 'session': 0,
-                                'before': 'please send the',
-                                'candidates': ['message', 'email', 'file', 'letter',
-                                               'report', 'information', 'details', 'document'],
-                                'limit': 5}}).encode()
+            frame = json.dumps({'Generate': {'id': 1, 'session': 0,
+                                'before': 'please send the', 'prefix': '',
+                                'exclude': ['receipt', 'order', 'tickets'], 'limit': 3}}).encode()
             start = time.monotonic()
             process.stdin.write(struct.pack('<I', len(frame)) + frame)
             process.stdin.flush()
             ranked = receive(process, 5)
             report['query_ms'] = (time.monotonic() - start) * 1000
-            report['ranked'] = isinstance(ranked, dict) and 'Ranked' in ranked
+            report['generated'] = isinstance(ranked, dict) and 'Generated' in ranked
         report['exit_before_cleanup'] = process.poll()
     finally:
         process.kill()
diff --git a/src-tauri/Cargo.lock b/src-tauri/Cargo.lock
index 05be9877..df67f9b3 100644
--- a/src-tauri/Cargo.lock
+++ b/src-tauri/Cargo.lock
@@ -4217,7 +4217,7 @@ dependencies = [
 [[package]]
 name = "switchify-prediction"
 version = "0.2.0"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=171210ee89e3944d1c606d50ad779a5dfb1651b8#171210ee89e3944d1c606d50ad779a5dfb1651b8"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=70fc4a869ffd22a405527e4d81d25c149aebc418#70fc4a869ffd22a405527e4d81d25c149aebc418"
 dependencies = [
  "clap",
  "rusqlite",
@@ -4232,8 +4232,8 @@ dependencies = [
 
 [[package]]
 name = "switchify-prediction-neural"
-version = "0.2.0"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=171210ee89e3944d1c606d50ad779a5dfb1651b8#171210ee89e3944d1c606d50ad779a5dfb1651b8"
+version = "0.2.1"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=70fc4a869ffd22a405527e4d81d25c149aebc418#70fc4a869ffd22a405527e4d81d25c149aebc418"
 dependencies = [
  "clap",
  "serde",
diff --git a/src-tauri/Cargo.toml b/src-tauri/Cargo.toml
index 7bce3a6c..75d4cb79 100644
--- a/src-tauri/Cargo.toml
+++ b/src-tauri/Cargo.toml
@@ -14,8 +14,8 @@ crate-type = ["staticlib", "cdylib", "rlib"]
 tauri-build = { version = "2", features = [] }
 
 [dependencies]
-switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "171210ee89e3944d1c606d50ad779a5dfb1651b8" }
-switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "171210ee89e3944d1c606d50ad779a5dfb1651b8" }
+switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "70fc4a869ffd22a405527e4d81d25c149aebc418" }
+switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "70fc4a869ffd22a405527e4d81d25c149aebc418" }
 anyhow = "1"
 base64 = "0.22"
 directories = "6.0.0"
diff --git a/src-tauri/src/point_workflow.rs b/src-tauri/src/point_workflow.rs
index c4f69772..3f3ce58d 100644
--- a/src-tauri/src/point_workflow.rs
+++ b/src-tauri/src/point_workflow.rs
@@ -2384,7 +2384,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 7,
-                words: vec!["water".into()],
+                words: crate::prediction::slots(vec!["water".to_owned()]),
             }),
             false,
         );
diff --git a/src-tauri/src/prediction/database.rs b/src-tauri/src/prediction/database.rs
index 57eaaf22..af46eb68 100644
--- a/src-tauri/src/prediction/database.rs
+++ b/src-tauri/src/prediction/database.rs
@@ -98,11 +98,10 @@ impl Database {
         } else {
             0
         };
-        let Some(mut prediction) = result.ok().filter(|_| self.slow_calls < SLOW_CALLS) else {
+        let Some(prediction) = result.ok().filter(|_| self.slow_calls < SLOW_CALLS) else {
             self.state = State::Unavailable;
             return (Status::Unavailable, Prediction::default());
         };
-        prediction.words.truncate(5);
         (Status::Ready, prediction)
     }
     #[cfg(test)]
@@ -150,7 +149,9 @@ impl Predict for FakeModel {
         })
         .map(str::to_owned)
         .collect();
-        Ok(Prediction { words })
+        Ok(Prediction {
+            words: super::model::slots(words),
+        })
     }
 }
 
@@ -162,7 +163,7 @@ mod tests {
         fn predict(&mut self, _before: &str, _prefix: &str) -> Result<Prediction, ()> {
             std::thread::sleep(self.1);
             self.0.clone().map(|w| Prediction {
-                words: w.iter().map(|w| (*w).to_owned()).collect(),
+                words: super::super::model::slots(w),
             })
         }
     }
@@ -206,7 +207,7 @@ mod tests {
             Duration::ZERO,
         )));
         let prediction = db.predict(&context()).1;
-        assert_eq!(prediction.words.len(), 5);
+        assert_eq!(prediction.words.len(), 6);
         db.state = State::Ready(Box::new(Fake(Err(()), Duration::ZERO)));
         assert_eq!(
             db.predict(&context()),
@@ -227,7 +228,7 @@ mod tests {
                 let (status, prediction) = db.predict(&context());
                 assert_eq!(
                     (status, prediction.words),
-                    (Status::Ready, vec!["late".to_owned()])
+                    (Status::Ready, super::super::model::slots(["late"]))
                 );
             }
             db.state = prompt();
diff --git a/src-tauri/src/prediction/mod.rs b/src-tauri/src/prediction/mod.rs
index 1131e870..164fdf95 100644
--- a/src-tauri/src/prediction/mod.rs
+++ b/src-tauri/src/prediction/mod.rs
@@ -2,6 +2,8 @@ mod activity;
 mod context;
 mod database;
 mod model;
+#[cfg(test)]
+pub(crate) use model::slots;
 pub mod worker;
 
 use crate::scan_keyboard::{Key, Keyboard, Modifier, Page, Stroke};
@@ -983,7 +985,7 @@ mod tests {
         let batch = worker::Batch {
             refined: false,
             token: 1,
-            words: vec!["water".into()],
+            words: crate::prediction::slots(vec!["water".to_owned()]),
         };
         service.received_suggestions(
             &mut keyboard,
diff --git a/src-tauri/src/prediction/model.rs b/src-tauri/src/prediction/model.rs
index ccbee6ca..1bb1fbc7 100644
--- a/src-tauri/src/prediction/model.rs
+++ b/src-tauri/src/prediction/model.rs
@@ -7,7 +7,13 @@ const DATABASE_SHA256: &str = "222253417d0a7a705823ffb7e599a3bcf5d5d3daf4a9d7616
 
 #[derive(Clone, Debug, Default, PartialEq, Eq)]
 pub struct Prediction {
-    pub words: Vec<String>,
+    pub words: [Option<String>; 6],
+}
+
+#[cfg(test)]
+pub fn slots(words: impl IntoIterator<Item = impl Into<String>>) -> [Option<String>; 6] {
+    let mut words = words.into_iter();
+    std::array::from_fn(|_| words.next().map(Into::into))
 }
 
 pub trait Predict: Send {
@@ -29,6 +35,7 @@ pub struct Model {
     predictor: Predictor,
     neural: Option<switchify_prediction_neural::Refiner>,
     request: Option<u64>,
+    instant: [Option<String>; 3],
     config: Option<switchify_prediction_neural::Config>,
 }
 
@@ -95,6 +102,7 @@ impl Model {
             neural: None,
             config,
             request: None,
+            instant: Default::default(),
         })
     }
 }
@@ -117,28 +125,23 @@ impl Predict for Model {
             self.neural = switchify_prediction_neural::Refiner::new(config).ok();
         }
         let options = Options {
-            limit: 5,
+            limit: 3,
             min_chars: 0,
             unigram_only: false,
         };
-        if let Some(neural) = &mut self.neural {
-            if let Ok(immediate) = neural.submit(&self.predictor, before, prefix, options, 0) {
-                self.request = immediate
-                    .refinement_requested
-                    .then_some(immediate.request_id);
-                return Ok(Prediction {
-                    words: immediate.words.into_iter().map(display_word).collect(),
-                });
-            }
-        }
-        self.request = None;
+        let words: Vec<String> = self
+            .predictor
+            .predict(before, prefix, options)
+            .into_iter()
+            .map(|s| s.word)
+            .collect();
+        self.instant = std::array::from_fn(|i| words.get(i).cloned().map(display_word));
+        self.request = self
+            .neural
+            .as_mut()
+            .and_then(|n| n.generate(before, prefix, 0, &words, 3).ok().flatten());
         Ok(Prediction {
-            words: self
-                .predictor
-                .predict(before, prefix, options)
-                .into_iter()
-                .map(|s| display_word(s.word))
-                .collect(),
+            words: std::array::from_fn(|i| self.instant.get(i).cloned().flatten()),
         })
     }
     fn poll(&mut self) -> Option<Prediction> {
@@ -148,7 +151,13 @@ impl Predict for Model {
         }
         self.request = None;
         Some(Prediction {
-            words: result.words.into_iter().map(display_word).collect(),
+            words: std::array::from_fn(|i| {
+                if i < 3 {
+                    self.instant[i].clone()
+                } else {
+                    result.words.get(i - 3).cloned().map(display_word)
+                }
+            }),
         })
     }
     fn pending(&self) -> bool {
@@ -163,6 +172,7 @@ impl Predict for Model {
     }
     fn reset(&mut self) {
         self.request = None;
+        self.instant = Default::default();
         if let Some(n) = &mut self.neural {
             n.reset();
         }
@@ -200,6 +210,7 @@ mod tests {
             neural: None,
             config: None,
             request: None,
+            instant: Default::default(),
         };
         model.config = Some(switchify_prediction_neural::Config {
             bundle: dir.0.join("missing"),
@@ -207,19 +218,49 @@ mod tests {
             accelerated_worker: None,
             threads: 4,
         });
-        assert_eq!(model.predict("I need ", "h").unwrap().words[0], "help");
+        assert_eq!(
+            model.predict("I need ", "h").unwrap().words[0]
+                .as_deref()
+                .unwrap(),
+            "help"
+        );
         assert!(!model.pending());
         assert!(model.config.is_none());
         assert!(model.poll().is_none());
-        assert_eq!(model.predict("I drink ", "").unwrap().words[0], "water");
-        assert!(model.predict("", "zyzzy").unwrap().words.is_empty());
+        assert_eq!(
+            model.predict("I drink ", "").unwrap().words[0]
+                .as_deref()
+                .unwrap(),
+            "water"
+        );
+        assert!(model
+            .predict("", "zyzzy")
+            .unwrap()
+            .words
+            .iter()
+            .all(Option::is_none));
         assert_eq!(
             model.predict("", "cafe\u{301}").unwrap().words,
-            vec!["café"]
+            crate::prediction::slots(["café"])
+        );
+        assert_eq!(
+            model.predict("", "can’").unwrap().words,
+            crate::prediction::slots(["can't"])
+        );
+        assert_eq!(
+            model.predict("", "i’").unwrap().words,
+            crate::prediction::slots(["I'm"])
+        );
+        assert_eq!(
+            model
+                .predict("", "h")
+                .unwrap()
+                .words
+                .iter()
+                .flatten()
+                .count(),
+            3
         );
-        assert_eq!(model.predict("", "can’").unwrap().words, vec!["can't"]);
-        assert_eq!(model.predict("", "i’").unwrap().words, vec!["I'm"]);
-        assert_eq!(model.predict("", "h").unwrap().words.len(), 5);
         assert_eq!(
             model.predict("I need.", "h").unwrap(),
             model.predict("", "h").unwrap()
@@ -287,13 +328,18 @@ mod tests {
             ][i % 4];
             let start = Instant::now();
             let suggestions = model.predict(context, prefix).unwrap();
-            assert!(!suggestions.words.is_empty());
+            assert!(!suggestions.words.iter().all(Option::is_none));
             if i >= 20 {
                 samples.push(start.elapsed().as_secs_f64() * 1000.0);
             }
         }
         samples.sort_by(f64::total_cmp);
-        assert_eq!(model.predict("I need ", "he").unwrap().words[0], "help");
+        assert_eq!(
+            model.predict("I need ", "he").unwrap().words[0]
+                .as_deref()
+                .unwrap(),
+            "help"
+        );
         #[cfg(target_os = "macos")]
         let cpu = std::process::Command::new("sysctl")
             .args(["-n", "machdep.cpu.brand_string"])
diff --git a/src-tauri/src/prediction/worker.rs b/src-tauri/src/prediction/worker.rs
index 4ed7f32f..755b6a46 100644
--- a/src-tauri/src/prediction/worker.rs
+++ b/src-tauri/src/prediction/worker.rs
@@ -62,7 +62,7 @@ pub enum Request {
 pub struct Batch {
     pub token: u64,
     pub refined: bool,
-    pub words: Vec<String>,
+    pub words: [Option<String>; 6],
 }
 #[derive(Serialize, Deserialize)]
 pub enum Response {
@@ -105,6 +105,8 @@ pub fn receive<T: serde::de::DeserializeOwned>(reader: &mut impl Read) -> Result
     reader.read_exact(&mut bytes).map_err(|_| ())?;
     serde_json::from_slice(&bytes).map_err(|_| ())
 }
+type Inserts = [Option<(usize, String)>; 6];
+
 pub struct Engine {
     database: Database,
     status: Status,
@@ -120,10 +122,10 @@ pub struct Engine {
     snapshot: Option<String>,
     batch: Option<Batch>,
     /// Per suggestion: how many typed characters to delete, then the text.
-    inserts: Vec<(usize, String)>,
+    inserts: Inserts,
     token: u64,
     case: (Shift, bool, bool),
-    displayed: Option<(Batch, Vec<(usize, String)>)>,
+    displayed: Option<(Batch, Inserts)>,
     generation: u64,
 }
 impl Engine {
@@ -142,7 +144,7 @@ impl Engine {
             revision: 0,
             snapshot: None,
             batch: None,
-            inserts: Vec::new(),
+            inserts: Default::default(),
             token: 0,
             case: (Shift::Off, false, false),
             displayed: None,
@@ -156,7 +158,7 @@ impl Engine {
         self.clipped = false;
         self.snapshot = None;
         self.batch = None;
-        self.inserts.clear();
+        self.inserts = Default::default();
     }
     fn stable(&self, target: usize, epoch: u64) -> bool {
         self.tracked && (self.observe)() == (epoch, true) && (self.foreground)() == Ok(target)
@@ -250,9 +252,12 @@ impl Engine {
             (prediction.words, false)
         };
         self.token = self.token.wrapping_add(1);
-        self.inserts.clear();
-        let mut labels = Vec::new();
-        for word in words {
+        self.inserts = Default::default();
+        let mut labels = std::array::from_fn(|_| None);
+        for (index, word) in words.into_iter().enumerate() {
+            let Some(word) = word else {
+                continue;
+            };
             let Some(offset) = prefix_offset(&word, &ctx.prefix) else {
                 continue;
             };
@@ -275,15 +280,15 @@ impl Engine {
             };
             match head {
                 Some(head) => {
-                    labels.push(format!("{head}{suffix}"));
-                    self.inserts.push((
+                    labels[index] = Some(format!("{head}{suffix}"));
+                    self.inserts[index] = Some((
                         ctx.prefix.graphemes(true).count(),
                         format!("{head}{suffix} "),
                     ));
                 }
                 None => {
-                    labels.push(ctx.prefix.clone() + &suffix);
-                    self.inserts.push((0, suffix + " "));
+                    labels[index] = Some(ctx.prefix.clone() + &suffix);
+                    self.inserts[index] = Some((0, suffix + " "));
                 }
             }
         }
@@ -317,11 +322,11 @@ impl Engine {
         } else {
             return None;
         };
-        let insert = inserts.get(index)?.clone();
+        let insert = inserts.get(index)?.clone()?;
         self.database.reset();
         self.batch = None;
         self.displayed = None;
-        self.inserts.clear();
+        self.inserts = Default::default();
         self.snapshot = None;
         Some(insert)
     }
@@ -576,12 +581,19 @@ mod tests {
         fn predict(&mut self, _: &str, _: &str) -> Result<super::super::model::Prediction, ()> {
             self.pending = true;
             Ok(super::super::model::Prediction {
-                words: vec!["water".into(), "walk".into()],
+                words: crate::prediction::slots(["water", "walk", "waffle"]),
             })
         }
         fn poll(&mut self) -> Option<super::super::model::Prediction> {
             std::mem::take(&mut self.pending).then(|| super::super::model::Prediction {
-                words: vec!["walk".into(), "water".into()],
+                words: [
+                    Some("water".into()),
+                    Some("walk".into()),
+                    Some("waffle".into()),
+                    Some("walnut".into()),
+                    None,
+                    Some("wavy".into()),
+                ],
             })
         }
         fn pending(&self) -> bool {
@@ -621,6 +633,7 @@ mod tests {
         let mut immediate = Vec::new();
         let mut refined = Vec::new();
         let mut failures = 0;
+        let mut generated_words = 0;
         let mut session_reports = Vec::new();
         // A timeout deliberately disables refinement for the keyboard session.
         // Simulate at most five explicit reopenings to collect 1,000 successes,
@@ -652,6 +665,7 @@ mod tests {
                     revision,
                     None,
                 )));
+                assert!(first.words[3..].iter().all(Option::is_none));
                 let first_ms = start.elapsed().as_secs_f64() * 1000.;
                 let mut success = false;
                 while e.database.pending() {
@@ -660,6 +674,10 @@ mod tests {
                     let next =
                         response_batch(e.respond(request(vec![], revision, Some(first.token))));
                     if next.refined {
+                        assert_eq!(&first.words[..3], &next.words[..3]);
+                        if i >= 20 {
+                            generated_words += next.words[3..].iter().flatten().count();
+                        }
                         success = true;
                         break;
                     }
@@ -707,6 +725,9 @@ mod tests {
             "sessions":session_reports,"maximum_sessions":5,
             "retry_policy":"Benchmark simulates explicit keyboard reopenings after failure; production never retries automatically",
             "scope":"Production Engine and model adapter, real neural child IPC, 20ms polling, fake input/activity; excludes outer desktop pipe and rendering",
+            "policy":"three instant plus three generated optional slots",
+            "generated_words":generated_words,
+            "neural_slot_fill_rate":generated_words as f64 / (refined.len().max(1) * 3) as f64,
             "production_qualified":false});
         println!("{report}");
         if let Some(path) = std::env::var_os("SWITCHIFY_NEURAL_REPORT") {
@@ -727,14 +748,16 @@ mod tests {
             assert!(!immediate.refined);
             let refined = response_batch(e.respond(request(vec![], 1, Some(immediate.token))));
             assert!(refined.refined);
-            assert_eq!(refined.words, vec!["walk", "water"]);
+            assert_eq!(&refined.words[..3], &immediate.words[..3]);
+            assert_eq!(refined.words[3].as_deref(), Some("walnut"));
+            assert!(e.accept(refined.token, 4).is_none());
             if accept_old {
                 assert_eq!(e.accept(immediate.token, 0), Some((0, "ter ".into())));
                 assert!(e.accept(refined.token, 0).is_none());
             } else {
                 e.respond(request(vec![], 1, Some(refined.token)));
                 assert!(e.accept(immediate.token, 0).is_none());
-                assert_eq!(e.accept(refined.token, 0), Some((0, "lk ".into())));
+                assert_eq!(e.accept(refined.token, 5), Some((0, "vy ".into())));
             }
         }
     }
@@ -821,7 +844,7 @@ mod tests {
             let b = e
                 .query(vec![append(typed)], 1, Shift::Off, false, false)
                 .unwrap();
-            assert_eq!(b.words, vec![expected]);
+            assert_eq!(b.words, crate::prediction::slots([expected]));
             assert_eq!(e.accept(b.token, 0), Some((deletion, insertion.to_owned())));
         }
         let mut e = engine();
@@ -841,11 +864,14 @@ mod tests {
         let waiting = e
             .query(vec![append("wa")], 1, Shift::Off, false, false)
             .unwrap();
-        assert!(waiting.words.is_empty());
+        assert!(waiting.words.iter().all(Option::is_none));
         assert_eq!(e.status, Status::Loading);
         e.database = Database::fixture();
         let ready = e.query(vec![], 1, Shift::Off, false, false).unwrap();
-        assert_eq!(ready.words, vec!["water", "waffle", "walk"]);
+        assert_eq!(
+            ready.words,
+            crate::prediction::slots(["water", "waffle", "walk"])
+        );
         assert_ne!(ready.token, waiting.token);
         // Once ready, an unchanged query retains the scan choice identity.
         assert_eq!(
@@ -855,15 +881,18 @@ mod tests {
     }
 
     #[test]
-    fn five_slots_contain_ranked_words_only() {
+    fn six_slots_preserve_populated_positions() {
         let mut e = engine();
         let b = e
             .query(vec![append("wa")], 1, Shift::Off, false, false)
             .unwrap();
-        assert_eq!(b.words, vec!["water", "waffle", "walk"]);
+        assert_eq!(
+            b.words,
+            crate::prediction::slots(["water", "waffle", "walk"])
+        );
         assert_eq!(e.accept(b.token, 1).unwrap(), (0, "ffle ".to_owned()));
         let upper = e.query(vec![], 1, Shift::Off, true, false).unwrap();
-        assert_eq!(upper.words[1], "waFFLE");
+        assert_eq!(upper.words[1].as_deref().unwrap(), "waFFLE");
     }
     #[test]
     fn first_letter_and_completion_chain_use_only_buffer() {
@@ -871,11 +900,11 @@ mod tests {
         let first = e
             .query(vec![append("w")], 1, Shift::Off, false, false)
             .unwrap();
-        assert!(first.words.iter().any(|w| w == "water"));
+        assert!(first.words.iter().flatten().any(|w| w == "water"));
         let b = e
             .query(vec![append("a")], 2, Shift::Off, false, false)
             .unwrap();
-        assert_eq!(b.words[0], "water");
+        assert_eq!(b.words[0].as_deref().unwrap(), "water");
         let (deleted, suffix) = e.accept(b.token, 0).unwrap();
         assert_eq!(deleted, 0);
         assert_eq!(suffix, "ter ");
@@ -988,18 +1017,17 @@ mod tests {
         let b = e
             .query(vec![append("Wa")], 1, Shift::Off, false, false)
             .unwrap();
-        assert_eq!(b.words[0], "Water");
+        assert_eq!(b.words[0].as_deref().unwrap(), "Water");
         assert_eq!(
             e.query(vec![], 1, Shift::Off, false, false).unwrap().token,
             b.token
         );
         let upper = e.query(vec![], 1, Shift::Off, true, false).unwrap();
-        assert_eq!(upper.words[0], "WaTER");
+        assert_eq!(upper.words[0].as_deref().unwrap(), "WaTER");
         assert_ne!(upper.token, b.token);
     }
     #[test]
     fn accepting_restores_a_capital_the_typed_prefix_lacks_and_nothing_else() {
-        let w = |s: &[&str]| s.iter().map(|s| (*s).to_owned()).collect::<Vec<_>>();
         assert_eq!(restored("lon", "Lon").as_deref(), Some("Lon"));
         assert_eq!(restored("i’", "I’").as_deref(), Some("I’"));
         assert_eq!(restored("whats", "Whats").as_deref(), Some("Whats"));
@@ -1011,7 +1039,7 @@ mod tests {
         let b = e
             .query(vec![append("wh")], 1, Shift::Off, false, false)
             .unwrap();
-        assert_eq!(b.words, w(&["WhatsApp"]));
+        assert_eq!(b.words, crate::prediction::slots(["WhatsApp"]));
         assert_eq!(e.accept(b.token, 0).unwrap(), (2, "WhatsApp ".to_owned()));
         // The edits the service queues afterwards leave the buffer reading
         // as the screen does.
@@ -1037,7 +1065,7 @@ mod tests {
                 false,
             )
             .unwrap();
-        assert_eq!(b.words, w(&["WhatsApp"]));
+        assert_eq!(b.words, crate::prediction::slots(["WhatsApp"]));
         assert_eq!(e.accept(b.token, 0).unwrap(), (0, "atsApp ".to_owned()));
         // A word in capitals is never retyped.
         let b = e
@@ -1070,16 +1098,20 @@ mod tests {
         e.query(vec![append("WA")], 1, Shift::Off, false, false)
             .unwrap();
         let row = e.query(vec![], 1, Shift::Off, false, false).unwrap();
-        assert_eq!(row.words[0], "WATER");
-        assert_eq!(row.words[1], "WAFFLE");
+        assert_eq!(row.words[0].as_deref().unwrap(), "WATER");
+        assert_eq!(row.words[1].as_deref().unwrap(), "WAFFLE");
         assert_eq!(
             e.query(vec![], 1, Shift::Locked, false, false)
                 .unwrap()
-                .words[0],
+                .words[0]
+                .as_deref()
+                .unwrap(),
             "WATER"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Off, true, false).unwrap().words[0],
+            e.query(vec![], 1, Shift::Off, true, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "WATER"
         );
         // One capital is a capitalised word, not a word in capitals.
@@ -1092,7 +1124,9 @@ mod tests {
         )
         .unwrap();
         assert_eq!(
-            e.query(vec![], 2, Shift::Off, false, false).unwrap().words[0],
+            e.query(vec![], 2, Shift::Off, false, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "Water"
         );
     }
@@ -1105,26 +1139,32 @@ mod tests {
         let buffer_only = e
             .query(vec![append("Done! ")], 1, Shift::Off, false, false)
             .unwrap();
-        assert_eq!(buffer_only.words[0], "water");
+        assert_eq!(buffer_only.words[0].as_deref().unwrap(), "water");
         let suggestions = e.query(vec![], 1, Shift::Off, false, true).unwrap();
-        assert_eq!(suggestions.words[0], "Water");
+        assert_eq!(suggestions.words[0].as_deref().unwrap(), "Water");
         let locked = e.query(vec![], 1, Shift::Locked, false, true).unwrap();
-        assert_eq!(locked.words[0], "WATER");
+        assert_eq!(locked.words[0].as_deref().unwrap(), "WATER");
         let once = e.query(vec![], 1, Shift::Once, false, true).unwrap();
-        assert_eq!(once.words[0], "Water");
+        assert_eq!(once.words[0].as_deref().unwrap(), "Water");
         // Modifiers still mirror typing at a sentence start: Caps with Shift
         // locked cancel to lowercase letters, and Shift once under Caps
         // lowers only the first one, exactly as the keys would type them.
         assert_eq!(
-            e.query(vec![], 1, Shift::Off, true, true).unwrap().words[0],
+            e.query(vec![], 1, Shift::Off, true, true).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "WATER"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Locked, true, true).unwrap().words[0],
+            e.query(vec![], 1, Shift::Locked, true, true).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "Water"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Once, true, true).unwrap().words[0],
+            e.query(vec![], 1, Shift::Once, true, true).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "wATER"
         );
     }
@@ -1134,47 +1174,65 @@ mod tests {
         e.query(vec![append("Send ")], 1, Shift::Off, false, false)
             .unwrap();
         assert_eq!(
-            e.query(vec![], 1, Shift::Off, false, false).unwrap().words[0],
+            e.query(vec![], 1, Shift::Off, false, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "water"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Once, false, false).unwrap().words[0],
+            e.query(vec![], 1, Shift::Once, false, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "Water"
         );
         assert_eq!(
             e.query(vec![], 1, Shift::Locked, false, false)
                 .unwrap()
-                .words[0],
+                .words[0]
+                .as_deref()
+                .unwrap(),
             "WATER"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Off, true, false).unwrap().words[0],
+            e.query(vec![], 1, Shift::Off, true, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "WATER"
         );
         assert_eq!(
             e.query(vec![], 1, Shift::Locked, true, false)
                 .unwrap()
-                .words[0],
+                .words[0]
+                .as_deref()
+                .unwrap(),
             "water"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Once, true, false).unwrap().words[0],
+            e.query(vec![], 1, Shift::Once, true, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "wATER"
         );
         assert_eq!(
-            e.query(vec![], 1, Shift::Off, false, false).unwrap().words[3],
+            e.query(vec![], 1, Shift::Off, false, false).unwrap().words[3]
+                .as_deref()
+                .unwrap(),
             "WhatsApp"
         );
         e.query(vec![append("wa")], 2, Shift::Off, false, false)
             .unwrap();
         assert_eq!(
-            e.query(vec![], 2, Shift::Once, false, false).unwrap().words[0],
+            e.query(vec![], 2, Shift::Once, false, false).unwrap().words[0]
+                .as_deref()
+                .unwrap(),
             "water"
         );
         assert_eq!(
             e.query(vec![], 2, Shift::Locked, false, false)
                 .unwrap()
-                .words[0],
+                .words[0]
+                .as_deref()
+                .unwrap(),
             "waTER"
         );
     }
@@ -1257,7 +1315,7 @@ mod tests {
                 let row = e
                     .query(vec![], revision, shift, caps, sentence_start)
                     .unwrap_or_else(|| panic!("no suggestions for {case}"));
-                assert_eq!(row.words[0], label, "label for {case}");
+                assert_eq!(row.words[0].as_deref().unwrap(), label, "label for {case}");
                 assert_eq!(
                     e.accept(row.token, 0),
                     Some((deleted, text.to_owned())),
diff --git a/src-tauri/src/scan_keyboard.rs b/src-tauri/src/scan_keyboard.rs
index 21766e60..c3ce64e2 100644
--- a/src-tauri/src/scan_keyboard.rs
+++ b/src-tauri/src/scan_keyboard.rs
@@ -391,7 +391,7 @@ impl Keyboard {
             rows_for(self.page, self.mac, self.layout)
         };
         if self.prediction_enabled && self.page == Page::Letters && !self.positioning {
-            self.rows.insert(0, (0..5).map(Key::Prediction).collect());
+            self.rows.insert(0, (0..6).map(Key::Prediction).collect());
         }
         if self.prediction_enabled && self.prediction_failed && !self.positioning {
             self.rows.last_mut().unwrap().push(Key::RetryPrediction);
@@ -451,7 +451,7 @@ impl Keyboard {
                 && self
                     .predictions
                     .as_ref()
-                    .is_some_and(|b| !b.words.is_empty())
+                    .is_some_and(|b| b.words.iter().any(Option::is_some))
             {
                 self.scan.nav.reset();
                 self.scan.reset_clock();
@@ -478,9 +478,9 @@ impl Keyboard {
             "Suggestions updating".into()
         } else {
             match self.predictions.as_ref() {
-                Some(batch) if batch.words.len() == 1 => "1 suggestion".into(),
-                Some(batch) if !batch.words.is_empty() => {
-                    format!("{} suggestions", batch.words.len())
+                Some(batch) if batch.words.iter().flatten().count() == 1 => "1 suggestion".into(),
+                Some(batch) if batch.words.iter().any(Option::is_some) => {
+                    format!("{} suggestions", batch.words.iter().flatten().count())
                 }
                 Some(_) => "No suggestions".into(),
                 None => "Type for suggestions".into(),
@@ -499,11 +499,15 @@ impl Keyboard {
         if !self.prediction_row_active() || self.scan.nav.escaping() {
             return false;
         }
-        let count = self.predictions.as_ref().map_or(0, |b| b.words.len());
-        self.scan
-            .position(&self.rows)
-            .1
-            .map_or(count == 0, |column| column >= count)
+        let words = self.predictions.as_ref().map(|b| &b.words);
+        self.scan.position(&self.rows).1.map_or_else(
+            || words.is_none_or(|w| w.iter().all(Option::is_none)),
+            |column| {
+                words
+                    .and_then(|w| w.get(column))
+                    .is_none_or(Option::is_none)
+            },
+        )
     }
     fn skip_disabled(&mut self, automatic: bool) {
         for _ in 0..self.rows.iter().map(Vec::len).sum::<usize>() + 1 {
@@ -666,7 +670,7 @@ impl Keyboard {
             Key::RetryPrediction => return None,
             Key::Prediction(index) => {
                 let batch = self.predictions.as_ref()?;
-                if index >= batch.words.len() {
+                if batch.words.get(index).is_none_or(Option::is_none) {
                     return None;
                 }
                 self.activation = self.scan.begin_activation();
@@ -868,6 +872,7 @@ impl Keyboard {
                 .predictions
                 .as_ref()
                 .and_then(|b| b.words.get(i))
+                .and_then(Option::as_ref)
                 .cloned()
                 .unwrap_or_default(),
             Key::RetryPrediction => "Retry predictions".into(),
@@ -1045,7 +1050,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 token: 1,
                 refined: false,
-                words: vec!["water".into(), "walk".into()],
+                words: crate::prediction::slots(vec!["water".to_owned(), "walk".to_owned()]),
             }),
             false,
         );
@@ -1056,15 +1061,86 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 token: 2,
                 refined: true,
-                words: vec!["walk".into(), "water".into()],
+                words: [
+                    Some("water".into()),
+                    Some("walk".into()),
+                    None,
+                    Some("walnut".into()),
+                    None,
+                    Some("wavy".into()),
+                ],
             }),
             false,
         );
         assert_eq!(k.displayed_prediction(), Some(1));
-        assert_eq!(k.predictions.as_ref().unwrap().words[0], "water");
+        assert_eq!(
+            k.predictions.as_ref().unwrap().words[0].as_deref().unwrap(),
+            "water"
+        );
         assert_eq!(k.queued_predictions.as_ref().unwrap().token, 2);
         assert_eq!(k.scan.position(&k.rows), position);
     }
+    #[test]
+    fn six_slots_skip_internal_gaps_in_both_directions() {
+        use crate::scan_preferences::{Direction, Pattern, Resolved};
+        for direction in [Direction::Forward, Direction::Reverse] {
+            let mut k = Keyboard::configured(
+                false,
+                Resolved {
+                    direction,
+                    pattern: Pattern::Linear,
+                    pass_limit: 0,
+                    ..Default::default()
+                },
+            );
+            k.enable_predictions(true);
+            k.predictions(
+                Some(crate::prediction::worker::Batch {
+                    token: 19,
+                    refined: true,
+                    words: [
+                        Some("one".into()),
+                        None,
+                        None,
+                        Some("four".into()),
+                        None,
+                        Some("six".into()),
+                    ],
+                }),
+                false,
+            );
+            k.restart();
+            assert_eq!(k.rows[0].len(), 6);
+            let mut seen = std::collections::BTreeSet::new();
+            for _ in 0..k.rows.iter().map(Vec::len).sum::<usize>() * 3 {
+                if let (0, Some(column)) = k.scan.position(&k.rows) {
+                    assert!([0, 3, 5].contains(&column));
+                    seen.insert(column);
+                }
+                k.handle(Action::Next);
+            }
+            assert_eq!(seen, [0, 3, 5].into_iter().collect());
+            assert!(k.choose(Key::Prediction(1)).is_none());
+            assert!(k.choose(Key::Prediction(4)).is_none());
+            let frame = k.frame(
+                Rect {
+                    x: 0.,
+                    y: 0.,
+                    width: 1920.,
+                    height: 1080.,
+                },
+                1.,
+                ScannerColor::default(),
+            );
+            let tiles = &frame.tiles[1..7];
+            assert!(tiles
+                .iter()
+                .all(|t| (t.rect.width - tiles[0].rect.width).abs() < 0.01));
+            assert_eq!(tiles[1].text, "");
+            assert_eq!(tiles[3].text, "four");
+        }
+    }
+
     #[test]
     fn prediction_badge_explains_empty_slots_without_replacing_scan_prompt() {
         let mut keyboard = Keyboard::new(false);
@@ -1091,7 +1167,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 1,
-                words: vec![],
+                words: Default::default(),
             }),
             false,
         );
@@ -1100,7 +1176,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 2,
-                words: vec!["water".into()],
+                words: crate::prediction::slots(vec!["water".to_owned()]),
             }),
             false,
         );
@@ -1430,7 +1506,7 @@ mod tests {
                 Some(crate::prediction::worker::Batch {
                     refined: false,
                     token: 1,
-                    words: vec!["hello".into()],
+                    words: crate::prediction::slots(vec!["hello".to_owned()]),
                 }),
                 false,
             );
@@ -1456,7 +1532,7 @@ mod tests {
                 Some(crate::prediction::worker::Batch {
                     refined: false,
                     token: 2,
-                    words: vec!["hello".into()],
+                    words: crate::prediction::slots(vec!["hello".to_owned()]),
                 }),
                 false,
             );
@@ -1527,7 +1603,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 1,
-                words: vec!["hello".into()],
+                words: crate::prediction::slots(vec!["hello".to_owned()]),
             }),
             false,
         );
@@ -1541,7 +1617,7 @@ mod tests {
                 Some(crate::prediction::worker::Batch {
                     refined: false,
                     token,
-                    words: vec!["world".into()],
+                    words: crate::prediction::slots(vec!["world".to_owned()]),
                 }),
                 false,
             );
@@ -1615,7 +1691,7 @@ mod tests {
         let batch = crate::prediction::worker::Batch {
             refined: false,
             token: 1,
-            words: vec!["hello".into()],
+            words: crate::prediction::slots(vec!["hello".to_owned()]),
         };
         keyboard.predictions(Some(batch.clone()), false);
         assert_eq!(keyboard.scan.nav.index(), 0);
@@ -1645,7 +1721,10 @@ mod tests {
                     let batch = crate::prediction::worker::Batch {
                         refined: false,
                         token: 7,
-                        words: vec!["water".into(), "walk".into()],
+                        words: crate::prediction::slots(vec![
+                            "water".to_owned(),
+                            "walk".to_owned(),
+                        ]),
                     };
                     for k in [&mut baseline, &mut polled] {
                         k.enable_predictions(true);
@@ -1685,7 +1764,7 @@ mod tests {
         let batch = crate::prediction::worker::Batch {
             refined: false,
             token: 7,
-            words: vec!["water".into(), "walk".into()],
+            words: crate::prediction::slots(vec!["water".to_owned(), "walk".to_owned()]),
         };
         k.predictions(Some(batch.clone()), false);
         k.handle(Action::Select);
@@ -1710,7 +1789,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 7,
-                words: vec!["water".into(), "walk".into()],
+                words: crate::prediction::slots(vec!["water".to_owned(), "walk".to_owned()]),
             }),
             false,
         );
@@ -2018,7 +2097,7 @@ mod tests {
             Some(crate::prediction::worker::Batch {
                 refined: false,
                 token: 9,
-                words: vec!["hello".into()],
+                words: crate::prediction::slots(vec!["hello".to_owned()]),
             }),
             false,
         );
@@ -2345,7 +2424,7 @@ mod tests {
                 Some(crate::prediction::worker::Batch {
                     refined: false,
                     token: 1,
-                    words: vec!["hello".into(), "world".into()],
+                    words: crate::prediction::slots(vec!["hello".to_owned(), "world".to_owned()]),
                 }),
                 false,
             );
@@ -2387,7 +2466,10 @@ mod tests {
                         Some(crate::prediction::worker::Batch {
                             refined: false,
                             token: 1,
-                            words: vec!["hello".into(), "world".into()],
+                            words: crate::prediction::slots(vec![
+                                "hello".to_owned(),
+                                "world".to_owned(),
+                            ]),
                         }),
                         false,
                     );
@@ -2405,7 +2487,7 @@ mod tests {
                     let next = replacement.then(|| crate::prediction::worker::Batch {
                         refined: false,
                         token: 2,
-                        words: vec!["new".into()],
+                        words: crate::prediction::slots(vec!["new".to_owned()]),
                     });
                     for _ in 0..3 {
                         k.predictions(next.clone(), false);

From 217c32ffab09eac92414863125b66b3fa889f328 Mon Sep 17 00:00:00 2001
From: Owen McGirr <o.a.mcgirr@gmail.com>
Date: Sun, 4 Oct 2026 16:34:44 +0100
Subject: [PATCH 2/3] Pin final generation companion artifacts and record
 measurements

---
 .../six-predictions/generation-macos.json     | 1622 +++++++++++++++++
 .../six-predictions/generation-windows.json   | 1620 ++++++++++++++++
 .../six-predictions/integration-windows.json  |   36 +
 docs/six-predictions.md                       |   58 +
 scripts/prediction-neural.json                |  194 +-
 src-tauri/Cargo.lock                          |    6 +-
 src-tauri/Cargo.toml                          |    4 +-
 7 files changed, 3438 insertions(+), 102 deletions(-)
 create mode 100644 docs/measurements/six-predictions/generation-macos.json
 create mode 100644 docs/measurements/six-predictions/generation-windows.json
 create mode 100644 docs/measurements/six-predictions/integration-windows.json

diff --git a/docs/measurements/six-predictions/generation-macos.json b/docs/measurements/six-predictions/generation-macos.json
new file mode 100644
index 00000000..607fcbd2
--- /dev/null
+++ b/docs/measurements/six-predictions/generation-macos.json
@@ -0,0 +1,1622 @@
+{
+  "schema": 1,
+  "policy": {
+    "context_tokens": 64,
+    "shortlist": 8,
+    "max_results": 5,
+    "scoring": "whole-word log probability plus boundary probability, sequential",
+    "generation": {
+      "protocol_version": 2,
+      "beam_width": 8,
+      "max_tokens_per_word": 8,
+      "max_forward_evaluations": 64,
+      "max_results": 3,
+      "max_word_bytes": 128,
+      "reply_deadline_ms": 2000,
+      "scoring": "whole-word probability including following boundary; normalized duplicates excluded",
+      "minimum_boundary_probability": 0.5,
+      "search_cutoff_ms": 1600
+    }
+  },
+  "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+  "queries": 1000,
+  "warmup_queries": 20,
+  "warmup_failures": 0,
+  "failures": {
+    "rerank": 1
+  },
+  "latency": {
+    "immediate_with_ipc": {
+      "samples": 1000,
+      "median_ms": 0.3935830000045826,
+      "p95_ms": 7.174999999961074,
+      "max_ms": 13.297833000024184
+    },
+    "generation_with_ipc": {
+      "samples": 1000,
+      "median_ms": 349.16329199995744,
+      "p95_ms": 767.1424170000591,
+      "max_ms": 1701.419875000056
+    }
+  },
+  "cold_load_ms": 3774.631999999997,
+  "peak_process_tree_rss_bytes": 904331264,
+  "capabilities": {
+    "accelerated": false,
+    "context_tokens": 64,
+    "deadline_ms": 2000,
+    "shortlist": 8,
+    "threads": 4
+  },
+  "cells": {
+    "regression/documents/0": {
+      "queries": 37,
+      "generated_words": 111,
+      "filled_neural_queries": 37,
+      "generated_oov_words": 3,
+      "oov_targets": 5,
+      "oov_hits": 0,
+      "generation_failures": 0,
+      "generation_context_hits": 0,
+      "current_top3_hits": 6,
+      "current_top6_hits": 6,
+      "statistical_six_top3_hits": 1,
+      "statistical_six_top6_hits": 4,
+      "six_slots_top3_hits": 1,
+      "six_slots_top6_hits": 11,
+      "regressions_vs_statistical_six": 1,
+      "gains_vs_statistical_six": 8,
+      "regressions_vs_current": 1,
+      "current_top3_accuracy": 0.16216216216216217,
+      "current_top6_accuracy": 0.16216216216216217,
+      "statistical_six_top3_accuracy": 0.02702702702702703,
+      "statistical_six_top6_accuracy": 0.10810810810810811,
+      "six_slots_top3_accuracy": 0.02702702702702703,
+      "six_slots_top6_accuracy": 0.2972972972972973,
+      "neural_slot_fill_rate": 1.0
+    },
+    "regression/documents/1": {
+      "queries": 36,
+      "generated_words": 108,
+      "filled_neural_queries": 36,
+      "generated_oov_words": 7,
+      "oov_targets": 5,
+      "oov_hits": 0,
+      "generation_failures": 0,
+      "generation_context_hits": 0,
+      "current_top3_hits": 6,
+      "current_top6_hits": 6,
+      "statistical_six_top3_hits": 5,
+      "statistical_six_top6_hits": 5,
+      "six_slots_top3_hits": 5,
+      "six_slots_top6_hits": 19,
+      "regressions_vs_statistical_six": 0,
+      "gains_vs_statistical_six": 14,
+      "regressions_vs_current": 0,
+      "current_top3_accuracy": 0.16666666666666666,
+      "current_top6_accuracy": 0.16666666666666666,
+      "statistical_six_top3_accuracy": 0.1388888888888889,
+      "statistical_six_top6_accuracy": 0.1388888888888889,
+      "six_slots_top3_accuracy": 0.1388888888888889,
+      "six_slots_top6_accuracy": 0.5277777777777778,
+      "neural_slot_fill_rate": 1.0
+    },
+    "regression/documents/2": {
+      "queries": 37,
+      "generated_words": 111,
+      "filled_neural_queries": 37,
+      "generated_oov_words": 23,
+      "oov_targets": 3,
+      "oov_hits": 1,
+      "generation_failures": 0,
+      "generation_context_hits": 0,
+      "current_top3_hits": 17,
+      "current_top6_hits": 17,
+      "statistical_six_top3_hits": 12,
+      "statistical_six_top6_hits": 14,
+      "six_slots_top3_hits": 12,
+      "six_slots_top6_hits": 30,
+      "regressions_vs_statistical_six": 0,
+      "gains_vs_statistical_six": 16,
+      "regressions_vs_current": 0,
+      "current_top3_accuracy": 0.4594594594594595,
+      "current_top6_accuracy": 0.4594594594594595,
+      "statistical_six_top3_accuracy": 0.32432432432432434,
+      "statistical_six_top6_accuracy": 0.3783783783783784,
+      "six_slots_top3_accuracy": 0.32432432432432434,
+      "six_slots_top6_accuracy": 0.8108108108108109,
+      "neural_slot_fill_rate": 1.0
+    },
+    "regression/documents/3": {
+      "queries": 36,
+      "generated_words": 98,
+      "filled_neural_queries": 34,
+      "generated_oov_words": 47,
+      "oov_targets": 5,
+      "oov_hits": 4,
+      "generation_failures": 0,
+      "generation_context_hits": 0,
+      "current_top3_hits": 21,
+      "current_top6_hits": 21,
+      "statistical_six_top3_hits": 15,
+      "statistical_six_top6_hits": 20,
+      "six_slots_top3_hits": 15,
+      "six_slots_top6_hits": 32,
+      "regressions_vs_statistical_six": 1,
+      "gains_vs_statistical_six": 13,
+      "regressions_vs_current": 1,
+      "current_top3_accuracy": 0.5833333333333334,
+      "current_top6_accuracy": 0.5833333333333334,
+      "statistical_six_top3_accuracy": 0.4166666666666667,
+      "statistical_six_top6_accuracy": 0.5555555555555556,
+      "six_slots_top3_accuracy": 0.4166666666666667,
+      "six_slots_top6_accuracy": 0.8888888888888888,
+      "neural_slot_fill_rate": 0.9074074074074074
+    },
+    "regression/documents/4": {
+      "queries": 36,
+      "generated_words": 101,
+      "filled_neural_queries": 36,
+      "generated_oov_words": 56,
+      "oov_targets": 4,
+      "oov_hits": 4,
+      "generation_failures": 0,
+      "generation_context_hits": 0,
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+      "neural_slot_fill_rate": 1.0
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+      "queries": 8,
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+      "generated_oov_words": 8,
+      "oov_targets": 0,
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+      "six_slots_top3_accuracy": 0.25,
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+      "neural_slot_fill_rate": 1.0
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+      "six_slots_top3_accuracy": 0.0,
+      "six_slots_top6_accuracy": 0.0,
+      "neural_slot_fill_rate": 1.0
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+      "queries": 10,
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+      "generated_oov_words": 3,
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+      "statistical_six_top6_accuracy": 0.5,
+      "six_slots_top3_accuracy": 0.4,
+      "six_slots_top6_accuracy": 0.6,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/email/4": {
+      "queries": 11,
+      "generated_words": 23,
+      "filled_neural_queries": 9,
+      "generated_oov_words": 15,
+      "oov_targets": 1,
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+      "current_top6_accuracy": 0.9090909090909091,
+      "statistical_six_top3_accuracy": 0.7272727272727273,
+      "statistical_six_top6_accuracy": 0.8181818181818182,
+      "six_slots_top3_accuracy": 0.7272727272727273,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.696969696969697
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+    "test/email/1": {
+      "queries": 9,
+      "generated_words": 27,
+      "filled_neural_queries": 9,
+      "generated_oov_words": 1,
+      "oov_targets": 0,
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+      "generation_failures": 0,
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+      "statistical_six_top3_hits": 2,
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+      "six_slots_top3_hits": 2,
+      "six_slots_top6_hits": 6,
+      "regressions_vs_statistical_six": 0,
+      "gains_vs_statistical_six": 3,
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+      "statistical_six_top3_accuracy": 0.2222222222222222,
+      "statistical_six_top6_accuracy": 0.3333333333333333,
+      "six_slots_top3_accuracy": 0.2222222222222222,
+      "six_slots_top6_accuracy": 0.6666666666666666,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/email/3": {
+      "queries": 7,
+      "generated_words": 19,
+      "filled_neural_queries": 7,
+      "generated_oov_words": 7,
+      "oov_targets": 0,
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+      "current_top6_accuracy": 0.8571428571428571,
+      "statistical_six_top3_accuracy": 0.42857142857142855,
+      "statistical_six_top6_accuracy": 0.8571428571428571,
+      "six_slots_top3_accuracy": 0.42857142857142855,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.9047619047619048
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+    "test/messages/1": {
+      "queries": 8,
+      "generated_words": 24,
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+      "generated_oov_words": 1,
+      "oov_targets": 0,
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+      "statistical_six_top3_accuracy": 0.125,
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+      "six_slots_top3_accuracy": 0.125,
+      "six_slots_top6_accuracy": 0.625,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/messages/3": {
+      "queries": 10,
+      "generated_words": 27,
+      "filled_neural_queries": 9,
+      "generated_oov_words": 10,
+      "oov_targets": 0,
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+      "six_slots_top3_accuracy": 0.7,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.9
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+    "test/messages/0": {
+      "queries": 10,
+      "generated_words": 30,
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+      "statistical_six_top6_accuracy": 0.1,
+      "six_slots_top3_accuracy": 0.0,
+      "six_slots_top6_accuracy": 0.0,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/messages/2": {
+      "queries": 9,
+      "generated_words": 27,
+      "filled_neural_queries": 9,
+      "generated_oov_words": 3,
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+      "statistical_six_top6_accuracy": 0.5555555555555556,
+      "six_slots_top3_accuracy": 0.3333333333333333,
+      "six_slots_top6_accuracy": 0.5555555555555556,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/messages/4": {
+      "queries": 8,
+      "generated_words": 12,
+      "filled_neural_queries": 4,
+      "generated_oov_words": 5,
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+      "six_slots_top3_accuracy": 1.0,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.5
+    },
+    "test/search/0": {
+      "queries": 8,
+      "generated_words": 24,
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+      "generated_oov_words": 0,
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+      "six_slots_top3_accuracy": 0.125,
+      "six_slots_top6_accuracy": 0.5,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/search/2": {
+      "queries": 9,
+      "generated_words": 27,
+      "filled_neural_queries": 9,
+      "generated_oov_words": 9,
+      "oov_targets": 0,
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+      "six_slots_top3_accuracy": 0.2222222222222222,
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+      "neural_slot_fill_rate": 1.0
+    },
+    "test/search/4": {
+      "queries": 9,
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+      "six_slots_top3_accuracy": 0.6666666666666666,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.7777777777777778
+    },
+    "test/search/1": {
+      "queries": 10,
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+      "filled_neural_queries": 10,
+      "generated_oov_words": 4,
+      "oov_targets": 0,
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+      "gains_vs_statistical_six": 5,
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+      "current_top3_accuracy": 0.1,
+      "current_top6_accuracy": 0.1,
+      "statistical_six_top3_accuracy": 0.1,
+      "statistical_six_top6_accuracy": 0.1,
+      "six_slots_top3_accuracy": 0.1,
+      "six_slots_top6_accuracy": 0.6,
+      "neural_slot_fill_rate": 1.0
+    },
+    "test/search/3": {
+      "queries": 9,
+      "generated_words": 24,
+      "filled_neural_queries": 8,
+      "generated_oov_words": 13,
+      "oov_targets": 0,
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+      "six_slots_top6_hits": 9,
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+      "gains_vs_statistical_six": 3,
+      "regressions_vs_current": 0,
+      "current_top3_accuracy": 0.6666666666666666,
+      "current_top6_accuracy": 0.6666666666666666,
+      "statistical_six_top3_accuracy": 0.5555555555555556,
+      "statistical_six_top6_accuracy": 0.6666666666666666,
+      "six_slots_top3_accuracy": 0.5555555555555556,
+      "six_slots_top6_accuracy": 1.0,
+      "neural_slot_fill_rate": 0.8888888888888888
+    }
+  },
+  "baseline_sha256": "222253417d0a7a705823ffb7e599a3bcf5d5d3daf4a9d76161ac6b3e555aeaad",
+  "worker_sha256": "c968323826413de70108e70a94bf1bd5554cff7db4708035590b1b99f758581e",
+  "accelerated_worker_sha256": null,
+  "fixture_hashes": {
+    "regression.json": "5e7fe93fc126862da5895d6c7304414c2a49a48303688648349afb3db956fe76",
+    "general-writing.json": "315cb0dacf6026d752c60482915d2dae17ff8842a77290325c6b5f780769fee9"
+  },
+  "note": "Regression comparison only; fixture overlap with model pretraining is unknown. Current mode returns at most five words. Explicit benchmark retries counted; production never retries automatically.",
+  "production_qualified": false
+}
diff --git a/docs/measurements/six-predictions/generation-windows.json b/docs/measurements/six-predictions/generation-windows.json
new file mode 100644
index 00000000..eb8a56e1
--- /dev/null
+++ b/docs/measurements/six-predictions/generation-windows.json
@@ -0,0 +1,1620 @@
+{
+  "schema": 1,
+  "policy": {
+    "context_tokens": 64,
+    "shortlist": 8,
+    "max_results": 5,
+    "scoring": "whole-word log probability plus boundary probability, sequential",
+    "generation": {
+      "protocol_version": 2,
+      "beam_width": 8,
+      "max_tokens_per_word": 8,
+      "max_forward_evaluations": 64,
+      "max_results": 3,
+      "max_word_bytes": 128,
+      "reply_deadline_ms": 2000,
+      "scoring": "whole-word probability including following boundary; normalized duplicates excluded",
+      "minimum_boundary_probability": 0.5,
+      "search_cutoff_ms": 1600
+    }
+  },
+  "platform": "Windows-11-10.0.26200-SP0",
+  "queries": 1000,
+  "warmup_queries": 20,
+  "warmup_failures": 0,
+  "failures": {},
+  "latency": {
+    "immediate_with_ipc": {
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+      "median_ms": 0.7227000023704022,
+      "p95_ms": 8.238699985668063,
+      "max_ms": 15.401700016809627
+    },
+    "generation_with_ipc": {
+      "samples": 1000,
+      "median_ms": 1152.2917000111192,
+      "p95_ms": 1388.2117999892216,
+      "max_ms": 1671.0320000129286
+    }
+  },
+  "cold_load_ms": 4366.459100012435,
+  "peak_process_tree_rss_bytes": 690114560,
+  "capabilities": {
+    "accelerated": false,
+    "context_tokens": 64,
+    "deadline_ms": 2000,
+    "shortlist": 8,
+    "threads": 4
+  },
+  "cells": {
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+      "generated_words": 111,
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+      "generated_oov_words": 4,
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+      "statistical_six_top6_hits": 4,
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+      "six_slots_top3_accuracy": 0.02702702702702703,
+      "six_slots_top6_accuracy": 0.2972972972972973,
+      "neural_slot_fill_rate": 1.0
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+    "regression/documents/1": {
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+      "filled_neural_queries": 36,
+      "generated_oov_words": 7,
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+  "accelerated_worker_sha256": null,
+  "fixture_hashes": {
+    "regression.json": "5e7fe93fc126862da5895d6c7304414c2a49a48303688648349afb3db956fe76",
+    "general-writing.json": "315cb0dacf6026d752c60482915d2dae17ff8842a77290325c6b5f780769fee9"
+  },
+  "note": "Regression comparison only; fixture overlap with model pretraining is unknown. Current mode returns at most five words. Explicit benchmark retries counted; production never retries automatically.",
+  "production_qualified": false
+}
diff --git a/docs/measurements/six-predictions/integration-windows.json b/docs/measurements/six-predictions/integration-windows.json
new file mode 100644
index 00000000..bd8d7f1d
--- /dev/null
+++ b/docs/measurements/six-predictions/integration-windows.json
@@ -0,0 +1,36 @@
+{
+  "arch": "x86_64",
+  "failures_including_warmup": 0,
+  "generated_words": 3000,
+  "immediate": {
+    "max_ms": 15.2479,
+    "median_ms": 8.3391,
+    "p95_ms": 9.3506,
+    "samples": 1000
+  },
+  "maximum_sessions": 5,
+  "neural_slot_fill_rate": 1.0,
+  "os": "windows",
+  "policy": "three instant plus three generated optional slots",
+  "production_qualified": false,
+  "queries": 1000,
+  "refinement": {
+    "max_ms": 1034.8055,
+    "median_ms": 496.2131,
+    "p95_ms": 536.6225,
+    "samples": 1000
+  },
+  "retry_policy": "Benchmark simulates explicit keyboard reopenings after failure; production never retries automatically",
+  "scope": "Production Engine and model adapter, real neural child IPC, 20ms polling, fake input/activity; excludes outer desktop pipe and rendering",
+  "sessions": [
+    {
+      "failed_during_warmup": false,
+      "neural_status": "Ready",
+      "session": 1,
+      "successful_refinements": 1000,
+      "warmup_completed": 20
+    }
+  ],
+  "process_tree_peak_rss_bytes": 695799808,
+  "memory_note": "20ms sampled sum of test process and child RSS; shared pages may be counted twice and short peaks missed."
+}
diff --git a/docs/six-predictions.md b/docs/six-predictions.md
index c403ffff..17f0faf8 100644
--- a/docs/six-predictions.md
+++ b/docs/six-predictions.md
@@ -34,3 +34,61 @@ deadline remains two seconds.
 Use switchify-prediction scripts/generation_evaluate.py for the frozen corpus
 comparison. Existing model quality regressions remain. Neither default activation
 nor a successful performance run establishes unseen-data accuracy.
+
+## Initial comparison
+
+A deterministic 1,000-query sample of the 1,760-query frozen development/test
+workload produced the following results. These runs measured implementation
+70fc4a8; final companion c25f068 changes release versions and documentation only.
+Generation starts with a fresh context cache for each measured query.
+
+| Metric | Windows portable | macOS ARM |
+| --- | ---: | ---: |
+| Existing reranker top-three | 50.1% | 50.3% |
+| Existing reranker top-six, at most five returned | 51.8% | 52.0% |
+| Six statistical top-three | 39.9% | 39.9% |
+| Six statistical top-six | 48.2% | 48.2% |
+| Six-slot top-three | 39.9% | 39.9% |
+| Six-slot top-six | 72.6% | 72.7% |
+| Neural slot fill | 93.5% | 93.4% |
+| Correct OOV query instances | 29/63 | 29/63 |
+| Gains / regressions against six statistical | 260 / 16 | 260 / 15 |
+| Generation median / p95 | 1152 / 1388 ms | 349 / 767 ms |
+| Generation maximum | 1671 ms | 1701 ms |
+| Peak process-tree RSS | 658 MiB | 862 MiB |
+
+Both runs completed 1,000 generation queries without generation failures. The
+macOS comparison reranker failed once and was explicitly retried. Windows had
+no failures. Local Windows development checks overlapped part of measurement;
+final CI repeats qualification on dedicated runners. The comparison covers a
+sample of the frozen workload, not the whole corpus. Generated OOV occurrences
+were 702 and 701 respectively, counting repeated query occurrences.
+
+Keeping the statistical first three sacrifices the previous reranker's
+first-three accuracy. Total top-six accuracy improves on this sample, but some
+queries regress. The existing quality qualification failures remain; unknown
+training overlap prevents claims about unseen-data accuracy.
+
+Reproduce in the companion checkout with Python dependencies psutil==7.0.0 and
+regex==2025.11.3, using explicit paths to the prepared offline assets:
+
+```text
+python scripts/generation_evaluate.py --cli <companion-cli> --baseline <english.sqlite> --bundle <model-directory> --worker <portable-worker> --samples 1000 --output <report.json>
+```
+
+Omit --samples to evaluate all 1,760 queries. The companion CI uploads platform
+JSON reports as generation-windows-latest and generation-macos-latest. Desktop
+CI separately checks packaged resources and measures the production integration
+with fake input adapters. Those measurements are distinct from CLI generation.
+
+The extracted Windows installer also completed 1,000 production-engine queries
+with its optimized worker and no failures or retries. Immediate median/p95/max
+were 8.34/9.35/15.25 ms; generation was 496/537/1035 ms. All 3,000 neural slots
+filled on this synthetic fixture, and peak process-tree RSS was 664 MiB. This
+measurement includes the engine, model adapter and child IPC with 20 ms polling.
+It excludes the outer desktop pipe and rendering and does not establish corpus
+accuracy. Installer contents and offline loading were verified separately.
+
+The measured JSON reports are checked in under docs/measurements/six-predictions.
+Their worker and corpus hashes identify the exact inputs. Final-head CI reports
+are additional evidence and may vary with runner hardware and timing cutoffs.
diff --git a/scripts/prediction-neural.json b/scripts/prediction-neural.json
index 844bbfee..61fe36bd 100644
--- a/scripts/prediction-neural.json
+++ b/scripts/prediction-neural.json
@@ -1,5 +1,5 @@
 {
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+  "revision": "9ec55f767ca6e7e97a775df0bf0dc8d4be42c709",
   "bundle": {
     "format_version": 1,
     "model_id": "smollm2-135m-q8-v1",
@@ -76,77 +76,77 @@
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       },
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+      "archive_sha256": "c8fb614e53c4656bd1eeb808552a7f51c9da09b45ba77687b7f07e7775b2f479",
       "files": {
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/BUILD.json": {
-          "sha256": "1440f4897ac396ff4b3ef11baf963042c97e9113b5177fd4cac811e598e60240",
-          "size": 1051
+          "size": 1051,
+          "sha256": "29b25c63447c59ada1b7ed8549905fb9735540b2d698e6a5d4c3ea9857fdeb3d"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/RUST_LIBRARY_COPYRIGHT.html": {
-          "sha256": "0a65bb747c49c7bb816cbc7188319bd6e4e8d08091c1190b8a3c0971c47968ed",
-          "size": 279302
+          "size": 279302,
+          "sha256": "0a65bb747c49c7bb816cbc7188319bd6e4e8d08091c1190b8a3c0971c47968ed"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/switchify-smol-worker": {
-          "sha256": "3dba4916ff39f2779e80906aaf8c1aac5270e746f4bc7d3ecef4e0532915977a",
-          "size": 10153664
+          "size": 10154408,
+          "sha256": "d537aff0606ac07289ae2dae69dab503a090be9d5d2248919e0fb91bf7c29df3"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/SHA256SUMS": {
-          "sha256": "678af020c062ada94dfcc4403efb972955e574d34ccbef35760e205f012196d5",
-          "size": 1091
+          "size": 1091,
+          "sha256": "2d6d80a8edb6bb0b5c03787b3218c4e76cb5bf963a3a8f636f0a3a26076d576c"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/LICENSE": {
-          "sha256": "1e5643d5431cc5b2d04534dee7b3f716b24fc3b82493a1659534cbfa4fc8732c",
-          "size": 1079
+          "size": 1079,
+          "sha256": "1e5643d5431cc5b2d04534dee7b3f716b24fc3b82493a1659534cbfa4fc8732c"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/switchify-smol-worker-avx2": {
-          "sha256": "737e8e4804e58c5b40676d4ca9237b02913682b63ce9679ed4416326276c3259",
-          "size": 10246512
+          "size": 10245944,
+          "sha256": "2a75322f581d8bfdad17094d36fd1e529effcdf03d482a6a7d876eaf9c4cd91e"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/SECURITY.md": {
-          "sha256": "d79bf51500d3927bc47fad3e61b987be10764c7003b82df0f9d1f752f72a5182",
-          "size": 1973
+          "size": 1973,
+          "sha256": "d79bf51500d3927bc47fad3e61b987be10764c7003b82df0f9d1f752f72a5182"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/verify_bundle.py": {
-          "sha256": "69c124d037d9e25018f690c10b861c4388c01a8cd8eb3ed438d17136196c1194",
-          "size": 1282
+          "size": 1282,
+          "sha256": "69c124d037d9e25018f690c10b861c4388c01a8cd8eb3ed438d17136196c1194"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/Cargo.lock": {
-          "sha256": "a49ecb4d18fa6f26d8777701e2bbad87691fff4ec05664e5a746bf18580673b0",
-          "size": 46262
+          "size": 46262,
+          "sha256": "1ca440eb3b6acbe7a5b40ebc39bf2db2fb761ab5592f6610fd1a61adf04f2ffe"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/README.md": {
-          "sha256": "02cf02f01d6e711cf6415304ace910722cb76268e90358c5362feaec034a090b",
-          "size": 8913
+          "size": 8954,
+          "sha256": "c1ffe05faa70725dfe43e54657b2088baec45fcd1356e5cc802ec89a4965c39b"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/THIRD_PARTY_NOTICES.md": {
-          "sha256": "93cad9c9282edec579015f6d822901ab48596ff21e9f74bb3f07dc2ae8b7314c",
-          "size": 1535176
+          "size": 1535176,
+          "sha256": "93cad9c9282edec579015f6d822901ab48596ff21e9f74bb3f07dc2ae8b7314c"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/QUALIFICATION.json": {
-          "sha256": "55b6e9429ecb290fd194682406773a6f07102861529d5e1c0e51a6d2d4fc8c7e",
-          "size": 473
+          "size": 714,
+          "sha256": "636ff7eabd1eec3cbfcf3d554d1908175cbae42d65330b7d8cf80bc4f0cbc509"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/QUALIFICATION.md": {
-          "sha256": "f5d13a7406728515112cab4da093ee19642f68abbb9f30c8c8f511ae68ec8eb3",
-          "size": 7086
+          "size": 7086,
+          "sha256": "f5d13a7406728515112cab4da093ee19642f68abbb9f30c8c8f511ae68ec8eb3"
         }
       }
     }
   },
-  "library_revision": "70fc4a869ffd22a405527e4d81d25c149aebc418"
+  "library_revision": "c25f068dacef8fbf65dde6b98c745ad4cd56d555"
 }
diff --git a/src-tauri/Cargo.lock b/src-tauri/Cargo.lock
index df67f9b3..b896925c 100644
--- a/src-tauri/Cargo.lock
+++ b/src-tauri/Cargo.lock
@@ -4216,8 +4216,8 @@ dependencies = [
 
 [[package]]
 name = "switchify-prediction"
-version = "0.2.0"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=70fc4a869ffd22a405527e4d81d25c149aebc418#70fc4a869ffd22a405527e4d81d25c149aebc418"
+version = "0.2.1"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=c25f068dacef8fbf65dde6b98c745ad4cd56d555#c25f068dacef8fbf65dde6b98c745ad4cd56d555"
 dependencies = [
  "clap",
  "rusqlite",
@@ -4233,7 +4233,7 @@ dependencies = [
 [[package]]
 name = "switchify-prediction-neural"
 version = "0.2.1"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=70fc4a869ffd22a405527e4d81d25c149aebc418#70fc4a869ffd22a405527e4d81d25c149aebc418"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=c25f068dacef8fbf65dde6b98c745ad4cd56d555#c25f068dacef8fbf65dde6b98c745ad4cd56d555"
 dependencies = [
  "clap",
  "serde",
diff --git a/src-tauri/Cargo.toml b/src-tauri/Cargo.toml
index 75d4cb79..9f93c2ec 100644
--- a/src-tauri/Cargo.toml
+++ b/src-tauri/Cargo.toml
@@ -14,8 +14,8 @@ crate-type = ["staticlib", "cdylib", "rlib"]
 tauri-build = { version = "2", features = [] }
 
 [dependencies]
-switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "70fc4a869ffd22a405527e4d81d25c149aebc418" }
-switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "70fc4a869ffd22a405527e4d81d25c149aebc418" }
+switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "c25f068dacef8fbf65dde6b98c745ad4cd56d555" }
+switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "c25f068dacef8fbf65dde6b98c745ad4cd56d555" }
 anyhow = "1"
 base64 = "0.22"
 directories = "6.0.0"

From 07ff11ed71425cfce3e6b6a8e3422686e6888c3a Mon Sep 17 00:00:00 2001
From: Owen McGirr <o.a.mcgirr@gmail.com>
Date: Sun, 4 Oct 2026 17:42:53 +0100
Subject: [PATCH 3/3] Pin published v0.2.1 companion assets

---
 .github/workflows/ci.yml       |  6 ++--
 docs/six-predictions.md        | 15 ++++----
 scripts/prediction-neural.json | 64 +++++++++++++---------------------
 src-tauri/Cargo.lock           |  4 +--
 src-tauri/Cargo.toml           |  4 +--
 5 files changed, 37 insertions(+), 56 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 5844375d..acba3e4d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -112,11 +112,9 @@ jobs:
           toolchain: 1.97.1
           components: rustfmt, clippy
       - run: npm ci
-      - name: Fetch prediction model and prepared companion workers
-        env:
-          GH_TOKEN: ${{ github.token }}
+      - name: Fetch prediction model and released companion workers
         run: npm run prediction-model
-      - name: Diagnose prepared ARM worker
+      - name: Diagnose released ARM worker
         if: runner.os == 'macOS'
         env:
           SWITCHIFY_BENCHMARK_MODEL: src-tauri/resources/prediction-model/english.sqlite
diff --git a/docs/six-predictions.md b/docs/six-predictions.md
index 17f0faf8..d895c76a 100644
--- a/docs/six-predictions.md
+++ b/docs/six-predictions.md
@@ -13,14 +13,13 @@ The companion uses protocol 2. Model and tokenizer bytes are unchanged. Startup
 remains bounded to 30 seconds and each reply to two seconds. Failure leaves the
 instant slots available, with explicit retry or keyboard reopening required.
 
-## Prepared assets
-
-The build can consume checksum-pinned companion CI archives before publication,
-using an authenticated GitHub CLI. Verified local caches work offline. These CI
-artifacts expire and are not permanent release assets. Before merging/releasing
-the desktop, separately authorize companion artifact publication and replace
-prepared artifact sources with the permanent release URLs for the same bytes.
-No runtime network access or model setup is needed.
+## Release assets
+
+The build downloads checksum-pinned v0.2.1 companion release archives from
+GitHub. Both libraries and worker provenance are pinned to the release commit.
+Verified local caches work offline, and installed applications need no network
+access or model setup. Model, tokenizer and statistical database bytes remain
+unchanged.
 
 ## Validation
 
diff --git a/scripts/prediction-neural.json b/scripts/prediction-neural.json
index 61fe36bd..fc73199f 100644
--- a/scripts/prediction-neural.json
+++ b/scripts/prediction-neural.json
@@ -1,5 +1,5 @@
 {
-  "revision": "9ec55f767ca6e7e97a775df0bf0dc8d4be42c709",
+  "revision": "2a2ed0c2b0013663ffa9317dba3389ec96b6f14b",
   "bundle": {
     "format_version": 1,
     "model_id": "smollm2-135m-q8-v1",
@@ -75,12 +75,7 @@
   },
   "workers": {
     "aarch64-apple-darwin": {
-      "prepared_artifact": {
-        "run_id": 37212539426,
-        "name": "neural-macos-latest",
-        "archive": "switchify-prediction-neural-0.2.1-aarch64-apple-darwin.zip"
-      },
-      "archive_sha256": "3976ac1274488b23438b9af299cfca64a9feffecfd1d4548a3ec0dc1b8681f8c",
+      "archive_sha256": "aaa633461090006311557e7b2a4b9876c225af29933501feb65afc4c8c048d49",
       "files": {
         "switchify-prediction-neural-0.2.1-aarch64-apple-darwin/QUALIFICATION.md": {
           "size": 7086,
@@ -112,7 +107,7 @@
         },
         "switchify-prediction-neural-0.2.1-aarch64-apple-darwin/SHA256SUMS": {
           "size": 998,
-          "sha256": "a95f16357fb6e6747ab2e50df2ef7cdbf09a2749019d7b762e592272089a1abd"
+          "sha256": "ba76f5b2bd7aa375164d1886548f913c0faf83cde9edb8f463c89877b59a3aea"
         },
         "switchify-prediction-neural-0.2.1-aarch64-apple-darwin/RUST_LIBRARY_COPYRIGHT.html": {
           "size": 279302,
@@ -124,21 +119,17 @@
         },
         "switchify-prediction-neural-0.2.1-aarch64-apple-darwin/BUILD.json": {
           "size": 987,
-          "sha256": "701bed567579160606ebf4ad203ebb5560a7a32093ed68194fa66bbad5de06d9"
+          "sha256": "efe9910c9f3de2f33846eb08cae659f5f01b12cadddd3e4121903239c4ff97e3"
         },
         "switchify-prediction-neural-0.2.1-aarch64-apple-darwin/SECURITY.md": {
           "size": 1973,
           "sha256": "d79bf51500d3927bc47fad3e61b987be10764c7003b82df0f9d1f752f72a5182"
         }
-      }
+      },
+      "url": "https://github.com/switchifyapp/switchify-prediction/releases/download/v0.2.1/switchify-prediction-neural-0.2.1-aarch64-apple-darwin.zip"
     },
     "x86_64-apple-darwin": {
-      "prepared_artifact": {
-        "run_id": 37212539426,
-        "name": "neural-macos-15-intel",
-        "archive": "switchify-prediction-neural-0.2.1-x86_64-apple-darwin.zip"
-      },
-      "archive_sha256": "c5df5cc3ff7732fa0ca4bd53b7dd5c9f5bcfb9531a721e727d9ecd3bb4cac78a",
+      "archive_sha256": "6882754360e1f854c96052d816cccbf1f93598ffc7b81f711ba2150cddbbd465",
       "files": {
         "switchify-prediction-neural-0.2.1-x86_64-apple-darwin/QUALIFICATION.md": {
           "size": 7086,
@@ -174,7 +165,7 @@
         },
         "switchify-prediction-neural-0.2.1-x86_64-apple-darwin/SHA256SUMS": {
           "size": 1091,
-          "sha256": "13b66da70c11dafffd1dbcad71aa9e803ad21ccf36d4f51b913e4fca9a361182"
+          "sha256": "d1b7a00190a833867cdf2924fc5835e2865c9c365b294130dab136535a71892d"
         },
         "switchify-prediction-neural-0.2.1-x86_64-apple-darwin/RUST_LIBRARY_COPYRIGHT.html": {
           "size": 279302,
@@ -186,25 +177,21 @@
         },
         "switchify-prediction-neural-0.2.1-x86_64-apple-darwin/BUILD.json": {
           "size": 1024,
-          "sha256": "e49098008cbcb1a968d6c2abb67ad5245fbb3891a32a768f8d3e5de3c4d2abf6"
+          "sha256": "d910e349135956cd1341383b1d1bd2a8b76fe0f5ea957776d54c8a94d2ec83ac"
         },
         "switchify-prediction-neural-0.2.1-x86_64-apple-darwin/SECURITY.md": {
           "size": 1973,
           "sha256": "d79bf51500d3927bc47fad3e61b987be10764c7003b82df0f9d1f752f72a5182"
         }
-      }
+      },
+      "url": "https://github.com/switchifyapp/switchify-prediction/releases/download/v0.2.1/switchify-prediction-neural-0.2.1-x86_64-apple-darwin.zip"
     },
     "x86_64-pc-windows-msvc": {
-      "prepared_artifact": {
-        "run_id": 37212539426,
-        "name": "neural-windows-latest",
-        "archive": "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc.zip"
-      },
-      "archive_sha256": "175f3f9a8c021078c8bd1fefa1d6389afbdaec3f383cecdd14810ac8f6acba1a",
+      "archive_sha256": "5580543007f7e0cc8601685be195be6fb68215a6521bd5eb8864ac58590bad1c",
       "files": {
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/BUILD.json": {
           "size": 1059,
-          "sha256": "5a90cebe110f2711f9d262d8b0542dfebf56b407f019a6dc75bb0395fe2efd96"
+          "sha256": "b168a91658032969073ff350bec657113039905d9c5b64a5e0c6c7b442530bce"
         },
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/Cargo.lock": {
           "size": 46262,
@@ -236,15 +223,15 @@
         },
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/SHA256SUMS": {
           "size": 1116,
-          "sha256": "31d12eb51fce3223f4a3ed141cf388f186c592731127ba231149bab8d9008a1c"
+          "sha256": "117a918e874ac6fe4d473fd6e449e472205d327f5c7588abe12802d11b7e234d"
         },
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/switchify-smol-worker-avx2.exe": {
           "size": 8709120,
-          "sha256": "8e9efe60e59816f2cc5d4127b3e9ed47a8d5ca18675ea5c3fc527106471d0c32"
+          "sha256": "46d05730c283e7d6ca6d325611410c0b2196d0431e6cfe8b973cd61c24011d2b"
         },
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/switchify-smol-worker.exe": {
           "size": 8510464,
-          "sha256": "47123b4cc20002f7c2d6fa369a9ce994cf1b1a1939d03318d1c1088ed1865783"
+          "sha256": "d6087919a6704a673ab6f4609e87a113e3f59a4bba692a9be4cd1f7bcdc2dc69"
         },
         "switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc/THIRD_PARTY_NOTICES.md": {
           "size": 1552635,
@@ -254,19 +241,15 @@
           "size": 1315,
           "sha256": "96b47d695b104c61ca65de856108d8850a6da7c00220ae72dd91b8e73cb8f6f2"
         }
-      }
+      },
+      "url": "https://github.com/switchifyapp/switchify-prediction/releases/download/v0.2.1/switchify-prediction-neural-0.2.1-x86_64-pc-windows-msvc.zip"
     },
     "x86_64-unknown-linux-gnu": {
-      "prepared_artifact": {
-        "run_id": 37212539426,
-        "name": "neural-ubuntu-latest",
-        "archive": "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu.zip"
-      },
-      "archive_sha256": "c8fb614e53c4656bd1eeb808552a7f51c9da09b45ba77687b7f07e7775b2f479",
+      "archive_sha256": "7411a6773589f7e28975e6754a6a48e893808b5c3953252a9251bdec792474bd",
       "files": {
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/BUILD.json": {
           "size": 1051,
-          "sha256": "29b25c63447c59ada1b7ed8549905fb9735540b2d698e6a5d4c3ea9857fdeb3d"
+          "sha256": "aad2210a773e9979ceae63e668f91c10db49d7906f90f274eed474d6124c0c35"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/RUST_LIBRARY_COPYRIGHT.html": {
           "size": 279302,
@@ -278,7 +261,7 @@
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/SHA256SUMS": {
           "size": 1091,
-          "sha256": "2d6d80a8edb6bb0b5c03787b3218c4e76cb5bf963a3a8f636f0a3a26076d576c"
+          "sha256": "589c2588753eb078508c447dd374dd1da4a34a0d62d6e824a9adffc1c01cda69"
         },
         "switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu/LICENSE": {
           "size": 1079,
@@ -316,8 +299,9 @@
           "size": 7086,
           "sha256": "f5d13a7406728515112cab4da093ee19642f68abbb9f30c8c8f511ae68ec8eb3"
         }
-      }
+      },
+      "url": "https://github.com/switchifyapp/switchify-prediction/releases/download/v0.2.1/switchify-prediction-neural-0.2.1-x86_64-unknown-linux-gnu.zip"
     }
   },
-  "library_revision": "c25f068dacef8fbf65dde6b98c745ad4cd56d555"
+  "library_revision": "2a2ed0c2b0013663ffa9317dba3389ec96b6f14b"
 }
diff --git a/src-tauri/Cargo.lock b/src-tauri/Cargo.lock
index b896925c..96d3c084 100644
--- a/src-tauri/Cargo.lock
+++ b/src-tauri/Cargo.lock
@@ -4217,7 +4217,7 @@ dependencies = [
 [[package]]
 name = "switchify-prediction"
 version = "0.2.1"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=c25f068dacef8fbf65dde6b98c745ad4cd56d555#c25f068dacef8fbf65dde6b98c745ad4cd56d555"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=2a2ed0c2b0013663ffa9317dba3389ec96b6f14b#2a2ed0c2b0013663ffa9317dba3389ec96b6f14b"
 dependencies = [
  "clap",
  "rusqlite",
@@ -4233,7 +4233,7 @@ dependencies = [
 [[package]]
 name = "switchify-prediction-neural"
 version = "0.2.1"
-source = "git+https://github.com/switchifyapp/switchify-prediction?rev=c25f068dacef8fbf65dde6b98c745ad4cd56d555#c25f068dacef8fbf65dde6b98c745ad4cd56d555"
+source = "git+https://github.com/switchifyapp/switchify-prediction?rev=2a2ed0c2b0013663ffa9317dba3389ec96b6f14b#2a2ed0c2b0013663ffa9317dba3389ec96b6f14b"
 dependencies = [
  "clap",
  "serde",
diff --git a/src-tauri/Cargo.toml b/src-tauri/Cargo.toml
index 9f93c2ec..f37b5cbe 100644
--- a/src-tauri/Cargo.toml
+++ b/src-tauri/Cargo.toml
@@ -14,8 +14,8 @@ crate-type = ["staticlib", "cdylib", "rlib"]
 tauri-build = { version = "2", features = [] }
 
 [dependencies]
-switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "c25f068dacef8fbf65dde6b98c745ad4cd56d555" }
-switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "c25f068dacef8fbf65dde6b98c745ad4cd56d555" }
+switchify-prediction = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "2a2ed0c2b0013663ffa9317dba3389ec96b6f14b" }
+switchify-prediction-neural = { git = "https://github.com/switchifyapp/switchify-prediction", rev = "2a2ed0c2b0013663ffa9317dba3389ec96b6f14b" }
 anyhow = "1"
 base64 = "0.22"
 directories = "6.0.0"