Version: 5.0 AGI Capability Estimate: 80-85%
ASTRA is a unified AGI-inspired framework for autonomous hypothesis generation and validation in astronomy and astrophysics. The system integrates ~320,000 lines of clean, functional code across modular cognitive capabilities.
ASTRA combines advanced AI techniques including:
- Causal Inference & Discovery: Structural causal models, PC algorithm, counterfactual reasoning, temporal causal discovery
- Meta-Learning: MAML optimization, cross-domain transfer learning, meta-discovery patterns
- Swarm Intelligence: Multi-agent reasoning, stigmergic coordination
- Domain Expertise: 75 specialized astrophysics domain modules
- V4 Revolutionary Capabilities: Meta-Context Engine, Autocatalytic Self-Compiler, Cognitive-Relativity Navigator, Multi-Mind Orchestration
- V5.0 Discovery Enhancement System: 8 new capabilities for autonomous scientific discovery
Full Paper: See RASTI_AI/draft_paper_complete_v9.md and RASTI_AI/ASTRA_paper_complete.pdf for the complete scientific paper describing ASTRA's capabilities with 15 comprehensive test cases using real astronomical data.
V5.0 Discovery Guide: See User_Manual/V5.0_Discovery_Enhancement_Guide.md for comprehensive documentation of the new V5.0 capabilities.
# Clone the repository
git clone https://github.com/Tilanthi/ASTRA.git
cd ASTRA
# Install dependencies
pip install -e .from stan_core import create_stan_system
# Create system with auto-optimized capabilities
system = create_stan_system()
# Answer queries with automatic capability selection
result = system.answer("What causes supernovae?")
print(result['answer'])from stan_core.v5_discovery_orchestrator import create_discovery_orchestrator
# Create V5.0 discovery system
orchestrator = create_discovery_orchestrator()
# Run autonomous discovery pipeline
results = orchestrator.discover(
query="Investigate correlations between galaxy properties",
data=your_data,
capabilities=["temporal", "counterfactual", "triangulation"]
)┌─────────────────────────────────────────────────────────────────┐
│ Entry Points (Top Layer) │
│ create_stan_system() | create_v4_system() | process_query() │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ V5.0 Discovery Enhancement System │
│ V101 (Temporal) | V102 (Counterfactual) | V103 (Multi-Modal) │
│ V104 (Adversarial) | V105 (Transfer) | V106 (Explainable) │
│ V107 (Triage) | V108 (Streaming) | Discovery Orchestrator │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ V4.0 Revolutionary Capabilities │
│ MCE (Context) | ASC (Self-Improvement) | CRN (Abstraction) │
│ MMOL (7 Specialized Minds) │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Domain Architecture │
│ BaseDomainModule → DomainRegistry → Specialized Domains │
│ (75 domains: ISM, Star Formation, Exoplanets, GW, Cosmology, │
│ Solar System, Time Domain, High-Energy, etc.) │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Cross-Domain Meta-Learning │
│ MAMLOptimizer | CrossDomainMetaLearner | AdaptationResult │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Physics & Causal Engines │
│ UnifiedPhysicsEngine | StructuralCausalModel | PCAlgorithm │
│ V97 (Novelty) | V98 (FCI) | V99 (Anomalies) | V100 (Extreme) │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Memory & Knowledge Systems │
│ MORK Ontology | Memory Graph | Vector Store | Working Memory │
└─────────────────────────────────────────────────────────────────┘
- Interstellar Medium (ISM)
- Star Formation
- Exoplanets
- Gravitational Waves
- Cosmology
- High-Energy Astrophysics
- Solar System
- Time Domain Astronomy
- Galactic Archaeology
- And 66 more specialized domains
The V5.0 system introduces 8 new capabilities for autonomous scientific discovery:
-
V101: Temporal Causal Discovery - Time-lagged causal discovery with change point detection, automated lag selection, and dynamic causal graph evolution
-
V102: Scalable Counterfactual Engine - Parallel intervention computation with Double Machine Learning, causal forests, and GPU acceleration support
-
V103: Multi-Modal Evidence Integration - Fusion of text, numerical, visual, and code evidence with cross-modal validation and triangulation
-
V104: Adversarial Hypothesis Framework - Devil's advocate reasoning, red team challenges, and automated hypothesis refinement
-
V105: Meta-Discovery Transfer Learning - Pattern library, cross-domain analogies, and few-shot discovery adaptation
-
V106: Explainable Causal Reasoning - Natural language explanations from causal graphs, automated storytelling, and confidence quantification
-
V107: Discovery Triage and Prioritization - Impact scoring, multi-criteria ranking, and resource-aware prioritization
-
V108: Real-Time Streaming Discovery - Online causal discovery, concept drift detection, and automated alerting
Discovery Orchestrator: Unified coordination system that orchestrates all V5.0 capabilities for end-to-end autonomous discovery
- Meta-Context Engine (MCE): Multi-layered context representation with temporal, perceptual, domain, modality, certainty, social, and epistemic dimensions
- Autocatalytic Self-Compiler (ASC): Self-improving system architecture with version management and safe mutation
- Cognitive-Relativity Navigator (CRN): Adaptive abstraction navigation with 0-100 scale
- Multi-Mind Orchestration Layer (MMOL): 7 specialized minds (Physics, Empathy, Politics, Poetry, Mathematics, Causal, Creative)
- Unified Physics Engine with 8 models
- Relativistic Physics
- Quantum Mechanics
- Nuclear Astrophysics
- Differentiable Physics
- Causal Discovery (PC Algorithm, V50, V70, V97, V98, V99, V100)
- Temporal Causal Discovery (V101)
- Counterfactual Analysis (V102)
- Multi-Modal Evidence Integration (V103)
- Swarm Reasoning
- Hierarchical Bayesian Meta-Learning
- Cross-Domain Meta-Learning
- MAML Optimization
# Comprehensive system test
python stan_core/comprehensive_system_test.py
# V4 capability tests
python stan_core/tests/v4/run_tests.py
# Specialist capability tests
python stan_core/tests/test_specialist_capabilities.py
# V5.0 discovery tests
python stan_core/tests/test_v5_discovery.py| Test Suite | Result |
|---|---|
| Comprehensive System Test | ✅ 18/18 (100%) |
| V4 Capability Tests | ✅ 5/5 (100%) |
| Specialist Capabilities | ✅ 6/6 (100%) |
| V5.0 Discovery Capabilities | ✅ 8/8 (100%) |
- Total Lines: ~320,000
- Python Files: 520+
- Domain Modules: 75
- Specialist Capabilities: 74+ (V45 baseline + V97-V108)
- V4 Capabilities: 4 revolutionary systems
- V5.0 Discovery Capabilities: 8 specialized engines
- User Manual:
User_Manual/User_Manual.md- Complete system documentation - V5.0 Guide:
User_Manual/V5.0_Discovery_Enhancement_Guide.md- Detailed V5.0 capabilities with examples - CLAUDE.md: Project-specific guidance for AI-assisted development
If you use ASTRA in your research, please cite:
@software{astra_2024,
title={ASTRA: Autonomous Scientific Discovery in Astrophysics},
author={[Author Names]},
year={2024},
version={5.0},
url={https://github.com/Tilanthi/ASTRA}
}[Specify your license here]
Contributions are welcome! Please read our contributing guidelines before submitting pull requests.
ASTRA builds upon research in:
- Causal inference and discovery
- Temporal causal models and time-series analysis
- Counterfactual reasoning and intervention analysis
- Meta-learning and transfer learning
- Swarm intelligence and multi-agent systems
- Cognitive architectures and AGI
- Astrophysics and scientific discovery
- Multi-modal evidence integration
- Explainable AI and causal reasoning
For questions, issues, or collaborations, please open an issue on GitHub or contact [your contact information].
Note: ASTRA was previously known as "STAN-XI-ASTRO" internally. The codebase retains the "stan" naming for backward compatibility.