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neusym-bridge
Symbolic causal structure out of neural representations.
A neurosymbolic bridge: detecting shared structure across neural networks (CKA, Procrustes) and extracting causal structure expressible as symbolic rules.
PythonUpdated September 2, 2026
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Documentation
neusym-bridge
Neurosymbolic bridge: extracting symbolic causal structure from neural network common representations.
Project Structure
neusym-bridge/
├── configs/ # Experiment configurations
├── docs/results/ # Experiment reports and archived results
├── rules/ # Relatum rule files
├── scripts/ # Phase orchestration scripts
│ ├── run_phase0.py # Data generation + model training
│ ├── run_phase1.py # Common structure detection (CKA, Procrustes)
│ ├── run_phase2.py # Physics correspondence (SVCCA, SINDy)
│ └── run_phase3.py # Collapse mechanism verification
├── src/neusym_bridge/
│ ├── data/ # PDE solvers and data generation
│ ├── models/ # CNN world model + training
│ ├── analysis/ # Representation analysis + structure extraction
│ └── relatum/ # Probabilistic collapse mechanism
└── tests/ # 46 tests
Validation Phases
| Phase | Status | Key Result |
|---|---|---|
| 0: Environment | PASS | 3 models converge (ratio=1.006) |
| 1: Common Structure | PASS 4/5 | CKA=0.944 across 3 CNN models |
| 2: Physics Correspondence | Conditional | SVCCA=0.999, 3 causal directions found |
| 3: Collapse Mechanism | PASS 6/6 | Collapse, retraction, active query verified |