Overview
Companion Note
Unified View of the Resonance Framework
A synthesis document explaining how the three components interact and their theoretical foundations.
Adaptive World Model Evaluation
When is the model unreliable? How should it adapt? How long should we trust it?
3
Research Papers
100%
Open Source
R
Resonance Signal
The Resonance Framework
All components share a common signal: resonance (R) — the agreement between learned model predictions and physics-based priors.
R = exp(-||f_θ(x) - Φ(x)||² / σ²)
High R → Trust the model | Low R → Adapt, blend, or rebuild
Detection
When is the model unreliable? RBD detects distribution shift via model-physics resonance.
Adaptation
How should it adapt? HHA+MIT provides stress-based control with Adapt/Freeze/Rebuild decisions.
Horizon
How long to trust predictions? ARH+DH adapts evaluation horizons and discriminates noise from drift.
Publications
RBD
Resonance-Based Detection
Distribution Shift Detection via Model-Physics Agreement
R-weighted blending between model and physics predictions. When model and physics disagree, trust physics. When they agree, trust the model. Wrong physics gracefully degrades to baseline.
- +50.2% OOD improvement over baseline
- Graceful degradation with wrong physics
- No retraining required
HHA+MIT
Homeostatic Control & Model Invalidity
Stress-Based Adaptation with Adapt/Freeze/Rebuild Taxonomy
An agent that regulates learning via stress signals. HHA provides continuous control, MIT elevates stress into discrete epistemic decisions with 100% noise specificity.
- Three-regime taxonomy: Adapt/Freeze/Rebuild
- 100% noise specificity (0 false reconstructions)
- Panic Freeze protective mechanism
ARH+DH
Adaptive Horizons & Failure Diagnosis
Temporal Dynamics of World Model Evaluation
ARH adapts evaluation horizons based on confidence. DualHorizon discriminates noise from drift using sign structure analysis, validated on 25 real-world TCPD datasets.
- Confidence-modulated horizon (4-20 steps)
- 76% discrimination on TCPD benchmark
- Noise vs drift diagnosis
Open Source
# Central entry point
git clone https://github.com/TheCause/resonance-framework.git
# Individual components
git clone https://github.com/TheCause/resonance-based-detection.git # RBD
git clone https://github.com/TheCause/homeostatic-hamiltonian-agent.git # HHA+MIT
git clone https://github.com/TheCause/temporal-evaluation-framework.git # ARH+DH
# Run benchmarks (example: HHA+MIT)
cd homeostatic-hamiltonian-agent
pip install -r requirements.txt
python run_all_benchmarks.pyAll code is MIT licensed. Contributions welcome.
Contact
For questions about the research or collaboration opportunities: