anticipation-geometry

Bridges Princeton's KG-path reward function (arXiv:2603.14147) with Comp-Core's anticipation geometry. Provides domain-general anticipation scalars that work on any trajectory: motion vectors, conversation embeddings, knowledge graph paths, or task planning traces. | Question | Princeton (KG Reward) | Comp-Core (Anticipation Geometry) | |----------|----------------------|-----------------------------------| | What it evaluates | Path validity (retrospective) | Path dynamics (prospective) | | Core question | "Was this path correct?" | "Where is this path going?" | | Input | Completed KG path | Ongoing state trajectory | | Output | Composite reward score | 4 scalar fields per timestep | | Strength | Hard correctness signal | Early warning / steering signal | Combined, they enable: early detection of invalid reasoning, identification of exploration opportunities, and optimal steering at decision points. **Uncertainty** u(t) = H(angular distribution of KNN displacement vectors) / H_max - `numpy` (required) - `requests` (required, for GK + Supabase queries) - `sentence-transformers` (optional, for conversation embeddings; falls back to hash embedding)