Trajectory Cognition (TCog) is an experimental framework for turning static knowledge into an active, frame-aware cognitive architecture.
Instead of treating AI reasoning as isolated query-response events, TCog organizes knowledge into basis packages: structured bundles of anchored units, clusters, constraints, trajectories, relations, and tests. A TCog system does not merely retrieve relevant text. It asks:
- Which frame is active?
- Which clusters fire?
- Which constraints govern the answer?
- Which trajectory should structure the reasoning?
- Which claims are unsupported, misframed, or inadmissible?
Try the current TCog-R demo here:
TCog-R stands for:
Package-Bound Mechanical Retrieval for Frame-Aware LLM Reasoning
The demo runs in the browser. Package routing, cluster activation, constraint checking, trajectory matching, and frame-overreach detection are performed mechanically. LLM composition is optional and only used to phrase the result after TCog-R has already produced the retrieval/appraisal trace.
