Theta Consolidation
ThetaAgent executes a deterministic consolidation pipeline for cross-temporal knowledge linking. Named after theta rhythms in hippocampal replay, it compresses temporal experience into durable knowledge connections.
Pipeline Stages
Temporal Slicing → NVAR Signal → BERTSubs Inference → KG Selection → Augmentation
1. Temporal Slicing
Documents are organized into weekly slices (YYYY-WNN format). Each slice represents a temporal context for consolidation.
2. NVAR Dynamics
Nonlinear Vector AutoRegression using reservoir computing computes a consolidation signal from embedding centroid trajectories. High “urgency” indicates rapid semantic drift requiring consolidation attention.
3. BERTSubs Inference
Subsumption relationships between concepts are inferred using BERTSubs from DeepOnto. The inferencer identifies “A is-a B” relationships via fine-tuned BERT classification on ontology subsumptions.
4. Knowledge Gradient Selection
Candidate relationships are filtered using the Knowledge Gradient policy, balancing exploration (learning about uncertain candidates) against exploitation (selecting high-confidence relationships).
5. Document Augmentation
Selected relationships are injected into source documents as wikilinks and action links for navigation.
Usage
# Run consolidation
uv run gaius-cli --cmd "/theta consolidate" --format json
# View consolidation stats
uv run gaius-cli --cmd "/theta stats" --format json
# Check situational report
uv run gaius-cli --cmd "/sitrep" --format json
Dependencies
- DeepOnto with JVM (via JPype) for BERTSubs
- OWL domain ontology with
rdfs:subClassOfaxioms - Sufficient class count (~50+ classes) for training data