Theta Consolidation

ThetaAgent executes a deterministic consolidation pipeline for cross-temporal knowledge linking. Named after theta rhythms in hippocampal replay (Zhang et al., 2015), it compresses temporal experience into durable knowledge connections.

The agent operates in two phases: SITREP (situational awareness) and Consolidation (cross-temporal linking).

Phase 1: SITREP

The /sitrep command generates a structured SituationReport through three components:

Horizons — Time-based attention windows inspired by hippocampal theta waves:

AttentionSchema — Models what information is currently attended to. Targets have priority (0.0–1.0) and decay rates. Active targets above a threshold are included in the report.

SituationReport — Aggregates objectives, recent thoughts, agenda items, current events, and capability notes into a single markdown-formatted output.

Phase 2: Consolidation

Stage 1: Temporal Slicing

Documents are organized into weekly slices (YYYY-WNN). Each slice represents a temporal unit for consolidation.

Stage 2: NVAR Dynamics

Nonlinear Vector AutoRegression via reservoir computing (Gauthier et al., 2021) computes a consolidation urgency signal from embedding centroid trajectories.

Given slice centroids c_1,…,c_t in R^768:

• NVAR predicts c_hat_{t+1} using a polynomial basis over delayed embeddings
• Drift = ||c_hat_{t+1} - c_t||_2
• Urgency = sigmoid(alpha * drift)

High urgency indicates rapid semantic drift — the knowledge base is changing faster than consolidation is linking it.

Stage 3: BERTSubs Inference

Subsumption relationships (A is-a B) between concepts are inferred using BERTSubs (Chen et al., 2023) from DeepOnto. The inferencer classifies candidate concept pairs via fine-tuned BERT on rdfs:subClassOf axioms from an OWL domain ontology. Requires JVM (via JPype) and sufficient class count (~50+ classes).

Stage 4: Knowledge Gradient Selection

Candidate relationships are filtered using the Knowledge Gradient policy (Powell & Ryzhov, 2012). KG balances exploration (uncertain candidates) against exploitation (high-confidence relationships):

KG(x | S) = E[max_{x’} mu_{n+1}(x’) | S_n=S, x_n=x] - max_{x’} mu_n(x’)

Only candidates whose expected improvement exceeds a cost threshold are selected. The BeliefState tracks prior value and observation variance for each candidate.

Stage 5: Document Augmentation

Selected relationships are injected into source documents as:

• Wikilinks: [[Target Document]] for navigation
• Action links: [action:search "query"] for deferred execution

Effectiveness Tracking

The EffectivenessTracker measures augmentation impact by recording whether users follow injected links. When SHAP is available, feature attribution analysis identifies which augmentation types and document characteristics predict user engagement.

CLI Commands

# Situational report
uv run gaius-cli --cmd "/sitrep" --format json

# Run consolidation
uv run gaius-cli --cmd "/theta consolidate" --format json

# View consolidation stats
uv run gaius-cli --cmd "/theta stats" --format json