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Design Notes

How It Works

Evidence (supports / attacks)
        ↓
   Belief Node
        ↓
 TruthState (TRUE / FALSE / BOTH / NEITHER)
        ↓
 Confidence + Temporal Decay
        ↓
    Agent API

TruthState uses Belnap's four-valued logic. Instead of overwriting on conflict, the system represents the contradiction explicitly with BOTH — then lets you resolve it with new evidence.

Evidence Ledger is append-only. Evidence is never deleted, only invalidated. Every belief carries a full justification chain: what supports it, what attacks it, and what has expired.

Temporal Decay degrades evidence weight by belief type:

BeliefType Half-life
FACT 365 days
PREFERENCE 90 days
INFERENCE 30 days
PREDICTION 3 days

Formal Model

MnemeBrain is grounded in two well-established theories from knowledge representation and belief revision:

  • Belnap four-valued logic (1977) — used to represent contradictory evidence without collapsing the belief system. Instead of overwriting, the system holds BOTH as a valid, stable state.
  • AGM belief revision (Alchourrón, Gärdenfors, Makinson, 1985) — defines how a rational agent updates beliefs when new evidence arrives, with minimal disturbance to existing knowledge.

TruthState is computed over the evidence ledger using Belnap's lattice:

TruthState ∈ { TRUE, FALSE, BOTH, NEITHER }

TRUE     — net supporting evidence dominates
FALSE    — net attacking evidence dominates
BOTH     — significant supporting AND attacking evidence (contradiction)
NEITHER  — insufficient evidence to determine

Confidence is derived from weighted, time-decayed evidence:

confidence = Σ(support_weight × decay(t)) / (Σ(support_weight × decay(t)) + Σ(attack_weight × decay(t)))

where decay(t) = 0.5 ^ (t / half_life) and half_life varies by belief type (3 days for PREDICTION → 365 days for FACT).

Belief ranking uses a composite score across three signals:

rank_score = 0.60 × similarity        # semantic relevance to query
           + 0.25 × confidence        # evidence strength
           + 0.15 × stability         # inverse of revision volatility

Stability is 1 / (1 + revision_count) — beliefs that have been revised frequently rank lower than beliefs that have been stable, even at equal confidence. This prevents contradicted high-confidence beliefs from polluting retrieval.

Revision policy follows AGM minimal change: when new evidence contradicts an existing belief, the system retracts the minimum set of evidence necessary to restore consistency. Pluggable policies (recency, confidence-weighted, entrenchment-based) determine selection order.

Counterfactual reasoning uses copy-on-write sandbox isolation: hypothetical evidence is applied to a forked belief graph, leaving the canonical state unchanged.

References

  • Belnap, N. D. (1977). A useful four-valued logic. In Modern Uses of Multiple-Valued Logic. Reidel.
  • Alchourrón, C. E., Gärdenfors, P., & Makinson, D. (1985). On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic, 50(2), 510–530.
  • Lewis, D. (1973). Counterfactuals. Harvard University Press.
  • Gutierrez, B. J., et al. (2024). HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models. NeurIPS 2024.