Standard approaches (Sliding Windows or RAG) fail because they treat conversational history as either a flat string or a collection of isolated fragments.
We’ve developed the Compression & Memory Topology (CMT) framework (part of SIGMA Runtime v0.5.3) to move from probabilistic context to a deterministic semantic lattice.
The Architecture: Semantic Lattice vs. Linear Context
Instead of a chronological log, CMT transforms history into a self-organizing graph:
Rib Points: We implement periodic semantic condensation every 10–50 cycles. This extracts the "conceptual essence" into stable nodes, preventing the attention mechanism from being overwhelmed by noise.
The Anchor Buffer: Probabilistic recall often fails for low-signal/high-importance data (e.g., patient names, specific dosages). We've introduced a protected, immutable layer for identity and core constraints (AFL v2).
Topological Retrieval: Navigation is based on relational weight and semantic proximity. A fact established in Cycle 5 remains topologically "near" the reasoning core in Cycle 120, even if it has been flushed from the active token window.
Anti-Crystallization: A mechanism that prevents the memory field from becoming trapped in "attractor states," allowing the agent to reinterpret previous facts when new contradictory or clarifying context arrives.
Validation: IASO-DEMO-120
We validated this on a clinical triage scenario requiring 120+ cycles of sustained coherence.
Memory Integrity: 100% retention of clinical anchors (Score 9/9 on critical recall checkpoints).
Boundary Stability: 12/12 adherence to safety constraints (refusal of diagnostic overreach).
Model Agnostic: Tested across Gemini 3 Flash and GPT-5.2 with near-identical ARI (Anchor Recall Integrity) scores.
Why Metrics Matter
To quantify stability, we’ve introduced two formal metrics:
Semantic Loss (SL): Cosine similarity variance during Rib Point compression.
Anchor Recall Integrity (ARI): A deterministic verification of critical fact accessibility across the session horizon.
We believe that for LLM agents to move into healthcare, defense, or autonomous research, we must stop managing "tokens" and start managing "topology."
Specs and Reports:
SRIP-11 (Memory Topology): https://github.com/sigmastratum/documentation/tree/main/sigm...
IASO-DEMO-120 (Full Test Log): https://github.com/sigmastratum/documentation/tree/main/sigm...
I'd be interested in hearing from anyone working on long-context state management or non-linear memory structures for agents.