🤖 AI Summary
This work addresses the challenge that large language models face in maintaining coherent state memory across long-horizon, multi-session tasks due to constraints imposed by fixed context windows and KV cache limitations. The authors propose a model-agnostic cognitive state plane that, for the first time, formalizes cognitive states as dynamically evolving structures integrating episodic, semantic, and procedural memory. Grounded in formal models from cognitive psychology, the framework incorporates information-theoretically constrained selective encoding, goal-conditioned retrieval, reconstructive synthesis, and adaptive forgetting mechanisms. It further integrates KV-aware algorithms and write-path poisoning defenses to support enterprise-grade deployment. Evaluations across six domain-specific benchmarks demonstrate significant performance improvements on long-horizon intelligent tasks without requiring context window expansion or model retraining.
📝 Abstract
Large language models (LLMs) and small language models (SLMs) operate under strict context window and key-value (KV) cache constraints, fundamentally limiting their ability to reason coherently over long interaction horizons. Existing approaches -- extended context windows, retrieval-augmented generation, summarization, or static documentation -- treat memory as static storage and fail to preserve decision-relevant state under long-running, multi-session tasks. We introduce StatePlane, a model-agnostic cognitive state plane that governs the formation, evolution, retrieval, and decay of episodic, semantic, and procedural state for AI systems operating under bounded context. Grounded in cognitive psychology and systems design, StatePlane formalizes episodic segmentation, selective encoding via information-theoretic constraints, goal-conditioned retrieval with intent routing, reconstructive state synthesis, and adaptive forgetting. We present a formal state model, KV-aware algorithms, security and governance mechanisms including write-path anti-poisoning, enterprise integration pathways, and an evaluation framework with six domain-specific benchmarks. StatePlane demonstrates that long-horizon intelligence can be achieved without expanding context windows or retraining models.