StatePlane: A Cognitive State Plane for Long-Horizon AI Systems Under Bounded Context
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 cog...
| Authors | Sasank Annapureddy, John Mulcahy, Anjaneya Prasad Thamatani |
|---|---|
| Primary category | cs.AI |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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