DMF: A Deterministic Memory Framework for Conversational AI Agents 文章

arXiv:2606.03463v1 Announce Type: cross Abstract: Conversational AI agents require memory systems that are both scalable and semantically coherent across long interaction horizons. Existing approaches rely predominantly on large language model (LLM)-based summarisation at write time, which introduces non-determinism, escalating token costs, and opacity in pruning decisions. We present the Deterministic Memory Framework (DMF), a CPU-first approach that replaces generative memory compression with a fully deterministic pipeline grounded in classical NLP analysis, vector geometry, and mathematical scoring. DMF assigns each conversational interaction a Survival Score $\Omega$ computed from deterministic content signals, conversational cues, and structured provenance, combined through a logistic projection.