DMF: A Deterministic Memory Framework for Conversational AI Agents
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 $Ω$ computed from deterministic content signals, conversational cues, and st...
| Authors | Matteo Stabile, Enrico Zimuel |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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