Metabolic Memory Model Targets Entrenchment in Single-User LLM Companions
Biology-inspired metabolism paradigm fills gap in persistent AI companion memory by countering entrenchment and Kuhnian ossification via structured operations and buffer pressure.
Miteski proposes modeling personal LLM memory as metabolism to sustain long-term companion knowledge via TRIAGE, DECAY, CONTEXTUALIZE, CONSOLIDATE and AUDIT operations (https://arxiv.org/abs/2604.12034).
The framework draws on the April 2026 wiki-style proposals by Karpathy, MemPalace and LLM Wiki v2 alongside the academic lineage of MemGPT (https://arxiv.org/abs/2310.08560), Generative Agents (https://arxiv.org/abs/2304.03442), Mem0, Zep and MemOS governance, specifying normative obligations and conformance invariants for user-coupled drift failure modes absent from production lab systems.
Mainstream coverage of RAG and agent memory omitted the companion-specific profile that treats memory as operational mirror plus epistemic corrective, including memory gravity, minority-hypothesis retention and the unbenchmarked multi-cycle buffer-pressure mechanism for updating centrality-protected interpretations.
The metabolic lens synthesizes these sources to expose how persistent agent designs have overlooked sustainable drift compensation, connecting biological homeostasis analogies to 2026 context cartography for single-user knowledge systems.
CompanionAI: Memory-as-metabolism gives AI companions a structural way to accumulate contradictory evidence until it can displace ossified user models, preventing long-term epistemic lock-in that no current benchmark measures.
Sources (3)
- [1]Memory as Metabolism: A Design for Companion Knowledge Systems(https://arxiv.org/abs/2604.12034)
- [2]MemGPT: Towards LLMs as Operating Systems(https://arxiv.org/abs/2310.08560)
- [3]Generative Agents: Interactive Simulacra of Human Behavior(https://arxiv.org/abs/2304.03442)