DISCERN：写入时保留、读取时解析的 LLM 智能体持久记忆框架
DISCERN: Preserve at Write, Resolve at Read for Persistent LLM-Agent Memory
AI 导读
研究提出 DISCERN 智能体记忆框架，核心规则是“写入时保留、读取时解析”：写入时构建严格增量式的双时序溯源图，不合并易混淆实体、不丢弃被取代的值；读取时用零 LLM 工具驱动智能体循环，从四个门控动作中选择一个。
正文 · 原文
Abstract:Enterprise agents accumulate memory across dozens of sessions in which facts contradict, get superseded, and blur as similarly named entities collide in embedding space. The field treats this as a retrieval problem; we show it is not. On ENT-310, a synthetic enterprise sales-agent memory benchmark of 310 queries (210 answerable, 100 unanswerable), a vanilla Retrieval-Augmented Generation (RAG) reader over a memory store already matches strong memory systems on the recall pillars. Retention and currency are near-saturated, roughly 0.93 to 0.98 for every competent system, so recall is essentially solved. What remains unsolved is resolution: which entity a query refers to, which time-state is current, and whether to commit an answer at all or decline when evidence is insufficient. We propose DISCERN (DISambiguation and Cross-session Episodic memoRy Navigation), an agent-memory framework built on a single rule: preserve at write, resolve at read. At write time DISCERN builds a strictly additive bi-temporal provenance graph that never merges confusable entities and never drops superseded values. At read time a zero-LLM tool surface drives an agentic loop that navigates this graph and commits exactly one of four gated terminal actions. A four-judge LLM-as-a-Judge council scores two axes: answer correctness on answerable questions, and the answer-versus-abstain decision as a classification problem. On ENT-310, DISCERN is the best-calibrated system on abstention (F1 0.924 versus at most 0.802 across eight baselines) and leads macro answer correctness under equal pillar weighting (0.861 versus 0.806 for the strongest baseline), a lead driven by entity resolution (0.800, where no baseline exceeds 0.40). Applied with zero tuning to the full LoCoMo corpus, the same pipeline again leads macro answer correctness (0.663 versus 0.638) and stays among the best-calibrated on false-premise abstention.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15729 [cs.AI] |
| (or arXiv:2611.15729v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15729 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ramcharan Kakarla [view email]
[v1]
Wed, 11 Nov 2026 17:06:07 UTC (1,425 KB)
来源：arXiv cs.AI · arxiv.org