DISCERN: Preserve at Write, Resolve at Read for Persistent LLM-Agent Memory
Chirag Ahuja, Vladislav Tanov, Ramcharan Kakarla and Jonathan Taws at Amazon argue that persistent agent memory fails on resolution, not retrieval, and build DISCERN, a memory system that keeps every fact at write time and resolves entities and time at read time.
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Diagnosis. On ENT-310, a synthetic enterprise sales-agent benchmark of 310 queries, a plain RAG reader matches strong memory systems on recall. Retention and currency scores sit between 0.93 and 0.98 for every competent system.
What remains hard. The open problems are which entity a query means, which time-state is current, and whether to answer at all when evidence is missing.
Design. Writes go to an append-only bi-temporal provenance graph that never merges similar entities and never drops superseded values. Reads run an agent loop over zero-LLM tools that must end in one of four actions: answer, update, clarify or abstain.
Results. DISCERN reaches abstention F1 0.924 versus at most 0.802 for eight baselines, macro answer correctness 0.861 versus 0.806, and entity resolution 0.800 where no baseline exceeds 0.40. On LoCoMo with no tuning it leads macro correctness 0.663 versus 0.638.
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Enterprise agents accumulate memory across dozens of sessions in which facts contradict, get superseded, and blur as similarly named entities collide in embedding space.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DISCERN builds a strictly additive bi-temporal provenance graph that never merges confusable entities and never drops superseded values.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.