A memory store is described: an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model that supports point-in-time semantic retrieval without physically overwriting history.
Abstract
Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the model's context budget or send personal data through infrastructure the user does not control. We describe a memory store that avoids both problems: an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model. Each memory is stored as an immutable identity node linked to versioned content nodes carrying two closed-open time intervals: valid time (when the fact was true in the world) and transaction time (when the database recorded it). This design supports point-in-time semantic retrieval without physically overwriting history. Semantic edges between related memories are maintained automatically at write time using cosine similarity over 1024-dimensional embeddings. We evaluate the system on LongMemEval, a 500-question benchmark spanning six question types designed to stress long-term memory. Across 60 sampled questions, the current-state semantic search path achieves 46.7% R@10 overall, rising to 80% on knowledge-update questions. The time-travel path yields 80% R@10 on knowledge-update but decreases recall on temporal-reasoning questions (50% to 37.5%), a consequence of post-filter dilution that points directly to a concrete design improvement. We discuss what these results reveal about the limits of pure retrieval for different question types and what each failure mode suggests for future work.
ChronoMem is the first open-source system and benchmark for systematic semantic global memory rollback in LLM agents, and a post-exposure evaluation protocol that tests whether an agent can behave counterfactually after rollback by answering queries and summarizing history as if future updates had never occurred.
Yongye Su, Wujiang Xu, Chaoji Zuo et al.· arXiv.org· 1 citation
The results show that structured agent memory need not generate an intermediate representation of the past, and Zero-Mem achieves competitive performance while eliminating LLM calls and LLM-token consumption from memory operations.
Large language model (LLM) agents need durable, faithful memory of everything a user or organization has said and stored, yet most memory systems commit to a single organizing structure (a fact store, a vector index, or a knowledge graph) and inherit its blind spots. We present Agent Zero Memory, a provenance-aware long-term memory system that distils a user's conversations, files, and connected sources into three parallel memory systems, each capturing a different facet of the same history: an episodic Memory Events timeline that makes when and what changed first-class, an associative entity-event knowledge graph that links people and projects across sessions, and a semantic, curated, citation-locked Hierarchical Documentary Memory (HDM) of durable facts. A retrieval turn runs an intent gate (so self-contained turns add no latency), a source router, and three concurrent agentic searches, one per system, each a tool-using loop over hybrid (embedding + lexical) search under agent-controlled filters; their grounded, cited answers are integrated into one answer with a single confidence. We formalize the reading discipline: every learned item is a provenanced item carrying its origin, timestamp, and evidence pointer, and every answer is read under a citation lock, so it may cite only evidence its reader actually opened; fabrication is structurally excluded and the system abstains rather than guesses. On two public benchmarks the system sets a new state of the art: 95.60% on LongMemEval and 93.60% on LoCoMo, improving over the strongest prior systems by +0.73 and +1.10 points. A controlled study across eight backbone LLMs characterizes the accuracy-cost-latency frontier: accuracy varies by only 3.4 points while per-query cost varies by ~30x, with near-state-of-the-art quality at up to 20x lower cost per query, the signature of memory-driven, rather than model-driven, quality.
Overall, LightMem offers a context-efficiency trade-off rather than a general advantage over Naive RAG, whose value depends on the retriever and available token budget, motivating future work on retrieval, reranking, query formulation, and their interaction with raw and constructed memory representations.
Yong Zhou, Shuai Wang, B. Koopman et al.· arXiv.org· 0 citations
A structured memory framework for query-conditioned user-state inference for long-term personalization that achieves state-of-the-art performance on both PersonaMem and KnowU-Bench, demonstrating the effectiveness of query-conditioned user-state inference for long-term personalization.
Heng Wang, Yifei Li, Lingling Zhang et al.· 0 citations
Long-term conversational agents rely on memory databases to ensure consistent user state across multiple interactions. Although prior work has analyzed retrieval-augmented generation and persistent memory for conversational agents, few studies have evaluated how memory-write policies influence memory retrieval behavior later on. We investigate whether memory interference originates mainly from memory retrieval or from the accumulation of competing fact versions added during memory updates. Three memory-write policies were evaluated in a controlled virtual patient dialogue environment. Append-only retrieval (NAIVE-SLOT), append-only retrieval with recency ranking (RECENT-SLOT), and slot-overwrite memory (SMART-SLOT) which maintains a single canonical value for each fact. Three clinical scenarios were implemented, consisting of 4,320 recall observations and 1,080 adversarial trap probes. SMART-SLOT achieved the highest recall accuracy, cross-session consistency, and demonstrated the greatest resistance to stale-fact prompts while with insignificant additional latency.
Erica Butts, Salam Daher· Proceedings of the 26th ACM...· 0 citations
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