Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents
Ming Wu and Pengyuan Zhu build Agent Zero Memory, which runs three parallel memory systems over the same history and enforces a citation lock so an answer may only cite evidence its reader actually opened.
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Three structures instead of one: An episodic Memory Events timeline, an associative entity-event knowledge graph, and a curated citation-locked Hierarchical Documentary Memory. The argument is that any single organizing structure inherits its own blind spots, which is exactly what the Selective Forgetting paper in this same block demonstrates empirically for graphs.
Citation lock as a structural guarantee: Every learned item carries origin, timestamp and evidence pointer; every answer may cite only evidence its reader opened. Fabrication is excluded by construction and the system abstains rather than guesses.
Retrieval turn has an intent gate: Self-contained turns add no latency, then a source router fans out three concurrent agentic searches, one per memory system, each a tool-using loop over hybrid embedding plus lexical search.
New SOTA on both memory benchmarks: 95.60% on LongMemEval and 93.60% on LoCoMo, improving on the strongest prior systems by 0.73 and 1.10 points.
The cost frontier is the more interesting result: Across eight backbones accuracy varies by 3.4 points while per-query cost varies about 30x, with near-SOTA quality at up to 20x lower cost. Quality is coming from the memory design, not the model.
Abstract
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.