Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents

• URL: http://arxiv.org/abs/2608.29606v1
• Date: Sun, 30 Aug 2026 06:55:59 GMT
• Title: Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents
• Abstract summary: Agent Zero Memory is a provenance-aware long-term memory system.<n>It distils a user's conversations, files, and connected sources into three parallel memory systems.
• Score: 3.6666917625778193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MemFuse: Multi-Source Memory Fusion from Fragmented Observations [32.62990579442196]
  We introduce **MemFuseBench**, a benchmark for *multi-source memory fusion*.<n>MemFuseBench is built with a Scene-to-Sensor pipeline that synthesizes controllable scenarios into source-tagged observations, evidence-grounded questions, and adversarial distractors.<n>We propose **MemFuse**, a structured memory system that preserves source-level evidence in event-layer atomic memory and organizes related atomic events into cluster-layer fused memory within a causal graph.
  arXiv  Detail & Related papers  (2026-08-19T09:04:07Z)
• Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability [49.415740364852105]
  Deployed agents increasingly keep their long-term memory as a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools.<n>Prior systems design bespoke memory representations and study retrieval over them, leaving the default's two working assumptions untested.<n>We present the first systematic exploration of chunk-based memory for LLM agents.
  arXiv  Detail & Related papers  (2026-07-29T08:59:43Z)
• Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings [0.0]
  A seeded life-script sampler emits facts with validity intervals volatility classes, and source channels before any text exists.<n>A verifier confirms every planted fact and questions are instantiated mechanically from the script.<n>The synthetic, fictionalized corpus embeds features absent from the benchmarks we survey.
  arXiv  Detail & Related papers  (2026-07-24T04:19:45Z)
• Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning [77.7531245219403]
  textscHomer is a Hierarchical Online Memory Exploration and Reasoning framework.<n>textscHomer's memory mirrors the multi-scale structure of long videos, ranging from raw perception, to recurring entities, to events connected by explicit temporal and causal relations.<n>textscHomer outperforms the previous best agent method by $+5.5$, $+10.8$, and $+4.4$ points on M3-Bench-robot, M3-Bench-web, and Video-MME-Long.
  arXiv  Detail & Related papers  (2026-07-01T05:53:09Z)
• WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction [72.1620416874118]
  Multimodal large language models are increasingly deployed as long-horizon agents.<n>Existing benchmarks measure recall over static dialogue, collapse memory into a single end-of-task accuracy, and reduce visual observations to captions.<n>We formulate multimodal agent memory as an Action-World Interaction Loop with an observable four-stage lifecycle.
  arXiv  Detail & Related papers  (2026-05-28T04:27:20Z)
• MemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs [47.66730296440261]
  We introduce MemQ, which propagates credit backward through a provenance DAG that records which memories were retrieved when each new memory was created.<n>Across six benchmarks, MemQ achieves the highest success rate on all six in generalization evaluation and runtime learning.<n>We further study how $$ and $$ interact with the EC-MDP structure, providing principled guidance for parameter selection and future research.
  arXiv  Detail & Related papers  (2026-05-08T18:30:24Z)
• Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers [0.42061757959666934]
  Large language model (LLM) agents increasingly operate in settings where a single context window is too small to capture what has happened.<n>Memory is what turns a stateless text generator into a genuinely adaptive agent.<n>This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents.
  arXiv  Detail & Related papers  (2026-03-08T15:08:01Z)
• Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution [52.76038908826961]
  We propose $textbfReMe$ ($textitRemember Me, Refine Me$) to bridge the gap between static storage and dynamic reasoning.<n>ReMe innovates across the memory lifecycle via three mechanisms: $textitmulti-faceted distillation$, which extracts fine-grained experiences.<n>Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system.
  arXiv  Detail & Related papers  (2025-12-11T14:40:01Z)
• Agentic Learner with Grow-and-Refine Multimodal Semantic Memory [50.81667005063605]
  ViLoMem is a dual-stream memory framework that constructs compact, schema-based memory.<n>It encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences.
  arXiv  Detail & Related papers  (2025-11-26T18:55:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.