All-Mem: Agentic Lifelong Memory via Dynamic Topology Evolution

• URL: http://arxiv.org/abs/2603.19595v1
• Date: Fri, 20 Mar 2026 03:14:40 GMT
• Title: All-Mem: Agentic Lifelong Memory via Dynamic Topology Evolution
• Abstract summary: All-Mem maintains a structured memory bank via explicit, non destructive consolidation.<n>Periodically offline, an LLM diagnoser proposes confidence scored topology edits executed with three operators: SPLIT, MERGE, and UPDATE.<n>At query time, typed links enable hop bounded, budgeted expansion from active anchors to archived evidence when needed.
• Score: 20.68235182577703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong interactive agents are expected to assist users over months or years, which requires continually writing long term memories while retrieving the right evidence for each new query under fixed context and latency budgets. Existing memory systems often degrade as histories grow, yielding redundant, outdated, or noisy retrieved contexts. We present All-Mem, an online/offline lifelong memory framework that maintains a topology structured memory bank via explicit, non destructive consolidation, avoiding the irreversible information loss typical of summarization based compression. In online operation, it anchors retrieval on a bounded visible surface to keep coarse search cost bounded. Periodically offline, an LLM diagnoser proposes confidence scored topology edits executed with gating using three operators: SPLIT, MERGE, and UPDATE, while preserving immutable evidence for traceability. At query time, typed links enable hop bounded, budgeted expansion from active anchors to archived evidence when needed. Experiments on LOCOMO and LONGMEMEVAL show improved retrieval and QA over representative baselines.

Related papers

• REVEAL: A Rubric-Guided Agent for Explicit Evidence Sufficiency Verificationin Long-Video Question Answering [25.519927761448603]
  REVEAL is a rubric-guided agent framework for long-video question answering.<n>We introduce an adaptive visual-similarity-based preprocessing pipeline that groups visually coherent adjacent frames into natural event units.<n>Built upon this structured memory, REVEAL uses an automatically constructed rubric library to explicitly verify whether retrieved evidence satisfies sufficiency criteria.
  arXiv  Detail & Related papers  (2026-08-09T09:55:42Z)
• MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations [50.24315431387575]
  We introduce MemOps, a benchmark that reformulates conversational memory as a sequence of lifecycle operations.<n>MemOps represents each memory event with a structured trace specifying its trigger, target, scope, state transition, and supporting evidence.<n>Across long-context, retrieval-based, parametric and managed-memory systems, MemOps disentangles failure modes that final-answer accuracy alone conceals.
  arXiv  Detail & Related papers  (2026-07-14T15:33:44Z)
• REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs [17.526686616588794]
  Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons.<n>LLMs cannot retain all past interactions, making long-term memory management essential for storing, updating, and retrieving historical information.<n> REAL constructs long-term conversational memory as a temporal and confidence-aware directed property graph.
  arXiv  Detail & Related papers  (2026-06-09T10:53:10Z)
• Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents [51.30250860677378]
  We propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.<n>Running on a Cue-Tag-Content graph, our active reconstruction mechanism integrates reasoning directly into memory access.<n>Experiments on the LoCoMo benchmark and LongMemEval benchmark demonstrate significant improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:29:46Z)
• Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation [33.47493976010677]
  We propose MemIR, a typed Memory Intermediate Representation that operationalizes source monitoring as a structural constraint.<n>MemIR writes long-term memory into grounded atoms that separate raw evidence, retrieval cues, and truth-bearing claims.<n>Experiments on LoCoMo and BEAM-100K demonstrate that MemIR consistently outperforms existing memory baselines.
  arXiv  Detail & Related papers  (2026-05-25T13:56:31Z)
• SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent [51.274445160155864]
  Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions.<n>Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse.<n>We propose State-Adaptive Memory, a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall.
  arXiv  Detail & Related papers  (2026-05-23T08:37:16Z)
• FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation [73.22935457705057]
  We introduce FRESCO, a benchmark for evaluating re-rankers in temporally dynamic contexts.<n>By pairing recency-seeking queries with historical Wikipedia revisions, FRESCO tests whether re-rankers can prioritize factually recent evidence while maintaining semantic relevance.<n>Our evaluation reveals a consistent failure mode across existing re-rankers: a strong bias toward older, semantically rich documents, even when they are factually obsolete.
  arXiv  Detail & Related papers  (2026-04-14T17:04:25Z)
• Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval [59.295767860331004]
  RF-Mem is a familiarity uncertainty-guided dual-path memory retriever.<n>It embeds human-like dual-process recognition into the retriever.<n>It consistently outperforms one-shot retrieval and full-context reasoning under fixed budget and latency constraints.
  arXiv  Detail & Related papers  (2026-03-10T06:31:44Z)
• Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning [18.621823772319154]
  We propose an end-to-end reinforcement learning framework that unifies memory operations and question answering within a single policy.<n>UMA maintains a dual memory representation: a compact core summary for global context and a structured Memory Bank that supports explicit CRUD.<n>Across 13 datasets spanning Ledger-QA, Test-Time Learning, and Accurate Retrieval, UMA substantially outperforms long-context and RAG baselines on dynamic reasoning and learning tasks.
  arXiv  Detail & Related papers  (2026-02-13T16:54:23Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)
• MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning [26.119505362626338]
  We propose a unified memory framework that consolidates long-term agent experiences into three interconnected components.<n>MemWeaver employs a dual-channel retrieval strategy that jointly retrieves structured knowledge and supporting evidence to construct compact yet information-dense contexts.
  arXiv  Detail & Related papers  (2026-01-26T06:39:27Z)
• Continuum Memory Architectures for Long-Horizon LLM Agents [0.0]
  Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge.<n>We define the textitContinuum Memory Architecture (CMA), a class of systems that maintain and update internal state across interactions.<n>We show consistent behavioral advantages on tasks that expose RAG's structural inability to accumulate, mutate, or disambiguate memory.
  arXiv  Detail & Related papers  (2026-01-14T22:40:35Z)
• Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory [89.65731902036669]
  Evo-Memory is a streaming benchmark and framework for evaluating self-evolving memory in large language model (LLM) agents.<n>We evaluate over ten representative memory modules and evaluate them across 10 diverse multi-turn goal-oriented and single-turn reasoning and QA datasets.
  arXiv  Detail & Related papers  (2025-11-25T21:08:07Z)
• In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents [70.12342024019044]
  Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information limits their effectiveness.<n>We propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections.<n>RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
  arXiv  Detail & Related papers  (2025-03-11T04:15:52Z)

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.