Beyond Static Summarization: Proactive Memory Extraction for LLM Agents

• URL: http://arxiv.org/abs/2601.04463v1
• Date: Thu, 08 Jan 2026 00:37:29 GMT
• Title: Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
• Abstract summary: We argue that existing summary-based methods have two major limitations based on the recurrent processing theory.<n>We introduce a recurrent feedback loop where the agent uses self-questioning to actively probe the dialogue history.<n>Our ProMem significantly improves the completeness of the extracted memory and QA accuracy.
• Score: 20.065809066468354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory management is vital for LLM agents to handle long-term interaction and personalization. Most research focuses on how to organize and use memory summary, but often overlooks the initial memory extraction stage. In this paper, we argue that existing summary-based methods have two major limitations based on the recurrent processing theory. First, summarization is "ahead-of-time", acting as a blind "feed-forward" process that misses important details because it doesn't know future tasks. Second, extraction is usually "one-off", lacking a feedback loop to verify facts, which leads to the accumulation of information loss. To address these issues, we propose proactive memory extraction (namely ProMem). Unlike static summarization, ProMem treats extraction as an iterative cognitive process. We introduce a recurrent feedback loop where the agent uses self-questioning to actively probe the dialogue history. This mechanism allows the agent to recover missing information and correct errors. Our ProMem significantly improves the completeness of the extracted memory and QA accuracy. It also achieves a superior trade-off between extraction quality and token cost.

Related papers

• AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning [13.898789808338634]
  We propose an attribution-guided process-feedback framework for learning memory-construction policies with RL.<n>AttriMem augments the global outcome reward with local rewards derived from token-level contributions to the final answer.
  arXiv  Detail & Related papers  (2026-07-23T09:35:34Z)
• Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
  Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
  arXiv  Detail & Related papers  (2026-07-15T08:32:40Z)
• The Compliance Trap: Diagnosing How AI Agents Consume Conflicting Memory [9.041305193333434]
  Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments.<n>Existing work mostly treats memory as a supply problem, asking what experience to write, how to store it, and which entry to retrieve for the next task.<n>Yet we still lack a clear account of how models consume retrieved memory across a multi-step action trajectory.
  arXiv  Detail & Related papers  (2026-07-12T07:05:31Z)
• Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents [13.846550145896314]
  Memory-augmented LLM agents tackle complex long-horizon tasks by summarizing interaction trajectories into compact memory.<n>Existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality degrades.<n>We argue that memory optimization should focus not merely on trajectory-level success, but on the clarity of the belief induced by intermediate summaries.
  arXiv  Detail & Related papers  (2026-05-28T16:17:19Z)
• RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents [16.625864201773343]
  RecMem stores incoming interactions in a subconscious memory layer and encodes them using lightweight embedding models for retrieval.<n>Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy.
  arXiv  Detail & Related papers  (2026-05-15T15:17:36Z)
• 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)
• MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [78.46301394559903]
  Large Language Models (LLMs) are increasingly used for long-duration tasks.<n>Current methods face a trade-off between cost and accuracy.<n>MemSifter is a novel framework that offloads the memory retrieval process to a small-scale proxy model.
  arXiv  Detail & Related papers  (2026-03-03T02:57:38Z)
• MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks [55.145729491377374]
  Existing evaluations of agents with memory typically assess memorization and action in isolation.<n>We introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops.<n> MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning.
  arXiv  Detail & Related papers  (2026-02-18T09:49:14Z)
• Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory [31.318565330948562]
  This paper proposes the Meta-Cognitive Memory Abstraction method (MCMA)<n>MCMA treats memory abstraction as a learnable cognitive skill rather than a fixed design choice.<n> Experiments on ALFWorld, ScienceWorld, and BabyAI demonstrate substantial improvements in performance, out-of-distribution generalization, and cross-task transfer.
  arXiv  Detail & Related papers  (2026-01-12T12:26:02Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• 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)
• 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)
• Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents [33.617262543252494]
  We present ReMemR1, a memory-augmented agent with callback-enhanced memory that allows selective retrieval from the entire memory history.<n>We also propose Reinforcement Learning with Multi-Level Rewards (RLMLR), which combines final-answer rewards with dense, step-level signals that guide effective memory use.
  arXiv  Detail & Related papers  (2025-09-27T01:36:46Z)
• Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Length [0.0]
  In humans, susceptibility to such interference is inversely linked to working memory capacity.<n>We introduce PI-LLM, an evaluation that sequentially streams semantically related key-value updates and queries only the final values.
  arXiv  Detail & Related papers  (2025-06-09T19:49:11Z)
• Memory-enhanced Retrieval Augmentation for Long Video Understanding [91.7163732531159]
  We introduce a novel memory-enhanced RAG-based approach called MemVid.<n>Our approach operates in four basic steps: 1) memorizing holistic video information, 2) reasoning about the task's information needs based on memory, 3) retrieving critical moments based on the information needs, and 4) focusing on the retrieved moments to produce the final answer.<n>MemVid demonstrates superior efficiency and effectiveness compared to both LVLMs and RAG methods.
  arXiv  Detail & Related papers  (2025-03-12T08:23:32Z)
• Learning to Rehearse in Long Sequence Memorization [107.14601197043308]
  Existing reasoning tasks often have an important assumption that the input contents can be always accessed while reasoning. Memory augmented neural networks introduce a human-like write-read memory to compress and memorize the long input sequence in one pass. But they have two serious drawbacks: 1) they continually update the memory from current information and inevitably forget the early contents; 2) they do not distinguish what information is important and treat all contents equally. We propose the Rehearsal Memory to enhance long-sequence memorization by self-supervised rehearsal with a history sampler.
  arXiv  Detail & Related papers  (2021-06-02T11:58:30Z)

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.