AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models

• URL: http://arxiv.org/abs/2604.17377v1
• Date: Sun, 19 Apr 2026 11:02:08 GMT
• Title: AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models
• Abstract summary: Existing memory methods place excessive emphasis on organizing interactions by frequently rewriting them.<n>We introduce AnchorMem, a novel memory framework inspired by the Proust Phenomenon in cognitive science.<n>Our method reconciles fine-grained retrieval with the contextual integrity of interactions.
• Score: 22.304693716449112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models have achieved remarkable performance in complex tasks, they still need a memory system to utilize historical experience in long-term interactions. Existing memory methods (e.g., A-Mem, Mem0) place excessive emphasis on organizing interactions by frequently rewriting them, however, this heavy reliance on summarization risks diluting essential contextual nuances and obscuring key retrieval features. To bridge this gap, we introduce AnchorMem, a novel memory framework inspired by the Proust Phenomenon in cognitive science, where a specific anchor triggers a holistic recollection. We propose a method that decouples the retrieval unit from the generation context. AnchorMem extracts atomic facts from interaction history to serve as retrieval anchors, while preserving the original context as the immutable context. To reveal implicit narrative cues, we construct an associative event graph that uses higher-order event links that bind sets of related facts into shared event representations, strengthening cross-memory integration without relying on generic entities as bridges. During retrieval, the system anchors queries to specific facts and events to locate relevant memories, but reconstructs the context using the associated raw chunks and events. Our method reconciles fine-grained retrieval with the contextual integrity of interactions. Experiments across three closed-source and open-source models on the LoCoMo benchmark demonstrate that AnchorMem significantly outperforms baselines. Code is available at https://github.com/RayNeo-AI-2025/AnchorMem.

Related papers

• EM^2Mem: Event-Centric Multimodal Memory for Large Language Models [52.3165591032625]
  We propose EM2Mem, an event-centric multimodal memory framework that aligns heterogeneous evidence to event anchors during memory construction.<n>Across three long-video QA benchmarks, EM2Mem improves average accuracy over the strongest memory baseline by 2.0, 2.4, and 3.7 points.
  arXiv  Detail & Related papers  (2026-09-01T01:38:41Z)
• RippleMem: From Isolated Retrieval to Associative Recollection for Long-Term Agent Memory [13.519130680921565]
  We introduce RippleMem, a long-term memory system that replaces one-shot retrieval with adaptive associative recollection.<n>LoCoMo and LongMemEval-S show that RippleMem achieves the best overall performance across evaluated settings.
  arXiv  Detail & Related papers  (2026-08-13T15:05:01Z)
• 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)
• Memory Shot for Long-Term Dialogue [27.934717347947352]
  We propose MemShot, which renders local contiguous dialogue spans into structured visual memory units.<n>MemShot achieves stable and competitive performance on both LoCoMo and LongMemEval.<n>Further analysis reveals that MemShot enhances the localization and utilization of historical evidence.
  arXiv  Detail & Related papers  (2026-05-30T01:59:52Z)
• Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval [74.93546115702155]
  Chat-based image retrieval allows the human-interactive system to iteratively clarify and refine user intent through multi-round dialogue.<n>The key challenge in this task lies in dynamically understanding and updating the user's query intent across dialogue rounds.<n>This paper proposes a novel and efficient memory-based user intent updating framework for the chat-based image retrieval task.
  arXiv  Detail & Related papers  (2026-05-17T10:17:41Z)
• HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents [8.86975756125001]
  HeLa-Mem is a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics.<n>HeLa-Mem employs a dual-level organization: (1) an episodic memory graph that evolves through co-activation patterns, and (2) a semantic memory store populated via Hebbian Distillation.<n> Experiments on LoCoMo demonstrate superior performance across four question categories while using significantly fewer context tokens.
  arXiv  Detail & Related papers  (2026-04-18T05:11:18Z)
• AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents [49.63422082885992]
  We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents.<n>AdaMem organizes dialogue history into working, episodic, persona, and graph memories.<n>We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling.
  arXiv  Detail & Related papers  (2026-03-17T13:22:54Z)
• TraceMem: Weaving Narrative Memory Schemata from User Conversational Traces [9.654990538033362]
  Sustaining long-term interactions remains a bottleneck for Large Language Models.<n>We propose TraceMem, a framework that weaves structured, narrative memory schemata from user conversational traces.<n>TraceMem achieves state-of-the-art performance with a brain-inspired architecture.
  arXiv  Detail & Related papers  (2026-02-10T12:14:58Z)
• 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)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning [55.251697395358285]
  Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments.<n>To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences.<n>We propose CompassMem, an event-centric memory framework inspired by Event Theory.
  arXiv  Detail & Related papers  (2026-01-08T08:44:07Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory [21.4675019810992]
  Concept-level memory is reusable, modular abstractions distilled from solution traces and stored in natural language.<n>We evaluate on ARC-AGI, a benchmark that stresses compositional generalization and abstract reasoning.<n>We find abstract concepts to be the most consistent memory design, outscoring the baseline at all tested inference compute scales.
  arXiv  Detail & Related papers  (2025-09-04T17:54:19Z)
• Semantic Anchoring in Agentic Memory: Leveraging Linguistic Structures for Persistent Conversational Context [0.0]
  We propose a hybrid agentic memory architecture that enriches vector-based storage with explicit linguistic cues to improve recall of nuanced, context-rich exchanges.<n>Experiments on adapted long-term dialogue datasets show that semantic anchoring improves factual recall and discourse coherence by up to 18% over strong RAG baselines.
  arXiv  Detail & Related papers  (2025-08-18T05:14:48Z)

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.