Rethinking Memory as Continuously Evolving Connectivity

• URL: http://arxiv.org/abs/2605.28773v1
• Date: Wed, 27 May 2026 17:35:34 GMT
• Title: Rethinking Memory as Continuously Evolving Connectivity
• Abstract summary: Existing memory-augmented LLM agents treat memory as a static repository with pre-defined representations and fixed retrieval pipelines.<n>We propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages.<n>Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments.
• Score: 63.58181883607843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be connected. To address this, we propose FluxMem, a connectivity-evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. During execution, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills recurrent successful trajectories into reusable procedural circuits, guided by one metric for memory generalizability and evolutionary maturity. Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state-of-the-art performance, demonstrating strong adaptation and generalization in complex agentic environments. The code will be open-sourced in https://github.com/zjunlp/LightMem.

Related papers

• Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents [39.55670043123564]
  Existing memory systems update memory when new information arrives.<n>Retrieval feedback is rarely exploited to reorganize memory for future access continuously.<n>We propose textbfREALM, a framework for continually evolving long-term memory in LLM agents.
  arXiv  Detail & Related papers  (2026-09-13T05:17:37Z)
• UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams [77.52140119830241]
  We propose UniMem, a self-routing framework for autonomous memory management.<n>UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways.<n>Experiments on long-horizon streaming task sequences show that UniMem consistently outperforms baselines while maintaining execution fidelity.
  arXiv  Detail & Related papers  (2026-07-28T17:28:21Z)
• 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)
• HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution [4.180315687162217]
  HAGE is a weighted multi-relational memory framework for agentic large language model (LLM) systems.<n>Given a query, an LLM-based classifier identifies the relational intent, and a routing network dynamically modulates the corresponding dimensions of the edge embedding.<n>Traversal scores are computed via a learned combination of semantic similarity and these query-conditioned edge representations.
  arXiv  Detail & Related papers  (2026-05-11T03:41:18Z)
• MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution [52.29303869050117]
  Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction.<n>MemMA is a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths.
  arXiv  Detail & Related papers  (2026-03-19T10:15:59Z)
• 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)
• Neuromem: A Granular Decomposition of the Streaming Lifecycle in External Memory for LLMs [31.958776309037546]
  We present Neuromem, a scalable testbed that benchmarks External Memory Modules under an interleaved insertion-and-retrieval protocol.<n>We observe that performance typically degrades as memory grows across rounds, and time-related queries remain the most challenging category.
  arXiv  Detail & Related papers  (2026-02-15T02:53:37Z)
• MemFly: On-the-Fly Memory Optimization via Information Bottleneck [35.420309099411874]
  Long-term memory enables large language model agents to tackle complex tasks through historical interactions.<n>Existing frameworks encounter a dilemma between compressing redundant information efficiently and maintaining precise retrieval for downstream tasks.<n>MemFly is a framework grounded in information bottleneck principles that facilitates on-the-fly memory evolution for LLMs.<n>MemFly substantially outperforms state-of-the-art baselines in memory coherence, response fidelity, and accuracy.
  arXiv  Detail & Related papers  (2026-02-08T09:37:25Z)
• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• 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.