Rethinking Memory as Continuously Evolving Connectivity
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 granulari...
| Authors | Jizhan Fang, Buqiang Xu, Zhixian Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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