Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite

Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph memory has been adopted to offer structural organization for multi-hop retrieval and reasoning. However, existing methods store all memories in a flat graph, and accumulated historical memories can introduce irrelevant contexts and increase the cost of evidence selection during retrieval. Moreover, they typically update

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ethics.ai (5 August 2026), “Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite,” evidence record 16918, https://ethics.ai/record/16918 (originally published by arXiv cs.AI).

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