HiGram: Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
Score
78提出路径级定位与协同改写相结合的分层图记忆新思路，明显突破扁平图记忆的局限。
方法设计系统完整，实验覆盖多种冲突场景，论证较为充分。
在提升长期对话答案质量和减少token消耗方面具有直接应用价值，适合智能体记忆系统落地。
针对大模型智能体长期记忆这一当前热点问题，时效性强。
来自arXiv AI/CL/LG类别，属于学术预印本，方法细节和实验数据可查证。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper targets key pain points in agent memory updating, presents a novel and complete method with thorough experiments, and offers strong practical value for long-term dialogue and conflict scenarios.
AI commentary
「This paper deserves attention from anyone working on agent memory and long-term reasoning. Its value is not just another graph memory, but solving both 'where to update' and 'how to update' together, reducing retrieval costs caused by memory bloat. It is especially useful for those building agent products, as conflicting memories in long-term dialogues are an unavoidable challenge in real deployment.」
Quick overview
Agents for long-term reasoning require memory that can be efficiently and effectively updated over time. Existing graph memory methods typically store all memories in a flat graph, where accumulated historical memories can introduce irrelevant context and increase retrieval cost. Moreover, they often update memory units independently, requiring repeated unit-wise rewrites. To address these issues, HiGram proposes an evolving hierarchical graph memory framework with path-level localization and rewriting. It organizes memory into a coarse-to-fine architecture to reduce irrelevant information, uses query- and update-conditioned MicroGraphs to localize support subgraphs and evidence paths, and applies a coordinated rewriting method to jointly revise intra-unit and inter-unit dependencies. Experiments on long-term conversational QA and conflict-aware memory benchmarks show substantial gains in answer quality and token efficiency, as well as improved answer accuracy and evidence selection under dynamic, static, and conditional conflicts.
- Proposes a hierarchical graph memory architecture organizing memories into upper-level nodes and MemoryUnits to reduce irrelevant retrieval information
- Designs MicroGraph-based path-level localization to identify support subgraphs and evidence paths before rewriting
- Introduces a coordinated rewriting method that jointly revises intra-unit memory and inter-unit dependencies for valid dependency structure updates
- Demonstrates substantial improvements over baselines in answer quality and token efficiency on long-term conversational QA and conflict-aware benchmarks
Full text
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