用超图结构记忆任务技能，让大模型能自进化地复用和优化经验。
HyperSkill: Self-Evolving LLM Agents via Hypergraph-Structured Skill Memory
- 用超图存储子任务与可复用技能，保留它们的组合关系。
- 在GAIA和WebWalkerQA上提升最多达11.51和11.18分。
- 适合需要长期积累和迭代任务能力的研究者或开发者。
随着智能体任务复杂度提升，大模型依赖经验记忆来复用过程知识。高效的记忆设计需同时解决存什么、如何结构化与检索、以及如何演化。现有系统仅部分解决这些问题：将轨迹、洞见或工作流作为孤立条目存储，丢弃子任务间的组合关系与可复用技能；通过扁平嵌入相似性检索，忽略关系信号；且不利用记忆的结构进行维护。我们提出HyperSkill，一种基于超图的记忆框架，联合优化三方面。它将记忆表示为双节点超图（子任务步骤与可复用技能），每条超边关联单个轨迹中的子任务与技能。双重路径检索同时查询子任务与轨迹层级，按跨轨迹共现频率排序技能。周期性结构感知维护通过质量加权传播，剪枝低效节点并合并冗余技能。在xBench、GAIA与WebWalkerQA上，使用GPT-4o与Qwen3-30B-A3B，HyperSkill超越十种记忆基线，于GAIA最高提升+11.51，于WebWalkerQA最高提升+11.18。
原文摘要 · Abstract (English)
As agentic tasks grow in complexity, LLM agents increasingly rely on experiential memory to reuse procedural knowledge across tasks. Effective memory design must jointly address what to store, how memory is structured and retrieved, and how memory evolves. Existing systems tackle each only partially: they store trajectories, insights, or workflows as isolated entries, discarding compositional relationships among subtasks and reusable skills; retrieve by flat embedding similarity that ignores relational signals; and maintain memory without leveraging its relational structure. We propose HyperSkill, a hypergraph-based memory framework that jointly improves all three. HyperSkill represents memory as a hypergraph with two node types, subtask steps and reusable skills, where each hyperedge links the subtasks and skills from a single trajectory. Dual-path retrieval queries both subtask and trajectory levels, ranking skills by co-occurrence across retrieved trajectories. Periodic structure-informed maintenance prunes low-utility nodes and merges redundant skills via quality-weighted propagation. Across xBench, GAIA, and WebWalkerQA with GPT-4o and Qwen3-30B-A3B, HyperSkill outperforms ten memory baselines, yielding gains of up to +11.51 on GAIA and +11.18 on WebWalkerQA.
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