让大模型在推理时按需调用具体经验，提升复杂问题解决能力
HippoSpark: An On-Demand Experience System for LLM Reasoning
- 基于当前推理状态动态检索精准经验，而非通用解题规则
- 在数学、科学和编程任务中表现优于传统提示和任务级经验方法
- 适合需要精细引导的复杂推理场景，如AI数学竞赛或代码生成
将历史推理路径提炼为可复用的经验，已成为大模型研究的热点。然而现有方法多在任务层面操作，依赖通用摘要或规则，假设相似任务有共通解法，这在复杂推理中常失效，因关键瓶颈需精确的状态引导而非泛化启发。本文提出HippoSpark，一种面向状态级别的经验系统，可根据当前推理状态按需检索精准指导。在数学、科学和编程基准测试中，HippoSpark始终优于标准提示和任务级经验基线。结果表明，真正有效的经验系统应在关键瓶颈处提供可执行的指引，而非仅作为泛化的任务上下文。代码已开源：https://github.com/DanlingMeng/HippoSpark。
原文摘要 · Abstract (English)
Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research. However, existing methods predominantly operate at the task level, leveraging general summaries or rules under the assumption that analogous tasks share universal solution patterns. This approach often fails in complex reasoning, which typically falters at local bottlenecks that require precise, state-specific guidance rather than broad heuristics. We introduce HippoSpark, a state-level experience system that performs on-demand retrieval tailored to the immediate needs of the current reasoning state. Across mathematical, scientific, and programming benchmarks, HippoSpark consistently outperforms both standard prompting and task-level experience baselines. Our findings reveal that the most effective experience systems are those that provide actionable guidance at critical bottlenecks rather than serving as generic task-level context. Our code is available at https://github.com/DanlingMeng/HippoSpark.
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