ArcMemo: 基于大语言模型终身记忆的抽象推理组合
ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory
AI总结：
针对大语言模型推理轨迹中模式易丢失的问题，提出概念级终身记忆方法ArcMemo，在ARC-AGI基准上较无记忆基线获7.5%相对提升，实现测试时持续自我提升。
摘要：
尽管推理时缩放技术使大语言模型（LLM）能够执行越来越长且能力更强的推理轨迹，但当上下文窗口因新查询重置时，这些轨迹中发现的模式和洞见会被立即丢弃。外部记忆是留存这些发现的自然方式，近期研究已证明其对推理密集型任务有显著益处。我们发现一个机遇：通过超越基于实例的记忆条目（例如精确的查询/响应对，或与原始问题语境紧密耦合的摘要），转向概念级记忆——即从解题轨迹中提炼出的、以自然语言存储的可复用模块化抽象概念——可让这类记忆具备更广泛的可复用性和可扩展性。对于未来的查询，相关概念会被选择性检索并整合到提示中，从而在无需更新权重的情况下实现测试时持续学习。我们的设计引入了新策略，用于从推演过程中抽象出核心结论，并为新查询检索记忆条目，这促进了记忆复用，并允许记忆随额外经验不断扩展。我们在ARC-AGI基准上进行了评估，该基准强调组合泛化与抽象推理能力，因此非常适合概念记忆的测试。我们的方法相较于强大的无记忆基线取得了7.5%的相对提升，且性能随推理计算量增加持续提升。我们发现抽象概念是最稳定的记忆设计，在所有测试的推理计算规模下表现均优于基线。此外，测试时动态更新记忆的效果优于固定记忆设置，这支持了以下假设：积累并抽象模式能够以一种自我提升的方式实现更多问题的求解。代码可在https://github.com/matt-seb-ho/arc_memo获取。
英文摘要：
While inference-time scaling enables LLMs to carry out increasingly long and capable reasoning traces, the patterns and insights uncovered during these traces are immediately discarded once the context window is reset for a new query. External memory is a natural way to persist these discoveries, and recent work has shown clear benefits for reasoning-intensive tasks. We see an opportunity to make such memories more broadly reusable and scalable by moving beyond instance-based memory entries (e.g. exact query/response pairs, or summaries tightly coupled with the original problem context) toward concept-level memory: reusable, modular abstractions distilled from solution traces and stored in natural language. For future queries, relevant concepts are selectively retrieved and integrated into the prompt, enabling test-time continual learning without weight updates. Our design introduces new strategies for abstracting takeaways from rollouts and retrieving entries for new queries, promoting reuse and allowing memory to expand with additional experiences. We evaluate on ARC-AGI, a benchmark that stresses compositional generalization and abstract reasoning, making it a natural fit for concept memory. Our method yields a 7.5% relative gain over a strong no-memory baseline with performance continuing to scale with inference compute. We find abstract concepts to be the most consistent memory design, outscoring the baseline at all tested inference compute scales. Moreover, dynamically updating memory during test-time outperforms fixed settings, supporting the hypothesis that accumulating and abstracting patterns enables further solutions in a form of self-improvement. Code is available at https://github.com/matt-seb-ho/arc_memo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
