Strategist: Evolving Reasoning with Agents· 演进推理策略的AI元代理
A model is only half of an AI agent system. The other half is the inference strategy: the reasoning procedure that turns model calls into a response. Today that second half is written by hand, and the experience of building one strategy never carries to the next. We introduce Strategist, a meta-agent that evolves inference strategies across tasks. Each strategy is written as a blueprint,executable code that other strategies can reuse, and together the blueprints form a library. Given a task and a small development set, Strategist retrieves related blueprints, writes candidate strategies, tests them on real samples, and revises them from the results. Every candidate then enters the library, so later tasks build on earlier discoveries. On 30 benchmarks, Strategist beats the strongest baselin
通过演化推理策略库提升跨任务的AI代理性能。
- 核心方法
- 引入Strategist元代理，它能基于任务需求自动检索和生成推理策略蓝图，并通过实际样本反馈优化这些策略，形成可复用的策略库。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 当前AI代理系统的推理策略通常由人工编写且不跨任务复用，导致效率低下。
- 关键实验
- 在30个不同的基准测试上进行了实验，展示了Strategist相较于现有方法的优越性能。
- 主要贡献
- Strategist在30个基准测试中超越了最强的基线模型，证明了自动演化推理策略的有效性。
- 意义与局限
- 该方法显著提升了AI代理系统在不同任务间的适应性和效率，但仍需探索在更复杂任务中的表现。