Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning· 思考前的思考：通过元推理扩展智能体推理
As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control choices, like which partial work to build on, whether to start fresh, or when to stop. We introduce agentic meta-reasoning, an inference-time harness that makes these choices an explicit and structured reasoning process. Workers carry out the task-level computation, while a controller consolidates what the run has established, explores next options, assesses what each option is worth under the remaining budget, and dispatches the chosen work with context drawn from persistent memory. Between decisions the controller carries only a compact account of the run rather than replaying its full history. Our baselines span production coding agents a
通过元推理优化智能体在复杂任务中的控制决策过程。
- 核心方法
- 引入了智能体元推理，这是一种推理时间框架，用于显式和结构化地处理智能体在任务执行中的控制决策，包括选择继续已有工作、重新开始或停止等。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 随着智能体执行的任务越来越长和复杂，如何有效地控制每个执行步骤成为一个关键问题。
- 关键实验
- 未提供
- 主要贡献
- 提出了智能体元推理框架，提高了智能体在处理复杂任务时的决策效率和效果。
- 意义与局限
- 该研究有助于提高智能体在执行复杂任务时的自适应能力和效率，但具体的实验验证和应用场景还需进一步探索。