Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
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
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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
This paper introduces a novel meta-reasoning framework for AI agents, addressing the challenge of managing complex, multi-step tasks. For ML engineers, understanding agentic meta-reasoning is crucial for building more robust and efficient AI systems that can autonomously navigate intricate problems and optimize their own execution.