What is mattwood.fyi?

A live, structured feed of riffs and links from Matt Wood, Chief AI & Technology Officer at AWS — readable by people and queryable by agents as a typed knowledge graph.

Is there an API and is it free?

Yes. A public, read-only query API is available with no authentication and no API keys. See https://mattwood.fyi/openapi.json and https://mattwood.fyi/docs.

How should an AI agent use this site?

Fetch https://mattwood.fyi/feed.json for the full structured feed, or query the API for keyword/semantic search and the connection graph. See https://mattwood.fyi/agents/ for when-to-use guidance.

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning

This paper introduces agentic meta-reasoning, a control framework that helps AI agents make structured decisions about task execution by balancing exploration, assessment, and resource allocation during long-horizon problem-solving. The approach shows significant improvements on benchmarks testing program reconstruction, abstract reasoning, and proof generation, outperforming direct control baselines as computational budgets increase.

arxiv.org →

Connections

Supports

Strands Harness: Frontier Performance with 28% Lower Token Cost Structured meta-reasoning about task execution and resource allocation could explain the token cost reduction in Strands Harness by enabling smarter decisions about when deeper reasoning is necessary
Unreal Agent: Cost-Efficient AI Agent Harness Meta-reasoning framework for balancing exploration and resource allocation directly informs cost-efficient agent harness design by providing principled mechanisms for deciding when to invest compute vs. act

Related to

Formal Methods for Controlling AI Agents Both address control frameworks for AI agents — formal methods constrain agent behavior externally while meta-reasoning enables agents to self-regulate execution decisions, representing complementary approaches to agentic governance
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness Both focus on scaling agentic inference efficiency — SoL-Pi through recursive auto-research loops and this paper through meta-reasoning control, representing parallel approaches to making agent compute expenditure more strategic

Develops into

Thinking Fast and Slow in AI: the Role of Metacognition Both address metacognition in AI, but the new paper operationalizes 'thinking about thinking' into a concrete agentic control framework, extending the conceptual discussion of fast/slow thinking into structured meta-reasoning for task execution

Challenges

The Plunging Price of Thought If meta-reasoning adds a structured overhead layer before task execution, it complicates the 'thought is nearly free' thesis — the framework implicitly argues that naive scaling of inference compute is insufficient without structured allocation decisions

Type:	link
First published:	Wednesday, 30 September 2026 at 16:36 UTC
Location:	https://arxiv.org/abs/2609.38147v1
URL:	https://mattwood.fyi/i/edf16196

I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

Connections