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Metacognitive Steering: Learning the Structure of Scientific JudgmentMetacognitive Steering: Learning the Structure of Scientific Judgment

📅 2026-09-16 ⏱️ 约 7 分钟阅读⏱️ 7 min read ✍️ AI导航编辑部✍️ AI Nav Editorial 🔗 arxiv.org
Metacognitive Steering: Learning the Structure of Scientific Judgment
📝 内容摘要📝 Summary

Computer Science > Artificial Intelligence [Submitted on 14 Sep 2026] Title:Metacognitive Steering: Learning the SComputer Science > Artificial Intelligence [Submitted on 14 Sep 2026] Title:Metacognitive Steering: Learning the S

📌 核心要点

  • Computer Science > Artificial IntelligenceComputer Science > Artificial Intelligence
  • [Submitted on 14 Sep 2026][Submitted on 14 Sep 2026]
  • Title:Metacognitive Steering: Learning the Structure of Scientific JudgmentTitle:Metacognitive Steering: Learning the Structure of Scientific Judgment

Computer Science > Artificial Intelligence

[Submitted on 14 Sep 2026]

Title:Metacognitive Steering: Learning the Structure of Scientific Judgment

View PDF HTML (experimental)Abstract:Long-horizon scientific discovery requires agents to alternate between exploration, disciplined execution, and critical reassessment as evidence changes. Current language models are trained primarily on the products of science and optimized using outcome-level signals, providing limited supervision for these process-level shifts in scientific judgment. We investigate whether such judgment can be recovered from scientist interaction traces and used to control the internal computation of a frozen frontier model. Using contrastive interventions collected during real scientific research, we identify a coordinated, low-dimensional control structure within Kimi 2.6, a trillion-parameter mixture-of-experts model. Residual analysis, attention-weight subspace alignment, and cross-layer singular value decomposition converge on a mid-depth control surface spanning key layers. We introduce Metacognitive Steering, an inference-time controller that reads the model's cognitive regime and dynamically composes layer-specific interventions for exploration, procedural convergence, or critical reassessment without modifying model parameters. Behavioral analyses show that this control produces more sustained exploration, explicit pruning, and evidence-responsive synthesis. We operationalize the method in Columbus-1, an autonomous research system that identified eight independently reproduced, attacker-reachable vulnerabilities in BlueZ and directed the design, simulation, and fabrication of a ten-foot rocket intended to land propulsively using non-throttleable solid motors. Together, these results show that process-level scientific judgment can provide supervision for interpretable, dynamic control over a model's reasoning strategy.

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Computer Science > Artificial Intelligence

[Submitted on 14 Sep 2026]

Title:Metacognitive Steering: Learning the Structure of Scientific Judgment

View PDF HTML (experimental)Abstract:Long-horizon scientific discovery requires agents to alternate between exploration, disciplined execution, and critical reassessment as evidence changes. Current language models are trained primarily on the products of science and optimized using outcome-level signals, providing limited supervision for these process-level shifts in scientific judgment. We investigate whether such judgment can be recovered from scientist interaction traces and used to control the internal computation of a frozen frontier model. Using contrastive interventions collected during real scientific research, we identify a coordinated, low-dimensional control structure within Kimi 2.6, a trillion-parameter mixture-of-experts model. Residual analysis, attention-weight subspace alignment, and cross-layer singular value decomposition converge on a mid-depth control surface spanning key layers. We introduce Metacognitive Steering, an inference-time controller that reads the model's cognitive regime and dynamically composes layer-specific interventions for exploration, procedural convergence, or critical reassessment without modifying model parameters. Behavioral analyses show that this control produces more sustained exploration, explicit pruning, and evidence-responsive synthesis. We operationalize the method in Columbus-1, an autonomous research system that identified eight independently reproduced, attacker-reachable vulnerabilities in BlueZ and directed the design, simulation, and fabrication of a ten-foot rocket intended to land propulsively using non-throttleable solid motors. Together, these results show that process-level scientific judgment can provide supervision for interpretable, dynamic control over a model's reasoning strategy.

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Connected Papers (What is Connected Papers?)

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scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

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Demos

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arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

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Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

来源arxiv.org· 本文为编辑整理,仅供参考