Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs
As large language models (LLMs) transition into autonomous agents integrated with extensive tool ecosystems, traditional routing heuristics increasingly succumb to context pollution and "overthinking". We argue that the bottleneck is not a deficit in algorithmic capability or skill diversity, but the absence of disciplined second-order metacognitive governance. In this paper, our scientific contribution focuses on the computational translation of human cognitive control - specifically, delayed a
Record details
Published: 17 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (17 April 2026), “Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs,” evidence record 5706, https://ethics.ai/record/5706 (originally published by arXiv).
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