Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs
MESA-S framework introduces metacognitive governance to curb autonomous agent overthinking and tool reliance errors.
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 ski...
ArXiv CS.AI
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This research introduces a vital shift from simple confidence scoring to nuanced epistemic vigilance by separating parametric certainty from procedural trust. By implementing delayed appraisal, the framework prevents autonomous agents from blindly offloading tasks to unreliable tools, thereby increasing overall architectural reliability.
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