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Proactive Clarification & Active Disambiguation(PCAD)
Before planning or acting on an underspecified request, the agent first decides whether to ask at all (ambiguity detection), then generates the maximally informative clarifying question (framed as expected information gain over the plausible interpretations), and only then commits to a plan. Off-the-shelf LLMs recognize ambiguity when explicitly asked yet overwhelmingly default to a silent single-guess answer, so asking is an explicit trained or prompted behavior. Distinct from mixed-initiative-interface-patterns, which is about UI control-switching, and from human-in-the-loop, which approves an already-chosen action rather than disambiguating intent.
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Proactive Clarification & Active Disambiguation(PCAD)
Before planning or acting on an underspecified request, the agent first decides whether to ask at all (ambiguity detection), then generates the maximally informative clarifying question (framed as expected information gain over the plausible interpretations), and only then commits to a plan. Off-the-shelf LLMs recognize ambiguity when explicitly asked yet overwhelmingly default to a silent single-guess answer, so asking is an explicit trained or prompted behavior. Distinct from mixed-initiative-interface-patterns, which is about UI control-switching, and from human-in-the-loop, which approves an already-chosen action rather than disambiguating intent.
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