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This paper proposes ActionRating, a formulation that places clarification inside an agent's action space on a shared ordinal scale with navigation, enabling two information-seeking modes (mandatory and opportunistic). On hierarchical taxonomy classification benchmarks, experiments with 9 LLMs show that opportunistic clarification improves accuracy and information-seeking effectiveness.
The author argues that multi-agent loop failures are caused by poor organizational design rather than prompt engineering, proposing a hierarchical structure with clear authority and termination conditions to prevent indefinite loops.