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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.
Struct-Searcher introduces a belief revision theory-based structural agentic workflow for multimodal deep information seeking, achieving significant accuracy improvements over existing vision-language models and deep research agents.
WebShaper is a formalization-driven framework for synthesizing information-seeking datasets using set theory and Knowledge Projections, achieving state-of-the-art performance on GAIA and WebWalkerQA benchmarks among open-source agents.