WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent
  Triggerability in Task-Oriented Dialogue
Authors: name: Yaoyao Qian; name: Jindan Huang; name: Yuanli Wang; name: Simon Yu; name: Kyrie Zhixuan Zhou; name: Jiayuan Mao; name: Mingfu Liang; name: Hanhan Zhou
Published: 2025-06-02 17:11:10

Abstract: Task-oriented dialogue systems often face difficulties when user utterances
seem semantically complete but lack necessary structural information for
appropriate system action. This arises because users frequently do not fully
understand their own needs, while systems require precise intent definitions.
Current LLM-based agents cannot effectively distinguish between linguistically
complete and contextually triggerable expressions, lacking frameworks for
collaborative intent formation. We present STORM, a framework modeling
asymmetric information dynamics through conversations between UserLLM (full
internal access) and AgentLLM (observable behavior only). STORM produces
annotated corpora capturing expression trajectories and latent cognitive
transitions, enabling systematic analysis of collaborative understanding
development. Our contributions include: (1) formalizing asymmetric information
processing in dialogue systems; (2) modeling intent formation tracking
collaborative understanding evolution; and (3) evaluation metrics measuring
internal cognitive improvements alongside task performance. Experiments across
four language models reveal that moderate uncertainty (40-60%) can outperform
complete transparency in certain scenarios, with model-specific patterns
suggesting reconsideration of optimal information completeness in human-AI
collaboration. These findings contribute to understanding asymmetric reasoning
dynamics and inform uncertainty-calibrated dialogue system design.