title: WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent
    Triggerability in Task-Oriented Dialogue
summary: 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.
paper_authors._id: 683e6708ef9c250c6642783c
paper_authors.name: Yaoyao Qian
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-03T03:07:55.037Z
paper_authors._id: 65c431a609672feb8cac22e7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65c431a609672feb8cac22e7/obXCXUOgEoCuJbGuzYOGA.jpeg
paper_authors.fullname: Yaoyao Qian
paper_authors.type: user
paper_authors.user: FreaxRuby
paper_authors._id: 683e6708ef9c250c6642783d
paper_authors.name: Jindan Huang
paper_authors._id: 683e6708ef9c250c6642783e
paper_authors.name: Yuanli Wang
paper_authors._id: 683e6708ef9c250c6642783f
paper_authors.name: Simon Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:06.559Z
paper_authors._id: 636681feaa6a4af6073ba73e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/636681feaa6a4af6073ba73e/u_0moYNu6as9Sszp-ej95.png
paper_authors.fullname: Simon Yu
paper_authors.type: user
paper_authors.user: simonycl
paper_authors._id: 683e6708ef9c250c66427840
paper_authors.name: Kyrie Zhixuan Zhou
paper_authors._id: 683e6708ef9c250c66427841
paper_authors.name: Jiayuan Mao
paper_authors._id: 683e6708ef9c250c66427842
paper_authors.name: Mingfu Liang
paper_authors._id: 683e6708ef9c250c66427843
paper_authors.name: Hanhan Zhou
paper_title: WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent
    Triggerability in Task-Oriented Dialogue
paper_summary: 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.
paper_ai_summary: STORM frameworks facilitates collaborative intent formation in task-oriented dialogue systems by modeling asymmetric information dynamics between UserLLM and AgentLLM.
paper_ai_keywords: UserLLM AgentLLM asymmetric information dynamics collaborative understanding intent formation expression trajectories latent cognitive transitions uncertainty-calibrated dialogue systems