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