@inproceedings{zhu-etal-2026-distributed,
title = "Distributed Partial Information Puzzles: Examining Common Ground Construction under Epistemic Asymmetry",
author = "Zhu, Yifan and
Bradford, Mariah and
Lai, Kenneth and
Obiso, Timothy and
Venkatesha, Videep and
Pustejovsky, James and
Krishnaswamy, Nikhil",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.391/",
doi = "10.63317/36sdnib5usq7",
pages = "4974--4987",
abstract = "Establishing \textit{common ground}, a shared set of beliefs and mutually recognized facts, is fundamental to collaboration, yet remains a challenge for current AI systems, especially in multimodal, multiparty settings, where the collaborators bring different information to the table. We introduce the \textbf{Distributed Partial Information Puzzle (DPIP)}, a collaborative construction task that elicits rich multimodal communication under epistemic asymmetry. We present a multimodal dataset of these interactions, annotated and temporally aligned across speech, gesture, and action modalities to support reasoning over propositional content and belief dynamics. We then evaluate two paradigms for modeling common ground (CG): (1) state-of-the-art large language models (LLMs), prompted to infer shared beliefs from multimodal updates, and (2) an axiomatic pipeline grounded in Dynamic Epistemic Logic (DEL) that incrementally performs the same task. Results on the annotated DPIP data indicate that it poses a challenge to modern LLMs' abilities to track both task progression and belief state."
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<abstract>Establishing common ground, a shared set of beliefs and mutually recognized facts, is fundamental to collaboration, yet remains a challenge for current AI systems, especially in multimodal, multiparty settings, where the collaborators bring different information to the table. We introduce the Distributed Partial Information Puzzle (DPIP), a collaborative construction task that elicits rich multimodal communication under epistemic asymmetry. We present a multimodal dataset of these interactions, annotated and temporally aligned across speech, gesture, and action modalities to support reasoning over propositional content and belief dynamics. We then evaluate two paradigms for modeling common ground (CG): (1) state-of-the-art large language models (LLMs), prompted to infer shared beliefs from multimodal updates, and (2) an axiomatic pipeline grounded in Dynamic Epistemic Logic (DEL) that incrementally performs the same task. Results on the annotated DPIP data indicate that it poses a challenge to modern LLMs’ abilities to track both task progression and belief state.</abstract>
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%0 Conference Proceedings
%T Distributed Partial Information Puzzles: Examining Common Ground Construction under Epistemic Asymmetry
%A Zhu, Yifan
%A Bradford, Mariah
%A Lai, Kenneth
%A Obiso, Timothy
%A Venkatesha, Videep
%A Pustejovsky, James
%A Krishnaswamy, Nikhil
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F zhu-etal-2026-distributed
%X Establishing common ground, a shared set of beliefs and mutually recognized facts, is fundamental to collaboration, yet remains a challenge for current AI systems, especially in multimodal, multiparty settings, where the collaborators bring different information to the table. We introduce the Distributed Partial Information Puzzle (DPIP), a collaborative construction task that elicits rich multimodal communication under epistemic asymmetry. We present a multimodal dataset of these interactions, annotated and temporally aligned across speech, gesture, and action modalities to support reasoning over propositional content and belief dynamics. We then evaluate two paradigms for modeling common ground (CG): (1) state-of-the-art large language models (LLMs), prompted to infer shared beliefs from multimodal updates, and (2) an axiomatic pipeline grounded in Dynamic Epistemic Logic (DEL) that incrementally performs the same task. Results on the annotated DPIP data indicate that it poses a challenge to modern LLMs’ abilities to track both task progression and belief state.
%R 10.63317/36sdnib5usq7
%U https://aclanthology.org/2026.lrec-1.391/
%U https://doi.org/10.63317/36sdnib5usq7
%P 4974-4987
Markdown (Informal)
[Distributed Partial Information Puzzles: Examining Common Ground Construction under Epistemic Asymmetry](https://aclanthology.org/2026.lrec-1.391/) (Zhu et al., LREC 2026)
ACL