@inproceedings{yuan-etal-2026-quack,
title = "{QUACK}: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents",
author = "Yuan, Ye and
Song, Rui and
Li, Weien and
Li, Zeyu and
Liu, Haochen and
Kong, Xiangyu and
Han, Changjiang and
Yang, Yonghan and
Zhao, Zichen and
Dong, Zixuan and
Lyu, Fuyuan and
He, Bowei and
Wu, Haolun and
Kang, Jikun and
Liu, Xue",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.687/",
pages = "15805--15824",
ISBN = "979-8-89176-459-0",
abstract = "Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent{'}s language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent{'}s ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1{\%} of its verifiable spatial claims and 11.5{\%} of accusations are strictly unsupported. We release the full engine, evaluation framework, toolkit, and logs in https://github.com/AAAAA-Academia-Attractions/QUACK."
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<abstract>Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent’s language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent’s ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and 11.5% of accusations are strictly unsupported. We release the full engine, evaluation framework, toolkit, and logs in https://github.com/AAAAA-Academia-Attractions/QUACK.</abstract>
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%0 Conference Proceedings
%T QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents
%A Yuan, Ye
%A Song, Rui
%A Li, Weien
%A Li, Zeyu
%A Liu, Haochen
%A Kong, Xiangyu
%A Han, Changjiang
%A Yang, Yonghan
%A Zhao, Zichen
%A Dong, Zixuan
%A Lyu, Fuyuan
%A He, Bowei
%A Wu, Haolun
%A Kang, Jikun
%A Liu, Xue
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yuan-etal-2026-quack
%X Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text-only interaction, making it difficult to tell whether an agent’s language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open-source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance-level consistency. Its core Statement Verification Pipeline reconstructs each agent’s ground-truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language-action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and 11.5% of accusations are strictly unsupported. We release the full engine, evaluation framework, toolkit, and logs in https://github.com/AAAAA-Academia-Attractions/QUACK.
%U https://aclanthology.org/2026.emnlp-main.687/
%P 15805-15824
Markdown (Informal)
[QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents](https://aclanthology.org/2026.emnlp-main.687/) (Yuan et al., EMNLP 2026)
ACL
- Ye Yuan, Rui Song, Weien Li, Zeyu Li, Haochen Liu, Xiangyu Kong, Changjiang Han, Yonghan Yang, Zichen Zhao, Zixuan Dong, Fuyuan Lyu, Bowei He, Haolun Wu, Jikun Kang, and Xue Liu. 2026. QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15805–15824, Budapest, Hungary. Association for Computational Linguistics.