@inproceedings{chen-etal-2026-humans,
title = "Humans' {ALMANAC}: A Human Collaboration Dataset of Action-Level Mental Model {AN}notations for Agent Collaboration",
author = "Chen, Jiaju and
Lu, Yuxuan and
Su, Jiayi and
Chen, Chaoran and
Xiao, Songlin and
Zhang, Zheng and
Wang, Yun and
Li, Yunyao and
Zhao, Jian and
Wu, Tongshuang and
Li, Toby Jia-Jun and
Wang, Dakuo and
Yao, Bingsheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2171/",
pages = "46459--46481",
ISBN = "979-8-89176-462-0",
abstract = "Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as collaborative partners in human workflows. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning, partners' intentions, and shared goals during the collaborative process. Today{'}s agents rarely develop such capabilities since they are primarily optimized for task completion rather than for maintaining the underlying mental models required for collaborative processes. Moreover, the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants' self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans' next-turn behavior and mental models. Our results demonstrate ALMANAC{'}s utility in evaluating models' ability to simulate human collaborative behaviors and infer their underlying mental models. The project page is available at https://human-agent-collab.hailab.io/almanac."
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<abstract>Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as collaborative partners in human workflows. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning, partners’ intentions, and shared goals during the collaborative process. Today’s agents rarely develop such capabilities since they are primarily optimized for task completion rather than for maintaining the underlying mental models required for collaborative processes. Moreover, the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants’ self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans’ next-turn behavior and mental models. Our results demonstrate ALMANAC’s utility in evaluating models’ ability to simulate human collaborative behaviors and infer their underlying mental models. The project page is available at https://human-agent-collab.hailab.io/almanac.</abstract>
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%0 Conference Proceedings
%T Humans’ ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model ANnotations for Agent Collaboration
%A Chen, Jiaju
%A Lu, Yuxuan
%A Su, Jiayi
%A Chen, Chaoran
%A Xiao, Songlin
%A Zhang, Zheng
%A Wang, Yun
%A Li, Yunyao
%A Zhao, Jian
%A Wu, Tongshuang
%A Li, Toby Jia-Jun
%A Wang, Dakuo
%A Yao, Bingsheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-humans
%X Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as collaborative partners in human workflows. Effective collaboration, however, requires collaborators to continuously maintain and align mental models of their own reasoning, partners’ intentions, and shared goals during the collaborative process. Today’s agents rarely develop such capabilities since they are primarily optimized for task completion rather than for maintaining the underlying mental models required for collaborative processes. Moreover, the community lacks authentic human collaboration data with action-level mental model annotations that could guide agents toward collaborative competence. To bridge this gap, we present ALMANAC, a dataset of Action-Level Mental model ANnotations for Agent Collaboration built from the Map Task, a classic dyadic routing task from social science. ALMANAC contains 2,987 collaboration actions, each paired with theory-informed mental model annotations that record the participants’ self-reasoning, perceived partner intent, and perceived team goal. We benchmark six LLMs on predicting humans’ next-turn behavior and mental models. Our results demonstrate ALMANAC’s utility in evaluating models’ ability to simulate human collaborative behaviors and infer their underlying mental models. The project page is available at https://human-agent-collab.hailab.io/almanac.
%U https://aclanthology.org/2026.findings-emnlp.2171/
%P 46459-46481
Markdown (Informal)
[Humans’ ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model ANnotations for Agent Collaboration](https://aclanthology.org/2026.findings-emnlp.2171/) (Chen et al., Findings 2026)
ACL
- Jiaju Chen, Yuxuan Lu, Jiayi Su, Chaoran Chen, Songlin Xiao, Zheng Zhang, Yun Wang, Yunyao Li, Jian Zhao, Tongshuang Wu, Toby Jia-Jun Li, Dakuo Wang, and Bingsheng Yao. 2026. Humans’ ALMANAC: A Human Collaboration Dataset of Action-Level Mental Model ANnotations for Agent Collaboration. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 46459–46481, Budapest, Hungary. Association for Computational Linguistics.