---
title: "GroupToM-Bench — paper by Tang et al. (2026)"
description: "Multimodal LLMs have become better at inferring what a single agent believes or wants. 1.9 points. 54 references."
authors: ["Weidong Tang", "Jierui Li", "Yueling Hou", "Zihan Mei", "Can Zhang", "Xinyan Wan", "Zhiyuan Liang", "Pengfei Zhou", "Yang You", "Wangbo Zhao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1859/"
doi: "10.18653/v1/2026.acl-long.1859"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tang-2026-grouptom-bench-benchmarking-group-theory/"
---
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# GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs

**Authors:** Weidong Tang, [Jierui Li](https://md.research.elamur.ai/free/en/authors/jierui-li/), Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, [Yang You](https://md.research.elamur.ai/free/en/authors/yang-you/) and [Wangbo Zhao](https://md.research.elamur.ai/free/en/authors/wangbo-zhao/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1859/) · [PDF](https://aclanthology.org/2026.acl-long.1859.pdf) · [DOI 10.18653/v1/2026.acl-long.1859](https://doi.org/10.18653/v1/2026.acl-long.1859) · [OpenAlex](https://openalex.org/W7166860370)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multimodal LLMs have become better at inferring what a single agent believes or wants. Real social settings demand more: forecasting how private beliefs and goals interact under hierarchy, conformity and information asymmetry, often producing group results nobody intended, such as groupthink or the Abilene Paradox. GroupToM-Bench is presented as the first multimodal benchmark for this group-level Theory of Mind (ToM). Its expert-written scenarios combine visual cues, such as facial expressions or how people are positioned, with dialogue and hidden private states.

Evaluation follows a causal chain with three stages:
- micro-level BDI, i.e., what each individual believes, desires and intends,
- meso-level frictions among members and the structural limits the group operates under,
- macro-level outcome forecasting and attribution of causes.

This chain is operationalized as a seven-level cognitive audit. Frontier models score clearly below humans. They recover individual motives fairly well, yet tend to predict an optimistic, rational consensus, as if a group were simply the sum of its members. The authors call this linear superposition bias and argue that scaling has not removed it. The benchmark therefore serves as a diagnostic tool for socially grounded AI.

## Key points
- The resource contains 240 expert-curated scenarios in eight socio-psychological domains (e.g., information, belief, dilemmas, emotion, conformity, power, identity, inertia), with 3,120 tasks in total.
- The audit has seven levels. L1–L3 test individual belief, desire and intention before any interaction. L4–L7 cover tension, constraint, collective prediction and mechanistic attribution.
- Open-ended answers at L5 and L7 are graded by GPT-5 against expert gold references. The authors acknowledge a possible self-scoring bias, since GPT-5 is also among the evaluated models.
- Visual dependence is uneven across the benchmark. In an image-removal ablation, GPT-4o drops only about 1.9 points on average.
- Stated limitations include mostly Western social norms in the scenarios and a possible link between safety alignment and models' reluctance to predict collective failure.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) — University, Singapore, Singapore · 207 works · 39,576 citations
- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 145 works · 114,409 citations
- [University of Electronic Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) — University, Chengdu, China · 38 works · 2,088 citations
- [Xidian University](https://md.research.elamur.ai/free/en/universities/xidian-university/) — University, Xi'an, China · 17 works · 1,357 citations

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## Cite

Cite as: Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You and Wangbo Zhao. 2026. GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tang2026grouptom,
  title = {{GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs}},
  author = {Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You and Wangbo Zhao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1859},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1859}
}
```

- [← previous GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs](https://md.research.elamur.ai/free/en/papers/long-2026-grouprank-groupwise-paradigm-effective-efficient/)
- [next → GRUFF: LLM Pronoun Fidelity, Reasoning, and Biases in German](https://md.research.elamur.ai/free/en/papers/mewes-2026-gruff-llm-pronoun-fidelity-reasoning/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/)

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*Updated 1 November 2026*