AAAI 2026technical0 citations
MovieGraph-ToM: Evaluating Long-Range Theory of Mind in Large Language Models via Implicit Social-Causal Graphs
Tingjiang Wei, Qin Ni, Rong Gao, Yingying Wang, Liang He
Abstract
The capacity for social reasoning, particularly Theory of Mind (ToM), is a foundational prerequisite for aligning Large Language Models (LLMs) with human values. However, current evaluations are predominantly confined to simplistic, short-text scenarios, obscuring their true capabilities and potential failure modes in complex, long-range social dynamics. To address this deficit, we introduce MovieGraph-ToM, a large-scale benchmark for evaluating long-range ToM and social cognition within extended, multimodal narratives. We employ a "scaffold-and-probe" methodology: we construct a ground-truth Social-Causal Graph offline, which maps the narrative
BibTeX
@inproceedings{aaai2026_moviegraphtomeva,
title = {MovieGraph-ToM: Evaluating Long-Range Theory of Mind in Large Language Models via Implicit Social-Causal Graphs},
author = {Tingjiang Wei and Qin Ni and Rong Gao and Yingying Wang and Liang He},
booktitle = {AAAI 2026},
year = {2026}
}