arXiv subjects

Erqiang Wang

Publications and source records attributed to Erqiang Wang.

2 recordsLinked to original sources

arXiv2026

AffToM: Benchmarking Recursive Affective Theory of Mind in Dynamic Multi-Party Interactions

Understanding emotion in realistic social interactions requires more than recognizing facial expressions or assigning affective labels. The same event can elicit different emotions depending on what each participant knows, what they believe others know or feel, and how these nested mental states evolve over time. Existing benchmarks largely study higher-order epistemic Theory of Mind (ToM) and multimodal emotion reasoning in isolation, leaving recursive affective reasoning under asymmetric and dynamically changing information underexplored. Therefore, we introduce AffToM, a benchmark for affective ToM with 1,922 test pairs and 19,759 training pairs. AffToM evaluates Individual Affective Reasoning (IAR), which infers emotions from events and socio-cognitive context, and Multi-party Affective Reasoning (MPAR), which recursively tracks participants' nested beliefs and emotions as new evidence emerges. We develop an event-driven multi-agent generation pipeline grounded in dynamic character interaction graphs that explicitly capture event sequences, interpersonal relations, and mental-state updates. Furthermore, we propose AffToM-R1, a two-stage post-training framework combining supervised fine-tuning with perspective-aware rubric-guided reinforcement learning. Experiments show that MPAR remains challenging, with even the strongest evaluated MLLM achieving less than 60% accuracy, while AffToM-R1 improves MPAR accuracy by 13.38 percentage points over its base model. Our dataset is available at: https://github.com/MindIntLab-HFUT/AffToM.

cs.AI↗

arXiv2026

MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos

Mental health understanding in long-form videos requires nuanced reasoning over observable behavior, interpersonal context, and latent psychological states. Existing benchmarks largely reduce this task to coarse-grained classification, providing limited insight into whether models truly understand psychological phenomena or rely on superficial correlations. To address this limitation, we introduce MMHBench, a comprehensive multimodal benchmark for multi-perspective mental health understanding, comprising 268 long-form videos and 2,184 carefully curated questions. MMHBench organizes the evaluation into two complementary settings: (1) third-person assessment, consisting of 605 questions that focus on the interpretation of observable behaviors and multimodal evidence, and (2) first-person perspective-taking, comprising 1,579 questions that require perspective-conditioned reasoning to identify the interpretation of the mental state supported by the available multimodal evidence. We propose a Multi-Agent Question Generation (MAQG) framework that simulates diverse social roles to synthesize questions from multiple perspectives. The generated questions are refined through multi-role feedback and iterative optimization, followed by expert-guided verification to ensure quality and validity. Extensive evaluation of 22 representative multimodal large language models (MLLMs), spanning both open-source and leading closed-source models, demonstrates that long-form video mental health understanding remains highly challenging.

cs.AI↗