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arXiv:2610.28625v1 Announce Type: new Abstract: Understanding human needs and selecting responses require distinct judgments that aggregate accuracy can obscure. We introduce PsycheMME, a video bench...
arXiv:2604.01569v2 Announce Type: replace Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can...
arXiv:2606. 08239v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made substantial advancements in video understanding, yet the reliability of their responses remains underexplored.
SocialReasonBench is a new video‑multiple‑choice QA benchmark designed to test socially grounded reasoning in interactive narrative videos. It uses branching gameplay footage from *Detroit: Become Human*, where player choices create alternative social outcomes that can be verified against the game’s script and flowchart. The benchmark includes seven reasoning dimensions—such as intent recognition, emotional empathy, moral dilemma, counterfactual reasoning, and causal antecedent—and employs a multi‑agent pipeline to curate clips, ground answer labels, and generate theory‑guided questions with diagnostic distractors.
Omni-modal models can answer questions about video, but their predictions rarely show which participant, event, or interval supports an answer. Moreover, answer-level supervision alone does not teach...
LF-VideoQA is a new dataset for long‑form, open‑ended question answering over narrative videos, built around theory‑of‑mind questions that require reasoning across scenes averaging 23 minutes. It contains over 6,300 question‑answer‑rubric triples generated automatically from annotated screenplays and validated by humans. The dataset challenges current vision‑language models, showing that even oracle models fall short and that visual evidence is essential for answering these questions.