AffectWorld: Benchmarking Emotion-Driven Future World Prediction
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arXiv:2609.24308v1 Announce Type: new Abstract: Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, int...
Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWor...
The paper introduces TRACE, a cognition-oriented framework that models affective episodes as a sequence of Condition, Affect, and Effect stages, incorporating observable cues and cognitive factors like appraisal and regulation. TRACE-Bench evaluates multimodal models on five tasks—grounded affect recognition, regulation decoding, cause reasoning, effect reasoning, and full-chain reconstruction—using 3,746 question-answer pairs from 646 videos. The authors also present TRACER, a structured reasoning method that links each inference to explicit premises, achieving superior performance across all tasks compared to existing baselines.
arXiv:2607. 00296v1 Announce Type: cross Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations.
arXiv:2609.36776v1 Announce Type: new Abstract: Emotional Video Captioning aims to generate factually accurate and emotionally empathetic descriptions. While recent methods have recognized the import...
arXiv:2606. 15032v1 Announce Type: new Abstract: World models have rapidly become one of the central abstractions in modern AI.