Paper

Omni-Perception Policy Optimization for Multimodal Emotion Reasoning

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arXiv:2606. 25325v1 Announce Type: new Abstract: We find that current emotion-oriented Omni-MLLMs still lack reliable omni-modal perception: they (i) underutilize multimodal cues in their reasoning trajectories and (ii) exhibit unfaithful behavior, often hallucinating modality-specific statements from other modalities.

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Authors: Zhiyuan Han, Beier Zhu, Wenwen Tong, Pengyang Shao, Peipei Song, Xinyi Wang, Jiangnan Chen, Lewei Lu, Xun Yang
Released: 2026-06-25 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Omni-Perception Policy Optimization for Multimodal Emotion Reasoning overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe". Matching terms: under, token, compare, full. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.25325

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