@inproceedings{zhang-etal-2026-cair,
    title = "{CAIR}: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning",
    author = "Zhang, Fengyu  and
      Liu, Bin  and
      Tao, Jianhua  and
      Wen, Zhuofan  and
      Chen, Shun  and
      Yao, Hailiang  and
      Wen, Zhengqi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1263/",
    pages = "25249--25264",
    ISBN = "979-8-89176-395-1",
    abstract = "Multimodal emotion reasoning requires both accurate identification and logical rationales to explain emotional triggers. However, current methods often suffer from causal degeneracy, where models produce linguistically fluent but superficial explanations that lack authentic logical derivation. To resolve this, we propose CAIR (Causal Adaptive Information-based Reinforcement Learning), a reinforcement learning framework that treats rationales as causal mediators between raw perceptual signals and emotional semantics. Our core contribution is the Causal Mediation Reward (CMR), which quantifies a rationale{'}s interventional utility by measuring its marginal contribution to resolving predictive uncertainty. Additionally, we introduce an adaptive optimization mechanism based on the information bottleneck to balance perception and reasoning across varying cognitive loads. CAIR achieves state-of-the-art performance on MTMEUR with 73.80{\%} accuracy and competitive results on the SCEA subset of EmoBench-M (68.5{\%}), outperforming specialized SFT baselines by up to 14.4{\%} while enhancing rationale faithfulness. Our findings underscore that principled reward design, rather than mere model scaling, is essential for building systems with authentic, human-like emotional understanding."
}