@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning
%A Zhang, Fengyu
%A Liu, Bin
%A Tao, Jianhua
%A Wen, Zhuofan
%A Chen, Shun
%A Yao, Hailiang
%A Wen, Zhengqi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-cair
%X 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.
%U https://aclanthology.org/2026.findings-acl.1263/
%P 25249-25264
Markdown (Informal)
[CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning](https://aclanthology.org/2026.findings-acl.1263/) (Zhang et al., Findings 2026)
ACL
- Fengyu Zhang, Bin Liu, Jianhua Tao, Zhuofan Wen, Shun Chen, Hailiang Yao, and Zhengqi Wen. 2026. CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25249–25264, San Diego, California, United States. Association for Computational Linguistics.