# Ауытты ескеріңіз: Ауыт негізіндегі сенімділікке байланысты фузия және адаптивті LLM сұраулары сөйлесу эмоцияларын тану үшін

> Толық мәтін ағылшын тілінде

**Published:** 2026 ж. 9 қараша
**Author:** Jingwen Wang, Jiao Li, Yuhang Qian, Yuyin Wang, Min Zhang
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/kk/articles/mind-the-divergence-divergence-guided-reliability-aware-fusion-and-adapt

arXiv:2611.08860v1 Жариялау түрі: жаңа Анотация: Сөйлесудегі мультимодальды эмоция тану (MERC) мәтіндік, дыбыстық және бейнелік белгілерді біріктіре отырып, сөйлем деңгейіндегі эмоцияларды анықтайды. Алайда, қолда бар подходтер жалпы алғанда мультимодальды өңдеуді нақты мақсаттарға бейімдемейді.

arXiv:2611.08860v1 Announce Type: new Abstract: Multimodal emotion recognition in conversations (MERC) integrates textual, acoustic, and visual cues to identify utterance-level emotions. However, existing approaches generally do not explicitly adapt multimodal processing to the degree of inter-modal predictive disagreement. In real conversations, semantic incongruity, concealed emotion, noisy observations, and ambiguity can affect modality-specific emotional evidence differently, while the reliability of individual modalities varies across samples. Rather than identifying each source of inconsistency separately, we model their common consequence for emotion recognition as predictive disagreement among unimodal emotion distributions over the same emotion-label set. Such disagreement makes indiscriminate fusion prone to propagating unreliable or competing evidence. Inspired by the Brunswik Lens Model, we propose MiD-ERC, a divergence-centric framework that quantifies inter-modal predictive disagreement using a normalized divergence score and estimates sample-specific modality reliability. MiD-ERC uses these signals to coordinate divergence-guided cross-modal gating, divergence-modulated reliability weighting, and divergence-conditioned adaptive LLM prompting. The framework is trained end-to-end using only standard emotion annotations, without requiring additional conflict labels, auxiliary data construction, or external retrieval modules. Experiments on IEMOCAP and MELD demonstrate that MiD-ERC achieves state-of-the-art performance, with particularly pronounced improvements on high-divergence samples.

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*Материал бастапқы дереккөзден алынған: [arXiv — cs.AI](https://arxiv.org/abs/2611.08860). Түпнұсқаны ашу.*

**Тегтер:** #Papers with Code #arXiv

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