# Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction

arXiv: https://arxiv.org/abs/2603.08230 · HTML: https://mlsanity.com/paper/2603.08230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 12 of 12)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization](https://mlsanity.com/paper/2501.03262/) (2025)
- [Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://mlsanity.com/paper/2503.02318/) (2025)
- [Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model](https://mlsanity.com/paper/2501.07246/) (2025)
- [SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning](https://mlsanity.com/paper/2504.15900/) (2025)
- [SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models](https://mlsanity.com/paper/2506.12935/) (2025)
- [AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models](https://mlsanity.com/paper/2409.18339/) (2024)
- [Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition](https://mlsanity.com/paper/2012.05429/) (2020)
- [Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation](https://mlsanity.com/paper/2402.12862/) (2024)
- [Token-Level Logits Matter: A Closer Look at Speech Foundation Models for Ambiguous Emotion Recognition](https://mlsanity.com/paper/2505.18484/) (2025)
- [Scaling Ambiguity: Augmenting Human Annotation in Speech Emotion Recognition with Audio-Language Models](https://mlsanity.com/paper/2601.14620/) (2026)

## Abstract (arXiv)

Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguous nature of human emotional expression. Recent large audio-language models show promise in generating richer outputs, but their reasoning ability for ambiguous emotional understanding remains limited. In this work, we reformulate ambiguous emotion recognition as a distributional reasoning problem and present the first systematic study of ambiguity-aware reasoning in LALMs. Our framework comprises two complementary components: an ambiguity-aware objective that aligns predictions with human perceptual distributions, and a structured ambiguity-aware chain-of-thought supervision that guides reasoning over emotional cues. Experiments on IEMOCAP and CREMA-D demonstrate consistent improvements across SFT, DPO, and GRPO training strategies.