# EmoOmni: Bridging Emotional Understanding and Expression in Omni-Modal LLMs

arXiv: https://arxiv.org/abs/2602.21900 · HTML: https://mlsanity.com/paper/2602.21900/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models](https://mlsanity.com/paper/2607.03050/) (2026)
- [TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos](https://mlsanity.com/paper/2605.07593/) (2026)

## Abstract (arXiv)

The evolution of Omni-Modal Large Language Models~(Omni-LLMs) has revolutionized human--computer interaction, enabling unified audio-visual perception and speech response. However, existing Omni-LLMs struggle with complex real-world scenarios, often leading to superficial understanding and contextually mismatched emotional responses. This issue is further intensified by Omni-LLM's Thinker-Talker architectures, which are implicitly connected through hidden states, leading to the loss of emotional details. In this work, we present EmoOmni, a unified framework for accurate understanding and expression in multimodal emotional dialogue. At its core, we introduce the emotional Chain-of-Thought~(E-CoT), which enforces a reasoning from fine-grained multimodal perception to textual response. Moreover, we explicitly treat E-CoT as high-level emotional instructions that guide the talker, enabling accurate emotional expression. Complementing the model, we construct EmoOmniPipe to obtain the real-world annotated dialogue data and establish a benchmark, EmoOmniEval, to facilitate systematic assessment of multimodal emotional dialogue task. Experiments show that EmoOmni-7B achieves comparable performance with Qwen3Omni-30B-A3B-Thinking under the same talker.