# More Is Better: A MoE-Based Emotion Recognition Framework with Human Preference Alignment

arXiv: https://arxiv.org/abs/2508.06036 · HTML: https://mlsanity.com/paper/2508.06036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical LLM alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/)
- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Builds on (most cited first, 14 of 14)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning](https://mlsanity.com/paper/2406.11161/) (2024)
- [FG-CLIP: Fine-Grained Visual and Textual Alignment](https://mlsanity.com/paper/2505.05071/) (2025)
- [MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning](https://mlsanity.com/paper/2304.08981/) (2023)
- [MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition](https://mlsanity.com/paper/2404.17113/) (2024)
- [Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout](https://mlsanity.com/paper/2409.07078/) (2024)
- [Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples](https://mlsanity.com/paper/2409.04447/) (2024)
- [Improving Multimodal Emotion Recognition by Leveraging Acoustic Adaptation and Visual Alignment](https://mlsanity.com/paper/2409.05015/) (2024)
- [Audio-Guided Fusion Techniques for Multimodal Emotion Analysis](https://mlsanity.com/paper/2409.05007/) (2024)
- [Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better](https://mlsanity.com/paper/2409.18971/) (2024)
- [Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles](https://mlsanity.com/paper/2505.16784/) (2025)

## Built on by (most cited first, 4 of 4)

- [Massively Multimodal Foundation Models: A Framework for Capturing Interactions with Specialized Mixture-of-Experts](https://mlsanity.com/paper/2509.25678/) (2025)
- [VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents](https://mlsanity.com/paper/2602.00122/) (2026)
- [SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2603.01431/) (2026)
- [Omni IIE Bench: Benchmarking the Practical Capabilities of Image Editing Models](https://mlsanity.com/paper/2603.16944/) (2026)

## Abstract (arXiv)

In this paper, we present our solution for the semi-supervised learning track (MER-SEMI) in MER2025. We propose a comprehensive framework, grounded in the principle that "more is better," to construct a robust Mixture of Experts (MoE) emotion recognition system. Our approach integrates a diverse range of input modalities as independent experts, including novel signals such as knowledge from large Vision-Language Models (VLMs) and temporal Action Unit (AU) information. To effectively utilize unlabeled data, we introduce a consensus-based pseudo-labeling strategy, generating high-quality labels from the agreement between a baseline model and Gemini, which are then used in a two-stage training paradigm. Finally, we employ a multi-expert voting ensemble combined with a rule-based re-ranking process to correct prediction bias and better align the outputs with human preferences. Evaluated on the MER2025-SEMI challenge dataset, our method achieves an F1-score of 0.8772 on the test set, ranking 2nd in the track. Our code is available at https://github.com/zhuyjan/MER2025-MRAC25.