In the context of today's high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more critical than ever. However, existing benchmarks fail to simultaneously achieve ecological validity, signal clarity, and reliable fine-grained labeling. We introduce EmoS, a high-fidelity bilingual benchmark designed to resolve the limitations of ecological validity and noise in existing datasets by combining strictly filtered static slices with a dynamic Streaming Monologue subset. Supported by a rigorous dual-layer human annotation pipeline, EmoS provides trusted ground truth that captures continuous emotional evolution. Empirical results show that fine-tuning MLLMs (multimodal large language models) on EmoS yields significant gains over zero-shot baselines, laying the foundation for the training and evaluation of future emotion recognition models and empathy models. The dataset and code are publicly available at https://github.com/NLP2CT/EmoS.
@article{arxiv.2605.08847,
title = {EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding},
author = {Pengze Guo and Jingxi Liang and Zhiwen Xie and Qifeng Wang and Derek F. Wong},
journal= {arXiv preprint arXiv:2605.08847},
year = {2026}
}