Papers by Yutong Song
DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation (2026.findings-acl)
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Yutong Song, Jiang Wu, Kazi Shaharair Sharif, Pengfei Zhang, Wenjun Huang, Honghui Xu, Nikil Dutt, Amir M. Rahmani
| Challenge: | Simulating dementia patients with large language models is challenging due to the need to model cognitive impairment, emotional dynamics, and nonverbal behaviors over long conversations. |
| Approach: | They propose an expert-guided dementia dialogue agent for multi-turn patient simulation . they introduce a framework that trains a single LLM to jointly generate reasoning traces, patient utterances, and aligned behavioral actions . |
| Outcome: | The proposed model outperforms baselines in persona fidelity, clinical validity, and educational effectiveness. |
From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG (2026.findings-acl)
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| Challenge: | Existing multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities, making failures unverifiable. |
| Approach: | They propose a multimodal benchmark that features real-world landmarks with annotations across multiple viewpoints and a framework that treats visual elements as first-class retrieval units through three stages: element-level detection and classification, multi-granularity cross-modal alignment for evidence retrieval, and attribution-constrained generation. |
| Outcome: | The proposed framework achieves up to 29.2% improvement over six strong baselines for this task. |
A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)
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| Challenge: | Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies. |
| Approach: | They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization. |
| Outcome: | The proposed framework achieves state-of-the-art on negotiation datasets showing that it improves negotiation ability. |
Cost-Optimal Grouped-Query Attention for Long-Context Modeling (2025.emnlp-main)
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Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai, Xingyu Shen, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Current GQA configurations overlook how context length influences inference cost . |
| Approach: | They propose a recipe for deriving cost-optimal GQA configurations that decouple the total head size from the hidden size and allow more flexible control over attention FLOPs. |
| Outcome: | The proposed configurations reduce memory usage and FLOPs by more than 50% compared to Llama-3's GQA, with *no degradation in model capabilities*. |