Papers by Tian Xueyun
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization (2026.acl-long)
Copied to clipboard
Tian Xueyun, MingHua Ma, Bingbing Xu, Nuoyan Lyu, Wei Li, Heng Dong, Zheng Chu, Yuanzhuo Wang, Huawei Shen
| Challenge: | Recent studies show that supervised fine-tuning (SFT) is a common approach for reasoning in large language models. |
| Approach: | They propose to use supervised fine-tuning (SFT) on chain-of-thought trajectories demonstrations . they find that incorporating negative traxories yields substantial OOD generalization gains . |
| Outcome: | The proposed scheme yields 5.51% OOD gain over positive-only training. |
Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms for large language model (LLM) agents use memory construction and retrieval-augmented generation. |
| Approach: | They propose a framework that advocates for a paradigm shift toward lightweight construction paired with sophisticated utilization. |
| Outcome: | Experiments show that CoM outperforms baselines with accuracy gains of 7.5%–10.4% while reducing computational overhead to approximately 2.7% of token consumption and 6.0% of latency compared to complex memory architectures. |
ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing omni-multimodal large language models lack incomplete modality support or lack autonomous proactive monitoring. |
| Approach: | They propose a real-time omni-multimodal assistant for unified reactive and proactive interaction that decouples response initiation from generation to ensure precise triggering without task conflict. |
| Outcome: | The proposed model achieves state-of-the-art performance on proactive tasks while competing in reactive settings. |