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20242026
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cs.CL2026

Mining Useful General Data for Low-Resource Domain Adaptation

Pingjie Wang, Hongcheng Liu, Yusheng Liao +5

Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…

cs.CL2026

LaSR: Context-Aware Speech Recognition via Latent Reasoning

Heyang Liu, Ziyang Cheng, Jiayi Huang +5

Recent advances in Speech Large Language Models (Speech LLMs) have significantly enhanced spoken language understanding and reasoning. However, their contextual awareness is limite…

cs.CL2026

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks

Zhe Chen, Yusheng Liao, Zhiyuan Zhu +4

Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world…

cs.CL2026

Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs

Hongcheng Liu, Yuhao Wang, Zhe Chen +5

Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring syn…

cs.CL2026

Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling

Shuyang Jiang, Yusheng Liao, Ya Zhang +2

While large reasoning models trained with critic-free reinforcement learning and verifiable rewards (RLVR) represent the state-of-the-art, their practical utility is hampered by ``…

cs.CL2026

VocalNet-MDM: Accelerating Streaming Speech LLM via Self-Distilled Masked Diffusion Modeling

Ziyang Cheng, Yuhao Wang, Heyang Liu +4

Recent Speech Large Language Models~(LLMs) have achieved impressive capabilities in end-to-end speech interaction. However, the prevailing autoregressive paradigm imposes strict se…