6 papers
PEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language Models
Junxin Wang, Yingfeng Luo, Xiangchen Song +12
Effective multimodal reasoning requires a model to reliably utilize visual information while sufficiently exploring diverse reasoning paths. To achieve this goal, prior work combin…
Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification
Yingfeng Luo, Shaowei Wei, Daixin Wang +7
Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments. Yet how trustworthy such sel…
Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs
Dingyang Lin, Yingfeng Luo, Chenglong Wang +4
Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answers into errors. We investig…
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
Yingfeng Luo, Hongyu Liu, Dingyang Lin +6
Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy…
AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models
Hankun Kang, Di Lin, Zhirong Liao +6
With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate responses across diverse cultures, bec…
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang +9
Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains chall…