1 citations · 1 across the 5 of their papers we have counts for
5 papers
MUSE: A Unified Agentic Harness for MLLMs
Jianglin Lu, Hailing Wang, Xu Ma +4
Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze from a screenshot or selecting t…
Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models
Mingyuan Zhang, Yue Bai, Yifan Wang +2
Fine-tuning has become the dominant paradigm for adapting Vision-Language Models (VLMs), yet most approaches rely on explicit weight updates that introduce a fundamental trade-off.…
Cautious Next Token Prediction
Yizhou Wang, Lingzhi Zhang, Yue Bai +7
Next token prediction paradigm has been prevailing for autoregressive models in the era of LLMs. The current default sampling choice for popular LLMs is temperature scaling togethe…
Trajectory Prediction Meets Large Language Models: A Survey
Yi Xu, Ruining Yang, Yitian Zhang +5
Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and…
Boosting Large Language Models with Mask Fine-Tuning
Mingyuan Zhang, Yue Bai, Huan Wang +4
The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether maintaining the model integrity is \text…