Lijie Wen

Privilege-induced style drift can undermine reasoning model performance, but RLCSD effectively redirects the learning signal to focus on what truly matters—task-relevant tokens.

LLMs can now translate text in web images with significantly improved accuracy and efficiency thanks to a novel visual-aware adaptation framework that bridges the gap between high-level semantics and fine-grained visual details.

Targeted neuron fine-tuning can unlock superior image translation capabilities in multimodal large language models, outperforming traditional methods by preserving pre-trained knowledge.

LLM serving can achieve 5.6x higher throughput without sacrificing latency by decoupling preemption granularity from scheduling frequency.