works on

From the 1 of 21 linked papers with an AI index.

collaborators

21 papers

cs.LG2026

Towards Understanding On-Policy Distillation through the Lens of Test-Time Scaling

Xinmu Ge, Zizhuo Zhang, Yu Huang +9

On-policy distillation (OPD) has emerged as a promising post-training technique for enhancing LLM reasoning. It is commonly believed to enable the student model to distill knowledg…

cs.CL2026

SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay

Guilin Li, Jiaxing Zhang, Matthias Hwai Yong Tan +2

Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful acti…

cs.CV2026

CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition

Lai Wei, Chengqi Li, Jiapeng Li +3

Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as co…

cs.AI2026

Evidence-Grounded AI for Musculoskeletal Care

Wenjie Li, Yujie Zhang, Fanrui Zhang +34

The paper presents OrthoPilot, a clinical AI system powered by a large language model that integrates real-time hospital data and external medical knowledge to provide evidence‑bas…

cs.CL2026

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG

Haotian Zhou, Weiran Huang, Siqi Liu +3

Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In…

cs.CV2026

Black-Box Continual Learning for Vision-Language Models

Yuting Li, Weihang Fang, Haoyuan Gao +4

The rapid deployment of Vision-Language Models (VLMs) in dynamic environments necessitates the ability to learn continuously without forgetting. However, traditional continual lear…