5 citations · 5 across the 9 of their papers we have counts for
9 papers
Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs
Mingyu Jin, Yutong Yin, Jingcheng Niu +7
In this work, we investigate how Large Language Models (LLMs) adapt their internal representations when encountering inputs of increasing difficulty, quantified as the degree of ou…
Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training
Zhengyao Gu, Jonathan Light, Raul Astudillo +7
Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and cha…
Agentic AI for Scalable and Robust Optical Systems Control
Zehao Wang, Mingzhe Han, Wei Cheng +12
We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural langu…
SkillGen: Learning Domain Skills for In-Context Sequential Decision Making
Ruomeng Ding, Wei Cheng, Minglai Shao +1
Large language models (LLMs) are increasingly applied to sequential decision-making through in-context learning (ICL), yet their effectiveness is highly sensitive to prompt quality…
Baichuan-M1: Pushing the Medical Capability of Large Language Models
Bingning Wang, Haizhou Zhao, Huozhi Zhou +39
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like…
ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Wei Cheng, Yucheng Lu, Boyang Xia +9
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typic…
KV Shifting Attention Enhances Language Modeling
Mingyu Xu, Wei Cheng, Bingning Wang +1
The current large language models are mainly based on decode-only structure transformers, which have great in-context learning (ICL) capabilities. It is generally believed that the…
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt
Peter Anderson, Mano Vikash Janardhanan, Jason He +2
Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. Yet, few text embeddings…
Calibrate to Discriminate: Improve In-Context Learning with Label-Free Comparative Inference
Wei Cheng, Tianlu Wang, Yanmin Ji +3
While in-context learning with large language models (LLMs) has shown impressive performance, we have discovered a unique miscalibration behavior where both correct and incorrect p…