7 papers
MIRAGE: Protecting against Malicious Image Editing via False Moderation
Anshul Nasery, Ramnath Kumar, Cho-Jui Hsieh +1
The proliferation of AI-powered image editing systems raises serious concerns because it allows personal images to be arbitrarily manipulated at scale, with minimal effort, and a l…
DualEval: Joint Model-Item Calibration for Unified LLM Evaluation
Aaron J. Li, Hao Huang, Youngmin Park +6
Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better r…
Do Prompt-Elicited Trajectories Reflect Training-Time Reward Hacking? A Systematic Study on Monitoring Training-Time Reward Hacking in Code Generation
Lichen Li, Hengguang Zhou, Yijun Liang +2
Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinf…
Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Ranking
Liwei Wu, Cho-Jui Hsieh
We present an AI Co-Scientist framework that closes the research loop for the production search-ranking system of a large online travel platform -- pairing LLM agents with direct c…
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Fei Wang, Si Si, Cho-Jui Hsieh +1
Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential. While evolutionary algorithms have eme…
Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training
Sohyun An, Shuibenyang Yuan, Hayeon Lee +2
Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches predominantly rely on gold…