11 papers
One-Sided Verification Under Limited Expert Review
Fan Nie, Haotian Ye, Shurui Liu +3
AI has become increasingly helpful in scientific discovery, yet human expert review remains essential for those problems without reliable automated evaluation, posing a fundamental…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
Can Language Models Discover Scaling Laws?
Haowei Lin, Haotian Ye, Wenzheng Feng +8
Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To invest…
Inference-time Scaling of Diffusion Models through Classical Search
Xiangcheng Zhang, Haowei Lin, Haotian Ye +4
Classical search algorithms have long underpinned modern artificial intelligence. In this work, we tackle the challenge of inference-time control in diffusion models -- adapting ge…
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
Yuzheng Hu, Fan Wu, Haotian Ye +5
Online reinforcement learning (RL) excels in complex, safety-critical domains but suffers from sample inefficiency, training instability, and limited interpretability. Data attribu…
Generative Evaluation of Complex Reasoning in Large Language Models
Haowei Lin, Xiangyu Wang, Ruilin Yan +7
With powerful large language models (LLMs) demonstrating superhuman reasoning capabilities, a critical question arises: Do LLMs genuinely reason, or do they merely recall answers f…
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Haotian Ye, Himanshu Jain, Chong You +4
In real-world applications of large language models, outputs are often required to be confined: selecting items from predefined product or document sets, generating phrases that co…
Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors
Fan Nie, Lan Feng, Haotian Ye +5
Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often imp…
A Foundational Generative Model for Breast Ultrasound Image Analysis
Haojun Yu, Youcheng Li, Nan Zhang +20
Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains unt…
TFG: Unified Training-Free Guidance for Diffusion Models
Haotian Ye, Haowei Lin, Jiaqi Han +6
Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desira…
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Haowei Lin, Baizhou Huang, Haotian Ye +7
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine…