3 papers
cs.AI2025
QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models
Li Puyin, Tiange Xiang, Ella Mao +5
Understanding the physical world is essential for generalist AI agents. However, it remains unclear whether state-of-the-art vision perception models (e.g., large VLMs) can reason…
cs.LG2025
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning
Xinye Chen
This paper presents a novel reinforcement learning (RL) framework for dynamically optimizing numerical precision in the preconditioned conjugate gradient (CG) method. By modeling p…
cs.LG2024
LLM-ABBA: Understanding time series via symbolic approximation
Xinye Chen, Erin Carson, Cheng Kang
The success of large language models (LLMs) for time series has been demonstrated in previous work. Utilizing a symbolic time series representation, one can efficiently bridge the…