1 citations · 1 across the 7 of their papers we have counts for
8 papers
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
Jiacheng Shen, Masato Hagiwara, Milad Alizadeh +9
Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models handle specialized animal-re…
RedVLA: Physical Red Teaming for Vision-Language-Action Models
Yuhao Zhang, Borong Zhang, Jiaming Fan +4
The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, we currently lack effective m…
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
Lorenzo Magnino, Jiacheng Shen, Matthieu Geist +2
The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However,…
VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
Borong Zhang, Jiahao Li, Jiachen Shen +8
While Vision-Language-Action models (VLAs) are rapidly advancing toward generalist robot policies, quantitatively characterizing their capability boundaries and failure modes remai…
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Lorenzo Magnino, Kai Shao, Zida Wu +2
Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL…
Rejoining fragmented ancient bamboo slips with physics-driven deep learning
Jinchi Zhu, Zhou Zhao, Hailong Lei +8
Bamboo slips are a crucial medium for recording ancient civilizations in East Asia, and offers invaluable archaeological insights for reconstructing the Silk Road, studying materia…
Reinforcement Learning for Finite Space Mean-Field Type Games
Kai Shao, Jiacheng Shen, Mathieu Laurière
Mean field type games (MFTGs) describe Nash equilibria between large coalitions: each coalition consists of a continuum of cooperative agents who maximize the average reward of the…
CM-DQN: A Value-Based Deep Reinforcement Learning Model to Simulate Confirmation Bias
Jiacheng Shen, Lihan Feng
In human decision-making tasks, individuals learn through trials and prediction errors. When individuals learn the task, some are more influenced by good outcomes, while others wei…