7 citations · 25 across the 18 of their papers we have counts for
6 papers · 1 filter
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
Batu El, Jinhee Paeng, Fatih Dinc +7
AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their interactions can improve co…
Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing
Jiacheng Miao, Jin Mu, Guanhua Chen +1
Reliable hypothesis testing is the foundation of many empirical scientific claims. Large language model (LLM) agents are increasingly used to automate this process, as they can ins…
The Agentic Garden of Forking Paths
Jiacheng Miao, Jonathan K Pritchard, James Zou
Empirical research rarely admits a unique analysis. Different analytical choices can lead to different conclusions from the same data, yet these hidden forking paths are difficult…
What LLMs Think When You Don't Tell Them What to Think About?
Yongchan Kwon, James Zou
Characterizing the behavior of large language models (LLMs) across diverse settings is critical for reliable monitoring and AI safety. However, most existing analyses rely on topic…
Multi-Agent Teams Hold Experts Back
Aneesh Pappu, Batu El, Hancheng Cao +4
Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-specified workflows. In such settings, ef…
Sparse Reward Subsystem in Large Language Models
Guowei Xu, Mert Yuksekgonul, James Zou
Recent studies show that LLM hidden states encode reward-related information, such as answer correctness and model confidence. However, existing approaches typically fit black-box…