4 citations · 14 across the 9 of their papers we have counts for
4 papers · 1 filter
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
Lianhao Zhou, Hongyi Ling, Cong Fu +14
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous sys…
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
Lianhao Zhou, Hongyi Ling, Keqiang Yan +5
We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within prede…
Complex LLM Planning via Automated Heuristics Discovery
Hongyi Ling, Shubham Parashar, Sambhav Khurana +6
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on…
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights
Shubham Parashar, Blake Olson, Sambhav Khurana +4
We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential…