4 papers
Can Large Language Models Forecast What Researchers Study Next?
Fenghai Li, Zihan Tang, Haofei Yu +2
Large language models increasingly generate research ideas, yet judging their novelty or feasibility at generation time does not establish whether they anticipate subsequent work.…
ResearchCube: Multi-Dimensional Trade-off Exploration for Research Ideation
Zijian Ding, Fenghai Li, Ziyi Wang +1
Research ideation requires navigating trade-offs across multiple evaluative dimensions, yet most AI-assisted ideation tools leave this multi-dimensional reasoning unsupported, or r…
LiveTradeBench: Seeking Real-World Alpha with Large Language Models
Haofei Yu, Fenghai Li, Jiaxuan You
Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, l…
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
Haofei Yu, Keyang Xuan, Fenghai Li +6
Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, plannin…