5 papers
WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning
Yizhou Chi, Eric Chamoun, Zifeng Ding +1
Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information. Yet evaluating whether agents genuinely forecast…
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
Xiaochen Zhu, Caiqi Zhang, Yizhou Chi +3
Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simp…
SciPaths: Forecasting Pathways to Scientific Discovery
Eric Chamoun, Yizhou Chi, Yulong Chen +4
Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generatio…
THOUGHTSCULPT: Reasoning with Intermediate Revision and Search
Yizhou Chi, Kevin Yang, Dan Klein
We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential sol…
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
Yizhou Chi, Yizhang Lin, Sirui Hong +9
Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks…