2 citations · 5 across the 23 of their papers we have counts for
11 papers · 1 filter
Debugging Tabular Log as Dynamic Graphs
Chumeng Liang, Zhanyang Jin, Zahaib Akhtar +3
Tabular log abstracts objects and events in the real-world system and reports their updates to reflect the change of the system, where one can detect real-world inconsistencies eff…
ResearchArcade: Graph Interface for Academic Tasks
Jingjun Xu, Chongshan Lin, Haofei Yu +2
Academic research generates diverse data sources, and as researchers increasingly use machine learning to assist research tasks, a crucial question arises: Can we build a unified d…
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…
The Ramon Llull's Thinking Machine for Automated Ideation
Xinran Zhao, Boyuan Zheng, Chenglei Si +8
This paper revisits Ramon Llull's Ars combinatoria - a medieval framework for generating knowledge through symbolic recombination - as a conceptual foundation for building a modern…
ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities
Zhaochen Hong, Haofei Yu, Jiaxuan You
Evaluating consistency in large language models (LLMs) is crucial for ensuring reliability, particularly in complex, multi-step interactions between humans and LLMs. Traditional se…
Beyond Facts: Evaluating Intent Hallucination in Large Language Models
Yijie Hao, Haofei Yu, Jiaxuan You
When exposed to complex queries containing multiple conditions, today's large language models (LLMs) tend to produce responses that only partially satisfy the query while neglectin…
Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
Zijia Liu, Peixuan Han, Haofei Yu +2
Large Language Models (LLMs) demonstrate impressive capabilities but lack robust temporal intelligence, struggling to integrate reasoning about the past with predictions and plausi…
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
Kunlun Zhu, Jiaxun Zhang, Ziheng Qi +6
Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concern…
Synthetic Data RL: Task Definition Is All You Need
Yiduo Guo, Zhen Guo, Chuanwei Huang +5
Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduc…
Table as Thought: Exploring Structured Thoughts in LLM Reasoning
Zhenjie Sun, Naihao Deng, Haofei Yu +1
Large language models' reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to g…