18 citations · 30 across the 23 of their papers we have counts for
8 papers · 1 filter
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
Wenkang Wei, Yuan Fang, Renhe Jiang +2
How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on…
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
Zejun Liu, Jian Wu, Ru Peng +4
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focus…
HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
Yaozu Wu, Wei-Chieh Huang, Jizhou Guo +11
Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework…
PaperClaw: Harnessing Agents for Autonomous Research and Human-in-the-Loop Refinement
Weiwei Ye, Hangchen Liu, Dongyuan Li +1
Large language models have become capable reasoners and tool users that write and run code and search the literature, which makes automating the research process itself a realistic…
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
Hangchen Liu, Dongyuan Li, Renhe Jiang +3
Time series analysis underpins forecasting, monitoring, and decision making in domains such as finance and weather, where solving a task often requires both numerical accuracy and…
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…