1 citations · 1 across the 2 of their papers we have counts for
6 papers
Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
Zhe Yu, Wenpeng Xing, Chen Ye +4
Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness whe…
The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context
Zhe Yu, Wenpeng Xing, Yunzhao Wei +4
Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retrieved context actually govern…
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
Kyle Zheng, Han Zhang, Renliang Sun +2
Efficient reasoning is not only a matter of shortening an answer trace; for tool-using agents, it also depends on whether the agent is reasoning over the right action space. As API…
BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation
Fuyi Yang, Chenchen Ye, Mingyu Derek Ma +3
Hypothesis generation in biomedical research has traditionally centered on uncovering hidden relationships within vast scientific literature, often using methods like Literature-Ba…
MIRAI: Evaluating LLM Agents for Event Forecasting
Chenchen Ye, Ziniu Hu, Yihe Deng +4
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making
Mingyu Derek Ma, Chenchen Ye, Yu Yan +4
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency…