activity
20182026
most citedCORD-19: The COVID-19 Open Research Dataset

584 citations · 910 across the 50 of their papers we have counts for

collaborators

54 papers

cs.AI2026

HERA: Harness-Environment Co-Evolution for Reliable Agentic Abstention

Han Luo, Bingbing Wen, Guang Yang +3

Large language model (LLM) agents are increasingly capable of acting in complex tool-use environments, yet they often fail to recognize when tasks are infeasible and no valid solut…

cs.CL2026

You're Hired: Strategic Model Selection for LLM Collaboration

Zongwan Cao, Ziyuan Yang, Shangbin Feng +5

While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems remain bottlenecked on pre-def…

cs.HC2026

Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists

Arnavi Chheda-Kothary, Lucy Lu Wang, Joseph Chee Chang +1

Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text. While blind or low-vision (BLV) scientists have tradi…

cs.AI2026

Agentic Abstention: Do Agents Know When to Stop Instead of Act?

Han Luo, Bingbing Wen, Lucy Lu Wang

LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable…

cs.CL2026

Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation

Katelyn Xiaoying Mei, Yi-Li Hsu, Minjoon Choi +5

Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-…

cs.CL2026

ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?

Joey Chan, Yikun Han, Jingyuan Chen +8

Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' info…