activity
20232026
collaborators

7 papers

cs.SE2026

DeployBench: Benchmarking LLM Agents for Research Artifact Deployment

Yuanli Wang, Yaoyao Qian, Yue Zhang +8

LLM agents have made rapid progress on software engineering and ML research tasks, but these advances often assume access to a working runnable environment. For research artifacts…

cs.AI2026

ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces

Xiangyi Li, Kyoung Whan Choe, Yimin Liu +12

Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is r…

cs.AI2026

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

Xiangyi Li, Yimin Liu, Wenbo Chen +75

Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…

cs.SE2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…

cs.AI2025

WebGraphEval: Multi-Turn Trajectory Evaluation for Web Agents using Graph Representation

Yaoyao Qian, Yuanli Wang, Jinda Zhang +8

Current evaluation of web agents largely reduces to binary success metrics or conformity to a single reference trajectory, ignoring the structural diversity present in benchmark da…

cs.AI2025

WHEN TO ACT, WHEN TO WAIT: Modeling the Intent-Action Alignment Problem in Dialogue

Yaoyao Qian, Jindan Huang, Yuanli Wang +5

Dialogue systems often fail when user utterances are semantically complete yet lack the clarity and completeness required for appropriate system action. This mismatch arises becaus…

cs.CL2023

Trace and Edit Relation Associations in GPT

Jiahang Li, Taoyu Chen, Yuanli Wang

This study introduces a novel approach for analyzing and modifying entity relationships in GPT models, diverging from ROME's entity-focused methods. We develop a relation tracing t…