5 citations · 10 across the 22 of their papers we have counts for
12 papers · 1 filter
OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models
Qiushi Sun, Kanzhi Cheng, Yian Wang +20
Computer-using agents (CUAs) are advancing rapidly across the digital world. A CUA trajectory records the agent's actions, states, and reasoning. Verifying whether it fulfilled the…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
Kai Chen, Zichen Ding, Jiaye Ge +20
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly…
MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop
Yikun Fu, Bowen Fu, Zhenyu Wu +10
Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, ex…
Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents
Jianxiang Yu, Jiapeng Zhu, Bochen Lin +3
LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon interactive tasks. Existing ski…
Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?
Yibo Zhao, Zichen Ding, Jiayi Wu +2
Search agents powered by large language models can autonomously decompose queries, retrieve information, and synthesize answers through multi-step reasoning. However, the rapid gro…
OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis
Kanzhi Cheng, Zehao Li, Zheng Ma +11
Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achieving a marked performance leap…