most citedMeasuring Agents in Production

3 citations · 4 across the 9 of their papers we have counts for

collaborators

19 papers

cs.CR2026

Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens

Peizhi Niu, Wenjie Qu, Shangding Gu +14

Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities…

cs.AI2026

AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

Xiaoyuan Liu, Jianhong Tu, Yuqi Chen +26

Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, cr…

cs.AI2026

Agents' Last Exam

Yiyou Sun, Xinyang Han, Weichen Zhang +306

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…

cs.CY20263 cited

Measuring Agents in Production

Melissa Z. Pan, Negar Arabzadeh, Riccardo Cogo +22

LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful. We present the first syst…

cs.CR2026

CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities

Tianneng Shi, Robin Rheem, Dongwei Jiang +13

AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity e…

cs.CR2026

Progent: Securing AI Agents with Privilege Control

Tianneng Shi, Jingxuan He, Zhun Wang +4

AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger unauthorized actions. Securing these agen…