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cs.AI2026

How can we assess human-agent interactions? Case studies in software agent design

Valerie Chen, Rohit Malhotra, Xingyao Wang +7

While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases. In this pape…

cs.AI2026

Mind the Sim2Real Gap in User Simulation for Agentic Tasks

Xuhui Zhou, Weiwei Sun, Qianou Ma +8

As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, serving two roles: generating user…

cs.AI2026

Ambig-SWE: Interactive Agents to Overcome Underspecificity in Software Engineering

Sanidhya Vijayvargiya, Xuhui Zhou, Akhila Yerukola +2

AI agents are increasingly being deployed to automate tasks, often based on underspecified user instructions. Making unwarranted assumptions to compensate for the missing informati…

cs.AI2026

OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety

Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou +4

Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilitie…

cs.AI2025

Training Proactive and Personalized LLM Agents

Weiwei Sun, Xuhui Zhou, Weihua Du +5

Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communica…