2 citations · 3 across the 7 of their papers we have counts for
9 papers · 1 filter
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…
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
Xuhui Zhou, Hyunwoo Kim, Faeze Brahman +9
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examini…
Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues
Myke C. Cohen, Zhe Su, Hsien-Te Kao +4
This paper presents an evaluation framework for agentic AI systems in mission-critical negotiation contexts, addressing the need for AI agents that can adapt to diverse human opera…
The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
Scott Geng, Hamish Ivison, Chun-Liang Li +4
Improvements in language models are often driven by improving the quality of the data we train them on, which can be limiting when strong supervision is scarce. In this work, we sh…
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents
Jen-tse Huang, Jiaxu Zhou, Tailin Jin +6
Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents, each focusing on a specific domain. Howeve…
AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents
Zhe Su, Xuhui Zhou, Sanketh Rangreji +4
Truthfulness (adherence to factual accuracy) and utility (satisfying human needs and instructions) are both fundamental aspects of Large Language Models, yet these goals often conf…