collaborators

6 papers

cs.CL2025

PersonaGym: Evaluating Persona Agents and LLMs

Vinay Samuel, Henry Peng Zou, Yue Zhou +6

Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user aligned interactions across domains like education and h…

cs.CL2025

Probing AI Safety with Source Code

Ujwal Narayan, Shreyas Chaudhari, Ashwin Kalyan +4

Large language models (LLMs) have become ubiquitous, interfacing with humans in numerous safety-critical applications. This necessitates improving capabilities, but importantly cou…

cs.AI2025

EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities

Talor Abramovich, Meet Udeshi, Minghao Shao +13

Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited.…

cs.AI2025

Agent Context Protocols Enhance Collective Inference

Devansh Bhardwaj, Arjun Beniwal, Shreyas Chaudhari +5

AI agents have become increasingly adept at complex tasks such as coding, reasoning, and multimodal understanding. However, building generalist systems requires moving beyond indiv…

cs.SE2024

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

John Yang, Carlos E. Jimenez, Alexander Wettig +4

Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as in…

cs.CL2024

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Carlos E. Jimenez, John Yang, Alexander Wettig +4

Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-w…