10 papers
Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
Haoyi Qiu, Yilun Zhou, Pranav Narayanan Venkit +4
As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2…
GTA: Generating Long-Horizon Tasks for Web Agents at Scale
Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey +4
Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable,…
Direct Judgement Preference Optimization
Peifeng Wang, Austin Xu, Yilun Zhou +2
Auto-evaluation is crucial for assessing response quality and offering feedback for model development. Recent studies have explored training large language models (LLMs) as generat…
DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou +3
Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing ci…
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization
Austin Xu, Yilun Zhou, Xuan-Phi Nguyen +2
To keep pace with the increasing pace of large language models (LLM) development, model output evaluation has transitioned away from time-consuming human evaluation to automatic ev…
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
Yilun Zhou, Austin Xu, Peifeng Wang +2
Scaling test-time computation, or affording a generator large language model (LLM) extra compute during inference, typically employs the help of external non-generative evaluators…