3 citations · 5 across the 8 of their papers we have counts for
8 papers
Prismata: Confining Cross-Site Prompt Injection in Web Agents
Corban Villa, Alp Eren Ozdarendeli, Sijun Tan +1
Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untruste…
: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen +14
Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in…
TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
Fangrui Huang, Souhad Chbeir, Arpandeep Khatua +8
Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks-…
Budget-aware Test-time Scaling via Discriminative Verification
Kyle Montgomery, Sijun Tan, Yuqi Chen +4
Test-time scaling is a powerful strategy for boosting the performance of large language models on complex reasoning tasks. While state-of-the-art approaches often employ generative…
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
Xiaoyuan Zhu, Yaowen Ye, Tianyi Qiu +6
As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little transparency into the deployed model. To re…
JudgeBench: A Benchmark for Evaluating LLM-based Judges
Sijun Tan, Siyuan Zhuang, Kyle Montgomery +5
LLM-based judges have emerged as a scalable alternative to human evaluation and are increasingly used to assess, compare, and improve models. However, the reliability of LLM-based…
Proof of Sampling: A Nash Equilibrium-Based Verification Protocol for Decentralized Systems
Yue Zhang, Shouqiao Wang, Sijun Tan +3
This paper introduces the Proof of Sampling (PoSP) protocol, a Nash Equilibrium-based verification mechanism, and its application to decentralized machine learning inference throug…
LLoCO: Learning Long Contexts Offline
Sijun Tan, Xiuyu Li, Shishir Patil +5
Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substan…