2 citations · 2 across the 4 of their papers we have counts for
4 papers
FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search
James Xu Zhao, Hui Chen, Bryan Hooi +1
Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve thes…
Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet
James Xu Zhao, Bryan Hooi, See-Kiong Ng
Test-time scaling increases inference-time computation through longer reasoning chains and has shown strong performance gains across many domains. However, frontier models still su…
How Does Response Length Affect Long-Form Factuality
James Xu Zhao, Jimmy Z. J. Liu, Bryan Hooi +1
Large language models (LLMs) are widely used for long-form text generation. However, factual errors in the responses would undermine their reliability. Despite growing attention to…
Prompt Optimization via Adversarial In-Context Learning
Xuan Long Do, Yiran Zhao, Hannah Brown +6
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator…