6 citations · 10 across the 10 of their papers we have counts for
3 papers · 1 filter
Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems
Taro Yano, Yoichi Ishibashi, Masafumi Oyamada
Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly understood. We study Best-o…
Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review
Haowen Li, Yoichi Ishibashi, Masafumi Oyamada
Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types o…
Effective Harness Engineering for Algorithm Discovery with Coding Agents
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
AlphaEvolve and FunSearch have demonstrated the potential of combining large language models (LLMs) with evolutionary search for automated algorithm discovery. However, discovery s…