1 citations · 2 across the 6 of their papers we have counts for
4 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…
Revisiting Observation Reduction for Web Agents: Comprehensive Evaluation with a Lightweight Framework
Masafumi Enomoto, Ryoma Obara, Haochen Zhang +1
HTML observations in LLM-based web agents are extremely long, and while many reduction methods have been proposed, it remains unclear which methods reduce overall agent latency whi…
Read More, Think More: Revisiting Observation Reduction for Web Agents
Masafumi Enomoto, Ryoma Obara, Haochen Zhang +1
Web agents based on large language models (LLMs) rely on observations of web pages -- commonly represented as HTML -- as the basis for identifying available actions and planning su…
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain
Shintaro Ozaki, Yuta Kato, Siyuan Feng +8
Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This app…