6 citations · 10 across the 10 of their papers we have counts for
12 papers
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…
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Taro Yano, Yoichi Ishibashi, Masafumi Oyamada
Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techn…
Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have demonstrated significant improvements in reasoning capabilities through supervised fine-tuning and reinforcement learning. However, when training…
DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
Yunzhen He, Yusuke Takase, Yoichi Ishibashi +1
Large Language Models (LLMs) are increasingly being used in real-world applications. However, concerns about the reliability of the content they generate persist, as it frequently…
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have achieved remarkable capabilities, yet their improvement methods remain fundamentally constrained by human design. We present Self-Developing, a fr…
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
Yoichi Ishibashi, Yoshimasa Nishimura
Recent advancements in automatic code generation using large language model (LLM) agent have brought us closer to the future of automated software development. However, existing si…
Knowledge Sanitization of Large Language Models
Yoichi Ishibashi, Hidetoshi Shimodaira
We explore a knowledge sanitization approach to mitigate the privacy concerns associated with large language models (LLMs). LLMs trained on a large corpus of Web data can memorize…
Evaluating the Robustness of Discrete Prompts
Yoichi Ishibashi, Danushka Bollegala, Katsuhito Sudoh +1
Discrete prompts have been used for fine-tuning Pre-trained Language Models for diverse NLP tasks. In particular, automatic methods that generate discrete prompts from a small set…
Subspace Representations for Soft Set Operations and Sentence Similarities
Yoichi Ishibashi, Sho Yokoi, Katsuhito Sudoh +1
In the field of natural language processing (NLP), continuous vector representations are crucial for capturing the semantic meanings of individual words. Yet, when it comes to the…
Reflection-based Word Attribute Transfer
Yoichi Ishibashi, Katsuhito Sudoh, Koichiro Yoshino +1
Word embeddings, which often represent such analogic relations as king - man + woman = queen, can be used to change a word's attribute, including its gender. For transferring king…