Saved in:
| Main Authors: | Yano, Taro, Ishibashi, Yoichi, Oyamada, Masafumi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.21963 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Effective Harness Engineering for Algorithm Discovery with Coding Agents
by: Ishibashi, Yoichi, et al.
Published: (2026)
by: Ishibashi, Yoichi, et al.
Published: (2026)
Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
by: Ishibashi, Yoichi, et al.
Published: (2025)
by: Ishibashi, Yoichi, et al.
Published: (2025)
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
by: Ishibashi, Yoichi, et al.
Published: (2024)
by: Ishibashi, Yoichi, et al.
Published: (2024)
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
by: Ishibashi, Yoichi, et al.
Published: (2024)
by: Ishibashi, Yoichi, et al.
Published: (2024)
An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability
by: Yamauchi, Yusuke, et al.
Published: (2025)
by: Yamauchi, Yusuke, et al.
Published: (2025)
Can a Crow Hatch a Falcon? Lineage Matters in Predicting Large Language Model Performance
by: Tamura, Takuya, et al.
Published: (2025)
by: Tamura, Takuya, et al.
Published: (2025)
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
by: Kim, Yubin, et al.
Published: (2024)
by: Kim, Yubin, et al.
Published: (2024)
Jellyfish: A Large Language Model for Data Preprocessing
by: Zhang, Haochen, et al.
Published: (2023)
by: Zhang, Haochen, et al.
Published: (2023)
Revisiting Observation Reduction for Web Agents: Comprehensive Evaluation with a Lightweight Framework
by: Enomoto, Masafumi, et al.
Published: (2026)
by: Enomoto, Masafumi, et al.
Published: (2026)
$M^3$ Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models
by: Akimoto, Kosuke, et al.
Published: (2024)
by: Akimoto, Kosuke, et al.
Published: (2024)
Read More, Think More: Revisiting Observation Reduction for Web Agents
by: Enomoto, Masafumi, et al.
Published: (2026)
by: Enomoto, Masafumi, et al.
Published: (2026)
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
by: Akimoto, Kosuke, et al.
Published: (2024)
by: Akimoto, Kosuke, et al.
Published: (2024)
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
by: Zhang, Jianguo, et al.
Published: (2024)
by: Zhang, Jianguo, et al.
Published: (2024)
EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training
by: Dorkin, Aleksei, et al.
Published: (2026)
by: Dorkin, Aleksei, et al.
Published: (2026)
Best-of-$\infty$ -- Asymptotic Performance of Test-Time LLM Ensembling
by: Komiyama, Junpei, et al.
Published: (2025)
by: Komiyama, Junpei, et al.
Published: (2025)
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by: Trirat, Patara, et al.
Published: (2024)
by: Trirat, Patara, et al.
Published: (2024)
Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance
by: Djuhera, Aladin, et al.
Published: (2025)
by: Djuhera, Aladin, et al.
Published: (2025)
EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training
by: Pan, Chengjun, et al.
Published: (2026)
by: Pan, Chengjun, et al.
Published: (2026)
Unified Mind Model: Reimagining Autonomous Agents in the LLM Era
by: Hu, Pengbo, et al.
Published: (2025)
by: Hu, Pengbo, et al.
Published: (2025)
Look Before You Leap: Autonomous Exploration for LLM Agents
by: Ye, Ziang, et al.
Published: (2026)
by: Ye, Ziang, et al.
Published: (2026)
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons
by: Hu, Renjun, et al.
Published: (2025)
by: Hu, Renjun, et al.
Published: (2025)
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
by: Tang, Shuo, et al.
Published: (2024)
by: Tang, Shuo, et al.
Published: (2024)
ActuBench: A Multi-Agent LLM Pipeline for Generation and Evaluation of Actuarial Reasoning Tasks
by: Schmidt, Jan-Philipp
Published: (2026)
by: Schmidt, Jan-Philipp
Published: (2026)
Domain Adaptation of LLMs for Process Data
by: Oyamada, Rafael Seidi, et al.
Published: (2025)
by: Oyamada, Rafael Seidi, et al.
Published: (2025)
Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline
by: Zeng, Guancheng, et al.
Published: (2024)
by: Zeng, Guancheng, et al.
Published: (2024)
Performance Evaluation of Emotion Classification in Japanese Using RoBERTa and DeBERTa
by: Takenaka, Yoichi
Published: (2025)
by: Takenaka, Yoichi
Published: (2025)
STACK: Adversarial Attacks on LLM Safeguard Pipelines
by: McKenzie, Ian R., et al.
Published: (2025)
by: McKenzie, Ian R., et al.
Published: (2025)
ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
by: Liu, Zexi, et al.
Published: (2025)
by: Liu, Zexi, et al.
Published: (2025)
English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
by: Dhaliwal, Mehak, et al.
Published: (2026)
by: Dhaliwal, Mehak, et al.
Published: (2026)
KLong: Training LLM Agent for Extremely Long-horizon Tasks
by: Liu, Yue, et al.
Published: (2026)
by: Liu, Yue, et al.
Published: (2026)
TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action
by: Zhou, Chenyue, et al.
Published: (2025)
by: Zhou, Chenyue, et al.
Published: (2025)
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
by: Zhou, Hang, et al.
Published: (2024)
by: Zhou, Hang, et al.
Published: (2024)
Training Proactive and Personalized LLM Agents
by: Sun, Weiwei, et al.
Published: (2025)
by: Sun, Weiwei, et al.
Published: (2025)
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
by: You, Haoran, et al.
Published: (2024)
by: You, Haoran, et al.
Published: (2024)
Post-training an LLM for RAG? Train on Self-Generated Demonstrations
by: Finlayson, Matthew, et al.
Published: (2025)
by: Finlayson, Matthew, et al.
Published: (2025)
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
by: Huang, Wei, et al.
Published: (2024)
by: Huang, Wei, et al.
Published: (2024)
LLM-Human Pipeline for Cultural Context Grounding of Conversations
by: Pujari, Rajkumar, et al.
Published: (2024)
by: Pujari, Rajkumar, et al.
Published: (2024)
From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning
by: Wang, Shaojie, et al.
Published: (2026)
by: Wang, Shaojie, et al.
Published: (2026)
DEPO: Dual-Efficiency Preference Optimization for LLM Agents
by: Chen, Sirui, et al.
Published: (2025)
by: Chen, Sirui, et al.
Published: (2025)
MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
by: Gao, Yicheng, et al.
Published: (2026)
by: Gao, Yicheng, et al.
Published: (2026)
Similar Items
-
Effective Harness Engineering for Algorithm Discovery with Coding Agents
by: Ishibashi, Yoichi, et al.
Published: (2026) -
Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
by: Ishibashi, Yoichi, et al.
Published: (2025) -
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
by: Ishibashi, Yoichi, et al.
Published: (2024) -
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
by: Ishibashi, Yoichi, et al.
Published: (2024) -
An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability
by: Yamauchi, Yusuke, et al.
Published: (2025)