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cs.SE2025★ 1 cited
Learning to Generate Unit Test via Adversarial Reinforcement Learning
Dongjun Lee, Changho Hwang, Kimin Lee
Unit testing is a core practice in programming, enabling systematic evaluation of programs produced by human developers or large language models (LLMs). Given the challenges in wri…
cs.AI2025
Automated Skill Discovery for Language Agents through Exploration and Iterative Feedback
Yongjin Yang, Sinjae Kang, Juyong Lee +3
Training large language model (LLM) agents to acquire necessary skills and perform diverse tasks within an environment is gaining interest as a means to enable open-endedness. Howe…
cs.AI2025
Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness
Yongjin Yang, Euiin Yi, Jongwoo Ko +3
The remarkable growth in large language model (LLM) capabilities has spurred exploration into multi-agent systems, with debate frameworks emerging as a promising avenue for enhance…