Verbal Process Supervision Elicits Better Coding Agents
Authors: name: Hao-Yuan Chen, name: Cheng-Pong Huang, name: Jui-Ming Yao

The emergence of large language models and their applications as AI agents
have significantly advanced state-of-the-art code generation benchmarks,
transforming modern software engineering tasks. However, even with test-time
computed reasoning models, these systems still struggle with complex software
engineering challenges. This work introduces CURA, a code understanding and
reasoning agent system enhanced with verbal process supervision (VPS),
achieving a 3.65\% improvement over baseline models on challenging benchmarks
like BigCodeBench. Furthermore, CURA, when paired with the o3-mini model and
VPS techniques, attains state-of-the-art performance. This work represents a
step forward in integrating reasoning-driven architectures with LLM-based code
generation, enabling agentic reasoning for language models to solve complex
software engineering tasks.