title: Verbal Process Supervision Elicits Better Coding Agents
summary: 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.
paper_authors._id: 67e21a81e2e69ea26eee4f67
paper_authors.name: Hao-Yuan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:30.277Z
paper_authors._id: 65bef46337491e7adc5ee7c9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bQ2WY0bU65IQ3IBn6SrnA.png
paper_authors.fullname: Hao-Yuan Chen
paper_authors.type: user
paper_authors.user: MarkChenX
paper_authors._id: 67e21a81e2e69ea26eee4f68
paper_authors.name: Cheng-Pong Huang
paper_authors._id: 67e21a81e2e69ea26eee4f69
paper_authors.name: Jui-Ming Yao
paper_title: Verbal Process Supervision Elicits Better Coding Agents
paper_summary: 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.
paper_ai_summary: CURA, an enhanced code understanding and reasoning agent with verbal process supervision, significantly improves code generation benchmarks, especially when integrated with the o3-mini model.
paper_ai_keywords: CURA code understanding reasoning agent verbal process supervision BigCodeBench o3-mini model reasoning-driven architectures LLM-based code generation