ProcBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents
📰 ArXiv cs.AI
Learn to evaluate process-level defects in LLM coding agents using ProcBench, a novel benchmark for execution-process evaluation
Action Steps
- Build a ProcBench framework to organize recurrent execution defects into a reusable ontology
- Run ProcBench on existing LLM coding agents to evaluate their execution-process capabilities
- Configure ProcBench to cover 11 defect types in 4 categories
- Test LLM coding agents using ProcBench to identify process-level defects
- Apply ProcBench to improve the control preservation in LLM coding agents
Who Needs to Know This
ML researchers and engineers working on LLM coding agents can benefit from ProcBench to identify and mitigate defects in their models, improving overall performance and reliability
Key Insight
💡 ProcBench provides a systematic way to evaluate and mitigate defects in LLM coding agents, improving their reliability and performance
Share This
🚀 Introducing ProcBench: a novel benchmark for evaluating process-level defects in LLM coding agents 🤖
Key Takeaways
Learn to evaluate process-level defects in LLM coding agents using ProcBench, a novel benchmark for execution-process evaluation
Full Article
Title: ProcBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents
Abstract:
arXiv:2605.20251v2 Announce Type: cross Abstract: Existing benchmarks for LLM coding agents primarily evaluate final outcomes. While useful for measuring overall capability, these metrics provide limited visibility and often miss defects that arise during execution. We present ProcBench, a benchmark for execution-process evaluation in LLM coding agents. ProcBench organizes recurrent execution defects into a reusable ontology covering 11 defect types in 4 categories, and evaluates agent trajector
Abstract:
arXiv:2605.20251v2 Announce Type: cross Abstract: Existing benchmarks for LLM coding agents primarily evaluate final outcomes. While useful for measuring overall capability, these metrics provide limited visibility and often miss defects that arise during execution. We present ProcBench, a benchmark for execution-process evaluation in LLM coding agents. ProcBench organizes recurrent execution defects into a reusable ontology covering 11 defect types in 4 categories, and evaluates agent trajector
DeepCamp AI