Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment
📰 ArXiv cs.AI
Learn to enhance architectural reasoning in code LLMs using scalable labeling with agentic judgment, improving software engineering beyond mere correctness
Action Steps
- Implement an agentic judging pipeline using a strong LLM as a proxy for expert architectural evaluation
- Configure the Architecture Complexity Judge (ACJ) to estimate codebase-specific architectural complexity
- Use the ACJ to label codebases and train an LLM for architectural reasoning
- Test the LLM's ability to reason about architectural trade-offs and design decisions
- Apply the agentic judgment pipeline to real-world software development projects to improve code quality
Who Needs to Know This
Software engineers and architects can benefit from this technique to improve the quality of their codebases, while AI researchers can explore new applications of LLMs in software development
Key Insight
💡 Agentic judgment can be used to improve the architectural understanding of code LLMs, enabling them to reason about design decisions and trade-offs
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Enhance architectural reasoning in code LLMs with scalable labeling & agentic judgment! #LLMs #SoftwareEngineering
Key Takeaways
Learn to enhance architectural reasoning in code LLMs using scalable labeling with agentic judgment, improving software engineering beyond mere correctness
Full Article
Title: Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment
Abstract:
arXiv:2606.14948v1 Announce Type: cross Abstract: LLMs have substantially improved software engineering yet real-world development requires architectural understanding. Such understanding is prohibitively expensive to label manually and impossible to verify through tests alone. We propose an agentic judging pipeline using a strong LLM as a scalable proxy for expert architectural evaluation, comprising two judges: the Architecture Complexity Judge (ACJ), which estimates codebase-specific architec
Abstract:
arXiv:2606.14948v1 Announce Type: cross Abstract: LLMs have substantially improved software engineering yet real-world development requires architectural understanding. Such understanding is prohibitively expensive to label manually and impossible to verify through tests alone. We propose an agentic judging pipeline using a strong LLM as a scalable proxy for expert architectural evaluation, comprising two judges: the Architecture Complexity Judge (ACJ), which estimates codebase-specific architec
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