Systematic LLM Debugging Method Introduced

A new paper on arXiv (2604.23027) proposes a systematic approach for debugging large language models (LLMs). The method treats LLMs as observable systems, offering structured, model-agnostic techniques from issue detection to refinement. It unifies evaluation, interpretability, and error analysis, enabling iterative diagnosis of weaknesses, prompt and parameter tuning, and data adaptation for fine-tuning. The approach is effective even without standardized benchmarks or evaluation criteria, aiming to accelerate troubleshooting in diverse AI applications.