A Systematic Approach for Large Language Models Debugging

arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debugging that treats models as observable systems, providing structured, model-agnostic methods from issue detection to model refinement. By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses, refine prompts and model parameters, and adapt data for fine-tuning or assessment, while remaining effective in contexts where standardized benchmarks and evaluation criteria are lacking. We argue that such a structured methodology not only accelerates troubleshooting but also fosters reproducibility, transparency, and scalability in the deployment of LLM-based systems.

Related

• Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition
• TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs
• Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations
• ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying

Source: arXiv cs.AI | 2026-04-28