What is it about?
Large language models can often give convincing answers to complex problems, but a correct-looking answer does not always mean that the reasoning behind it is reliable. Models may make invalid deductions, overlook important information, or reach the right answer through a flawed reasoning process. We introduce SymDiag, a method for finding and explaining these reasoning errors. SymDiag converts a model's reasoning steps into a symbolic form that can be checked using formal logic. It examines the reasoning step by step to identify contradictions, unsupported conclusions, and missing information, and then provides concrete evidence showing where the reasoning went wrong. This allows us to move beyond simply asking whether an answer is correct and instead understand why the reasoning succeeds or fails.
Featured Image
Photo by Markus Spiske on Unsplash
Why is it important?
Most current approaches evaluate large language models mainly by their final answers or rely on another language model to judge their reasoning. These methods can miss an important problem: a model may produce the correct answer while using an invalid reasoning process. SymDiag provides a more rigorous and interpretable alternative by using symbolic verification to examine the reasoning itself. It can detect hidden reasoning errors, locate the problematic steps, and provide actionable feedback that can be used to repair subsequent reasoning attempts. Our results across mathematical, logical, scientific, and general reasoning tasks show that this approach improves the detection and correction of unreliable reasoning. This is an important step toward AI systems whose reasoning is not only accurate, but also verifiable, diagnosable, and trustworthy.
Perspectives
I believe that evaluating only the final answer is not enough for increasingly capable reasoning models. If we want to use large language models in situations where reliability matters, we also need to understand and verify the process by which they reach their conclusions. With SymDiag, we explored how symbolic reasoning tools can complement language models by turning otherwise opaque reasoning failures into concrete and interpretable diagnoses. I hope this work encourages more research on moving from simply measuring whether an AI system is correct to understanding where it goes wrong, why it goes wrong, and how it can be corrected.
Wenyao Cui
Beijing Institute of Technology
Read the Original
This page is a summary of: SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification, August 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3770855.3818004.
You can read the full text:
Contributors
The following have contributed to this page
