arXiv:2605.23965v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve strong performance on logical reasoning benchmarks, yet their reliability remains uncertain. Existing evaluations rely on static benchmarks, which fail to assess robustness under logically equivalent transformations and often overestimate reasoning capability. We propose LGMT (Logic-Grounded Metamorphic Testing), an oracle-free framework that leverages first-order logic (FOL) to evaluate LLM reasoning. By deriving metamorphic relations from formal logical equivalences, LGMT constructs semantically invariant
As LLMs are increasingly deployed in critical applications, the need for robust and reliable evaluation methods for their reasoning capabilities is becoming urgent.
This development introduces a more rigorous way to assess LLM reliability, moving beyond static benchmarks to evaluate robustness under logical transformations, which is crucial for trust and deployment in complex systems.
The standard of LLM evaluation is shifting from mere performance metrics to a deeper understanding of their logical consistency and robustness, potentially leading to more reliable and trustworthy AI systems.
- · AI developers focused on reliability
- · Enterprises deploying LLMs in critical decision-making
- · AI safety researchers
- · Logic and formal methods researchers
- · LLM providers with unreliable reasoning capabilities
- · Organizations relying solely on static, benchmark-driven evaluations
LGMT provides a new tool for developers to identify and mitigate logical reasoning flaws in LLMs more effectively.
Improved reliability and explainability derived from such testing could accelerate the adoption of LLMs in highly regulated industries like finance and healthcare.
The widespread use of logic-grounded testing could drive a paradigm shift in LLM design, prioritizing formal verification and logical consistency over purely statistical performance.
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Read at arXiv cs.LG