arXiv:2605.11599v3 Announce Type: replace Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior. Studies of prompt variation can reveal such failures, but without audit they can mix genuine model errors with invalid perturbations, extraction artifacts, and unmatched search procedures. We propose an audit-constrained protocol for targeted reasoning evaluation. Prompt variants are generated from a finite component grammar, rendered deterministically, evaluated under a fixed query budget, and counted as mode
The rapid deployment and increasing reliance on large language models necessitate more rigorous and standardized evaluation methods to ensure their reliability and performance, especially as they become more integrated into critical applications.
This protocol addresses a critical vulnerability in current LLM evaluation — the susceptibility to prompt variations — by proposing an audit-constrained, systematic approach to testing LLM reasoning, which is crucial for safety and trustworthiness.
The explicit introduction of an 'audit-constrained protocol' provides a more robust and less ambiguous framework for assessing LLM reasoning, potentially leading to more reliable benchmarks and a clearer understanding of model capabilities and limitations.
- · AI researchers
- · LLM developers
- · Auditing firms
- · Enterprise AI adopters
- · Developers of unreliable LLMs
- · Traditional, ad-hoc prompt testing methods
Improved understanding of LLM reasoning failures and increased development of robust, less brittle models.
Greater trust in LLM capabilities for enterprise and critical applications, accelerating adoption in sensitive sectors.
Standardized evaluation could become a regulatory requirement for AI systems, influencing market access and product development cycles.
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Read at arXiv cs.LG