arXiv:2606.30219v1 Announce Type: cross Abstract: LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while the latent properties they are meant to represent remain difficult to verify. This paper combines a hybrid survey - a systematic search paired with narrative synthesis and separately tracked grey evidence - with a conceptual framework and a structured ten-model audit. The synthesis spans eight evidence streams: benchmark validity, dynamic evaluation, LLM-as-judge reliability, safety evaluation, ja
The rapid deployment and increasing capabilities of large language models are exposing critical gaps in current evaluation and safety methodologies, necessitating new frameworks.
Improved and verified evaluation of LLMs directly impacts their safe and reliable deployment across sensitive applications, influencing trust and adoption.
This paper provides a hybrid survey and conceptual framework to identify and address LLM evaluation-safety failures, moving towards more robust assessment methods.
- · AI safety researchers
- · Benchmarking organizations
- · LLM developers focused on reliability
- · Entities relying solely on current benchmark scores
- · AI products with unverified safety claims
Identification of critical flaws in existing LLM evaluation benchmarks and safety metrics.
Development and adoption of more sophisticated and robust evaluation frameworks for AI models.
Increased public and regulatory scrutiny on AI safety reporting, potentially leading to new industry standards or compliance requirements.
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