arXiv:2605.27901v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring has been proposed as a promising safety mechanism for detecting misaligned behavior in large language models. However, its reliability remains largely unexplored beyond English and across diverse model families. We present the first large-scale evaluation of CoT monitorability across 13 diverse languages and seven frontier model families, comprising 16 models. Using adversarial-hint evaluations that require explicit intermediate computation, together with analysis of internal answer-token probabilities, we cons
The rapid deployment and increasing sophistication of large language models globally necessitate a deeper understanding of their safety mechanisms across diverse linguistic contexts.
The reliability of AI safety mechanisms like CoT monitoring directly impacts trust and deployment in critical applications, particularly in multilingual environments.
Confidence in generalized CoT monitoring for LLM safety is reduced, highlighting the need for more robust, typologically diverse validation approaches.
- · AI safety researchers focusing on multilingual robustness
- · Developers of custom, localized AI safety protocols
- · Organizations prioritizing AI explainability
- · LLM developers over-relying on English-centric safety evaluations
- · Users in non-English speaking regions impacted by misaligned models
- · Uncritical adopters of generic CoT monitoring solutions
Increased focus on developing language-specific AI safety benchmarks and verification methods.
Potential for new regulations or standards mandating multilingual safety assessments for AI products.
Differentiated market adoption of AI systems based on their proven safety and reliability in local languages, fragmenting global AI ecosystems.
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Read at arXiv cs.AI