AI Research
Replication Without Persistence in Hosted LLMs: Measurement Sensitivity in Action-Time Belief Evaluation
Uncovering the instability of behavioral benchmarks in non-deterministic AI service environments
Behavioural evaluations of hosted language models can vary because the evaluated service, the measurement instrument, or both differ across runs. We separate three validation questions: whether a prior finding recurs on fresh data under its historical configuration (replication),...
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ArXiv CS.AI
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ArXiv CS.AI
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This research exposes the inherent instability of testing hosted LLMs, where non-deterministic updates and black-box environments undermine scientific rigor. Establishing standardized evaluation protocols is critical to ensuring that claimed breakthroughs in model reasoning are actually reproducible.
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