AI RESEARCH
When Stability Fails: Hidden Failure Modes Of LLMS in Data-Constrained Scientific Decision-Making
arXiv CS.AI
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ArXi:2603.15840v1 Announce Type: cross Large language models (LLMs) are increasingly used as decision- tools in data-constrained scientific workflows, where correctness and validity are critical. However, evaluation practices often emphasize stability or reproducibility across repeated runs. While these properties are desirable, stability alone does not guar- antee agreement with statistical ground truth when such references are available. We