When Stability Fails: Hidden Failure Modes Of LLMS in Data-Constrained Scientific Decision-Making

• URL: http://arxiv.org/abs/2603.15840v1
• Date: Mon, 16 Mar 2026 19:17:09 GMT
• Title: When Stability Fails: Hidden Failure Modes Of LLMS in Data-Constrained Scientific Decision-Making
• Abstract summary: Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific work- flows.<n>We introduce a controlled behavioral evaluation framework that explicitly seprates four dimensions of LLM decision-making.<n>We show that LLMs can exhibit near-perfect run-to-run stability while diverging from statistical ground truth.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly used as decision-support 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 introduce a controlled behavioral evaluation framework that explicitly sep- arates four dimensions of LLM decision-making: stability, correctness, prompt sensitivity, and output validity under fixed statistical inputs. We evaluate multi- ple LLMs using a statistical gene prioritization task derived from differential ex- pression analysis across prompt regimes involving strict and relaxed significance thresholds, borderline ranking scenarios, and minor wording variations. Our ex- periments show that LLMs can exhibit near-perfect run-to-run stability while sys- tematically diverging from statistical ground truth, over-selecting under relaxed thresholds, responding sharply to minor prompt wording changes, or producing syntactically plausible gene identifiers absent from the input table. Although sta- bility reflects robustness across repeated runs, it does not guarantee agreement with statistical ground truth in structured scientific decision tasks. These findings highlight the importance of explicit ground-truth validation and output validity checks when deploying LLMs in automated or semi-automated scientific work- flows.

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