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The study examines how renaming option labels in typed decision models affects model behavior. By swapping the names of two options (e.g., from 0/1 to no/yes) while keeping the underlying rubrics unchanged, the authors observed a dramatic shift in decision rankings—AUC dropped from .94 to .23 and answer flips increased by 70.4 per hundred. The effect is amplified with more options and depends on the semantic polarity of the labels, yet the models still maintain a zero type‑error rate.
arXiv:2602.13576v2 Announce Type: replace-cross Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langu...
arXiv:2608. 02665v1 Announce Type: cross Abstract: A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form.
JevOut demonstrates that natural, short additions to the context of decision models can flip their outputs from correct to incorrect, even when the correct answer remains unchanged. By optimizing context additions while keeping the source, question, choices, and gold answer fixed, the study found that 61.4% of initially correct decisions were redirected to a wrong option, with 45% receiving high confidence. Similar fragility was observed across three other decision systems on seven datasets, with flip rates between 64.9% and 73.2%.
arXiv:2609.37647v1 Announce Type: cross Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?