Author

Sara Rezaeimanesh

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#machine learning Preprint Oct 2026

INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples

INCONVENIENT is introduced, a benchmark designed to study robustness to convenience-sample bias and provides explicit control over both the direction and severity of relationship shifts, enabling a systematic assessment of robustness to convenience sampling.

Sara Rezaeimanesh, Chad Klochko, Kundan Thind et al. · 0 citations

Preprint Aug 2026

Ordinal Gates, Cardinal Bets: Matching LLM Confidence to the Financial Decision Operator

LLM confidence scores are not independently deployable objects: their decision value depends on the downstream operator and exposure controller that consume them. Monotone recalibration cannot change a coverage-matched rank-based gate, whereas position sizing consumes score magnitude, so changing a confidence map can i...

Rayan Singh, Sara Rezaeimanesh · 0 citations