arXiv:2608. 15095v1 Announce Type: new Abstract: AI systems deployed outside clean benchmark settings often rely on observations that are incomplete, unstable, costly, or degraded by monitoring failures.
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arXiv:2606. 01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures.
arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
The paper investigates how making responsible‑AI evaluations more efficient—through batching, quantization, and benchmark reduction—affects the stability of conclusions drawn about model behavior. By testing three dense and mixture‑of‑experts models on the BBQ and BBQ‑V datasets under seven different conditions, the authors compare accuracy, bias, reasoning quality, subgroup performance, subset‑membership stability, runtime, and GPU energy consumption against a full‑benchmark BF16 baseline. Findings show that larger batching preserves accuracy and reduces energy in most settings, INT8 largely maintains quality but can increase energy use, INT4 introduces larger, context‑dependent changes, and reduced benchmarks save resources but are highly sensitive to which items are retained, underscoring that efficient evaluation must be validated against the benchmark’s intended conclusions.
arXiv:2607. 07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly.
arXiv:2606. 29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction.
The paper studies Online Kernel Supervised Principal Component Analysis (OKSPCA), which uses random features and an Adam-style orthonormal basis update to optimize a supervised spectral objective. It shows that accurate optimization of this objective does not guarantee accurate population subspace recovery or improved predictive performance, and it provides theoretical results on consistency, concentration, and perturbation of the estimator. Empirical experiments on six benchmarks reveal that replacing the tracker with the exact empirical target does not significantly change regression deficits, while classification-rank models capture most of the terminal objective energy but can exhibit substantial geometric deviation; sample-size studies further separate empirical accuracy from population recovery. The diagnostics also compare computational trade-offs, indicating that exact on-request computation can be faster in classification settings, whereas Adam saves time relative to full thin‑SVD in some dense regression requests, despite persistent geometric error.