The paper introduces a new consistency criterion for auditing decision systems that combines ensemble margin with local prediction variability to address predictive multiplicity, or the Rashomon effect. It shows that finite ensembles converge to the expected model’s consistency score as ensemble size and sample count grow, and demonstrates that ensembling models from the Rashomon set reduces unchecked incorrect predictions while keeping diversions moderate. Experiments on transformer and fine‑tuned language models for NLP and tabular classification confirm the method’s effectiveness and stronger alignment with existing multiplicity metrics.
By Sinjini Banerjee, Tim Marrinan, Anand D. Sarwate
arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.
By Fengqiang Wan, Qing-Yuan Jiang, Fu Shen, Yang Yang
arXiv:2605.24818v3 Announce Type: replace-cross
Abstract: The literature on test set contamination largely focuses on detection, but the correction of contaminated test scores is underexplored. Our c...
By Johnny Tian-Zheng Wei, Jerry Li, Ameya Godbole, Robin Jia
arXiv:2605. 24818v2 Announce Type: replace-cross Abstract: The literature on test set contamination largely focuses on detection, but the correction of contaminated test scores is underexplored.
By Johnny Tian-Zheng Wei, Jerry Li, Ameya Godbole, Robin Jia
The paper audits the confidence outputs of seven feed‑forward 3D reconstruction backbones across 13 datasets, evaluating four properties: error ranking, average error‑to‑uncertainty ratio, slope of this ratio, and coverage of the implied error distribution. While confidence ranks errors well, the decoded uncertainty is consistently too small—off by at least 2.4× on median cases—and worsens with higher confidence. A post‑hoc power‑law fit per backbone‑dataset pair improves all four metrics at the dataset level, reducing the median error by 1.35×, but fails to correct coverage for many held‑out scenes, indicating the models lack the correct error scale and distribution shape.
By Nanxing Nick Deng, Qing Cheng, Niclas Zeller, Daniel Cremers
The paper introduces Calibration-Aware Uncertainty Cascades (CAUC), a post‑hoc framework that calibrates each model’s confidence independently and uses these calibrated scores to decide when to accept an early prediction, invoke a stronger model, or combine outputs. CAUC establishes a common reliability scale across heterogeneous models, decoupling deployment policies from specific model pools or budgets. Experiments on six language benchmarks show a 1.9% relative accuracy gain over strong‑model‑only inference while cutting strong‑model calls by about 47%, and on image classification it maintains or improves performance while reducing GFLOPs by up to 57%.
By Yilin Zhang, Han Jiang, Cai Xu, Ying Liu, Wei Zhao
arXiv:2608. 05670v1 Announce Type: new Abstract: A model's agreement across perturbed inputs is used both as a label-free reliability signal and as a self-training target, on the premise that agreement tracks correctness.
By Rasul Khanbayov, Hasan Kurban
arXiv:2601. 07965v2 Announce Type: replace Abstract: When a model knows when it does not know, many possibilities emerge.
By Chenjie Hao, Weyl Lu, Yuko Ishiwaka, Zengyi Li, Weier Wan, Yubei Chen
The paper evaluates the robustness of automated fact‑checking systems by cross‑benchmarking nine models—including random baselines, fine‑tuned transformers, zero‑shot LLMs, and top AVeriTeC 2025 systems—across four datasets from scientific, open‑web, and climate domains. It finds that fine‑tuned models outperform zero‑shot LLMs on ClimateCheck, that system rankings vary strongly with domain and metric, and that replacing retrieved evidence with gold annotations boosts veracity accuracy by 14–22 points, underscoring retrieval as the main bottleneck. The authors provide code, pre‑processed datasets, and results to enable reproducible research.
By Aida Usmanova, Zangir Iklassov, Markus Leippold, Ricardo Usbeck
arXiv:2608. 13190v1 Announce Type: new Abstract: Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable.
By Qianqian Wang, Yunshan Li, Dawei Huang, Wenwu Gong, Lili Yang
arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.
By Shihan Feng, Cheng Zhang, Michael Xi, Ethan Hsu, Lesia Semenova, Chudi Zhong
arXiv:2608. 10145v1 Announce Type: new Abstract: LeWorldModel trains a latent world model with a prediction loss and a single anti-collapse regulariser, and reports approximately 87% of goals reached on TwoRoom, its simplest diagnostic environment.
By Joyjeet Singh