Conformal Calibration Transfer addresses the challenge of applying conformal prediction when labeled calibration data is only available in a source space, while predictions are needed in a target space linked via unlabeled paired observations. The proposed Transported Conformal Calibration (TCC) method transports source calibration into the target domain and then corrects residual mismatches using only unlabeled target inputs, with two variants: TCC‑KS, which conservatively adjusts calibration based on a label‑free uncertainty surrogate, and weighted‑TCC, which reweights transported calibration for efficiency when weights are stable. Finite‑sample target‑domain coverage guarantees are provided, and experiments on CIFAR‑100‑C, Tiny‑ImageNet‑C, and SEN12MS demonstrate reliable coverage transfer without labeled target data, along with label‑free diagnostics to signal when correction is required.
By Achref Doula
arXiv:2610.09541v1 Announce Type: cross
Abstract: Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee....
By Arjun Balaji
CS-WCP introduces confidence‑set weighted conformal prediction to provide robust prediction sets for large‑language‑model judges when deployment traffic shifts the prevalence of task or policy groups. By constructing simultaneous exact intervals for source and target group masses and taking the union over all compatible ratio vectors, CS‑WCP achieves high coverage (mean 0.973) with few failures across 336 constructed traffic shifts, outperforming standard source conformal prediction. The method offers an auditable coverage safeguard under uncertain mixture weights, focusing on conservative tail protection rather than tighter set sizes.
By Ibne Farabi Shihab, Fariya Afrin
arXiv:2608.08685v2 Announce Type: replace
Abstract: Reliable semi-dense matching is essential for modern geometric vision systems. Designed under a coarse-to-fine paradigm, it achieves an optimal bal...
By Khoa Hoang, Hoang-Tuan Nguyen, Huong Ninh, Hai Tran, Long Q. Tran
arXiv:2610.07883v1 Announce Type: new
Abstract: A correctly reconstructed distant point appears uncertain even when a frozen 3D model processes equivalent inputs because its output frame rotates frac...
By Sammam Mahdi, Fariha Binta Salim, Rakin Bin Rabbani, Aniqua Nusrat Zereen
arXiv:2608. 08826v1 Announce Type: new Abstract: Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may have to answer queries whose coordinate and inspection time are chosen only after the data are seen.
By Ibne Farabi Shihab, Adria Binte Habib
The paper investigates the nature of agreement among repeated samples of large language models (LLMs), showing that strong agreement can arise even for incorrect answers. It introduces a pluralistic agreement index, Gamma, which is decomposed into a mechanical component driven solely by per‑case answer preferences and a residual component that captures preference‑unexplained agreement. Experiments on GPT‑4.1 and several open‑weight models demonstrate that mechanical agreement dominates in many settings, while the residual varies with benchmark type and sampling protocol.
By Lizhuo Zhang, Mengmeng Tang, Chenfeng Long, Xiaoyong Tang, Xiang Luo
arXiv:2608. 12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it.
By Binshuang Li
arXiv:2608.23638v1 Announce Type: cross
Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration...
By Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
arXiv:2606. 13780v1 Announce Type: cross Abstract: Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it.
By Jack Y. Araz, Michael Spannowsky
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
arXiv:2607. 17531v1 Announce Type: cross Abstract: Test-time collaboration, including self-consistency, best-of-N selection, critic models, and verifier pipelines, is often credited with broadly improving LLM reasoning, yet its gains are uneven and sometimes negative.
By Jie Hu