TRACE-C: Rank-Calibrated Relational Anomaly Detection for Multi-Stream Operational Telemetry
- URL: http://arxiv.org/abs/2608.21251v1
- Date: Fri, 21 Aug 2026 16:01:56 GMT
- Title: TRACE-C: Rank-Calibrated Relational Anomaly Detection for Multi-Stream Operational Telemetry
- Abstract summary: TRACE-C is an auditable strictly-prior rank-calibrated detector for aligned multi-stream telemetry.<n>We evaluate six Great Britain grid streams with a January-April 2019 fit, July-December 2019 development evidence, and a 2020 hold-out frozen before inspection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Operational telemetry can be jointly anomalous while every individual stream stays inside its familiar range. TRACE-C is an auditable strictly-prior rank-calibrated detector for aligned multi-stream telemetry: same-regime rolling median/MAD residuals feed three window channels -- a maximum normalized local sum, a Gaussian copula-form dependence contrast on robust-z residuals, and a worst standardized AR(1) innovation -- whose channel ranks are Fisher-aggregated and ranked against earlier aggregates. We evaluate six Great Britain grid streams with a January-April 2019 fit, July-December 2019 development evidence, and a 2020 hold-out frozen before inspection. TRACE-C ranks Storm Atiyah first among 2019 test windows, but a disclosed channel ablation attributes that rank to the local channel, not the copula-form channel: copula-only ranks Atiyah 59th. The short 9 August frequency event is ranked far lower by the fused detector (143) than by the temporal channel alone (40), and reconstruction baselines rank it first. In 2020 no window is selected, which is consistent with record-rule saturation rather than an uneventful year; the highest-ranked frozen window was later interpreted as Storm Ellen. Three interpretive limits carry throughout. The resulting p-values are selection quantities, not event probabilities. The copula-form channel is not a literal copula density: the method applies no probability-integral or normal-score transform. Empirical rank counts are diagnostics, not coverage or false-discovery proofs. Every table and figure in this paper is generated from committed machine-readable reports.
Related papers
- Predictive Likelihood Ratios for Language Model Watermark Detection [3.8200540264474907]
Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key.<n>We construct predictive likelihood ratios that average over uncertain probability deficits and residual-tail distributions.
arXiv Detail & Related papers (2026-09-14T14:39:29Z) - Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and a Tight Univariate Rate [47.36630149538994]
In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension.<n>Warmuth and Amid asked at COLT 2023 whether any of three such rules admits a competitive online regret guarantee.<n>Our analysis identifies a common obstruction: cheap nuisance causes the refit to underweight the truly predictive coordinate.
arXiv Detail & Related papers (2026-08-18T09:32:03Z) - Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training [51.18425726646089]
We propose textscSURE, a unified latent-space framework for image and video diffusion models.<n>It learns reward distributions and directly uses their reliability to guide dense post-training.<n>textscSURE-REFL achieves the highest VBench quality, semantic, and total scores among the evaluated methods.
arXiv Detail & Related papers (2026-08-06T14:55:42Z) - Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models [0.0]
ClosurePairs crosses compatible microstates with repeated disturbances.<n>It recovers source attribution at unchanged likelihood.<n>It reduces equal-budget decomposition error in a nonlinear interaction benchmark.
arXiv Detail & Related papers (2026-08-01T11:07:24Z) - Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
arXiv Detail & Related papers (2026-07-07T10:10:00Z) - A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmaps [0.0]
multimodal brain-encoding models predict fMRI responses to naturalistic video with high accuracy.<n>We run TRIBE on 48 YouTube videos and reduce its predicted cortical response to a per-second engagement curve.<n>Running the probe on TRIBE's input streams reveals at most a small, borderline visual-stream signal.<n>We release code, a video-ID manifest, and a heatmap-acquisition method robust to YouTube's SABR streaming.
arXiv Detail & Related papers (2026-07-01T19:03:17Z) - Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics [0.0]
Detectors for radio-frequency impairments are usually reported with a single AUC measured on the distribution they were tuned on.<n>We ask whether this optimism can be predicted before any out-of-distribution data is seen.
arXiv Detail & Related papers (2026-06-20T14:10:59Z) - Beyond ROC-AUC: Operating-Point Performance Reporting for Biometric Verification [0.0]
A biometric verifier is often deployed with a strict false match budget, so only a narrow, low false match rate (FMR) slice of the score range is used.<n>In practice, a single area under the receiver operating characteristic curve (ROC-AUC) is still reported as the resolution.<n>A system that looks stronger on full ROC-AUC is shown to be significantly worse at FMR = 10-3.
arXiv Detail & Related papers (2026-06-13T15:28:54Z) - Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
arXiv Detail & Related papers (2026-05-20T19:08:38Z) - Posterior Augmented Flow Matching [64.1559809786948]
Posterior-Augmented Flow Matching (PAFM) is a generalization of flow matching (FM)<n>We show that PAFM improves over FM by up to 3.4 FID50K across different model scales.
arXiv Detail & Related papers (2026-05-01T17:59:59Z) - TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression [109.69084997173196]
Deepscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood.
Recent works show that this may result in sub-optimal convergence due to the challenges associated with covariance estimation.
We study two questions: (1) Does the predicted covariance truly capture the randomness of the predicted mean?
Our results show that not only does TIC accurately learn the covariance, it additionally facilitates an improved convergence of the negative log-likelihood.
arXiv Detail & Related papers (2023-10-29T09:54:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.