Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing

• URL: http://arxiv.org/abs/2609.03973v1
• Date: Thu, 03 Sep 2026 15:09:52 GMT
• Title: Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing
• Abstract summary: An image editor may satisfy every plausibility constraint separately even when no single latent explanation fits the complete output.<n>We formalize this local-to-global failure using a common witness grade and witness nerve.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: An image editor may satisfy every regional plausibility constraint separately even when no single latent explanation fits the complete output. We formalize this local-to-global failure using a common witness grade and witness nerve. The framework separates auditing from causal identification: shared exogeneity alone allows every coupling of the regime marginals, whereas an externally justified witness relation yields sharp partial-identification bounds for prespecified image features. Helly-type arguments provide short incompatibility certificates for quasiconvex losses, heterogeneous action strata, and finite witness atlases; a blocker-hypergraph formula gives exact repair counts. Simultaneous confidence regions for the regime marginals give finite-sample outer coverage of the complete identified interval. Controlled MNIST, Morpho-MNIST, and smallNORB studies demonstrate the predicted local-global separation, while synthetic experiments test sharp bounds, certificate recovery, and structured computation. The method audits a declared feature relation and does not identify unrestricted pixel-level counterfactuals.

Related papers

• A distribution-free certification framework for trustworthy crash-severity prediction [1.089614199781423]
  We develop a certification layer that wraps any severity model unmodified.<n>On 5.2 million Texas records across seven base models spanning four decades, the layer attaches identical validity.<n>The framework is released as an open-source package with theorem-level tests.
  arXiv  Detail & Related papers  (2026-09-10T14:20:08Z)
• SEAM: Global consistency beyond local accuracy in scientific machine learning [3.1861308132183375]
  We introduce Scientific Explanation-Admissibility Machines (SEAM), a generator-agnostic framework that makes local-to-global consistency question computable across regions, sensors, regimes, and model components.<n>SEAM detects incompatible explanations even when local predictions are accurate, and attributes failures to specific channels and overlaps.<n>It adds a global explanation-consistency audit to existing solvers and learning models, testing whether their local explanations form a coherent scientific account.
  arXiv  Detail & Related papers  (2026-08-06T07:45:23Z)
• GAUGE: Granularity-Adaptive Counterfactual Gating of Evidence for Incomplete Multimodal Classification [17.595478415811687]
  Multimodal classification typically assumes all modalities are available, yet real-world inputs are often incomplete.<n>We propose GAUGE, a lightweight counterfactual gating framework for incomplete multimodal classification.<n>GAUGE scores the counterfactual effect of replacing every unit with a reference representation through prediction-aware Taylor evidence scores.
  arXiv  Detail & Related papers  (2026-08-06T05:07:14Z)
• Local verification cannot detect non-transportability: a cohomological theory of context preservation in agentic reasoning [0.0]
  Agentic AI systems routinely transport conclusions across biological, clinical and financial contexts.<n>We show that the emerging safeguard of local verification is structurally incomplete.<n>We quantify the distortion under unequal precision and supply the precision-whitened form that restores exactness.
  arXiv  Detail & Related papers  (2026-08-04T21:09:50Z)
• Self-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss [4.6304326752276985]
  This paper develops a four-layer theory of self-certification of representation adequacy.<n>The sequential layer poses certification as an optimal-stopping problem in the currency of task loss.<n>A final boundary layer identifies the open theorem needed to cover policy switching or representation repair.
  arXiv  Detail & Related papers  (2026-08-03T14:09:34Z)
• Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability [46.784084625532984]
  This paper explores unsupervised disentangled representation learning from a functional perspective.<n>We prove that this condition yields identifiability of general nonlinear generative models.
  arXiv  Detail & Related papers  (2026-06-19T12:47:07Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection [56.362776482614976]
  We study anomaly detection in a setting where constraints are given as logical rules over learned visual concepts.<n>We propose a neural rule evaluator that compiles each constraint into a directed acyclic graph and learns feature-aware subtree gates.<n>Across CLEVRER, OpenImages, and VidOR, the resulting evaluator improves rule-level anomaly AUROC over independent-events and same-image semantic-training baselines.
  arXiv  Detail & Related papers  (2026-05-25T02:52:36Z)
• Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection [56.362776482614976]
  Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection.<n>We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space.<n>We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette.
  arXiv  Detail & Related papers  (2026-05-23T06:27:42Z)
• Diverse Dictionary Learning [47.381311762862985]
  Given only observational data $X = g(Z)$, where both the latent variables $Z$ and the generating process $g$ are unknown, recovering $Z$ is ill-posed without additional assumptions.<n>We show that intersections, complements, and symmetric differences of latent variables linked to arbitrary observations, along with the latent-to-observed dependency structure, are still identifiable up to appropriate indeterminacies.
  arXiv  Detail & Related papers  (2026-04-19T18:18:34Z)
• Neural Uncertainty Principle: A Unified View of Adversarial Fragility and LLM Hallucination [60.197429875410286]
  Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems.<n>Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion.<n>In vision, masking highly coupled components improves robustness without costly adversarial training.<n>In language, the same prefill-stage probe detects hallucination risk before generating any answer tokens.
  arXiv  Detail & Related papers  (2026-03-20T02:07:10Z)
• Feasibility Preservation under Monotone Retrieval Truncation [0.12183405753834557]
  We study retrieval from a structural perspective, modeling query answering as a feasibility problem under truncation.<n>We show that monotone truncation suffices to guarantee finite witnessability for individual queries.<n>We further exhibit sharp counterexamples demonstrating failure under non-monotone truncation, non-finitely-generated query classes, and purely slotwise coverage.
  arXiv  Detail & Related papers  (2026-01-21T18:25:16Z)

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.