The paper proposes a measurement-geometry framework to quantify how well fixed measurement operators can distinguish between images generated by a prior, thereby guiding the design of more trustworthy…

This paper examines the faithfulness of natural language explanations for hidden activations in neural networks using a reconstruction-based test, and finds that the test is not faithful and can be ga…

This paper proposes CoHarden, a co-generation framework for automated program repair that uses a lax signal as an in-loop convergence criterion to prevent lax regressions.

This paper systematically explores the convex polygon reconstruction problem with specified sets of features, contributing new testing algorithms and hardness results.

This paper presents NL2Test, a tool that generates executable API regression tests from natural-language scenario descriptions and traffic captures, achieving an 82.4% exact-match rate in industrial s…

The paper establishes the first theoretical framework for analyzing the learnability of Test-Time Adaptation (TTA) under non-stationary data streams by introducing Recovery Complexity, which quantifie…

The paper introduces TopoTTA, a framework that uses persistent homology for topological data analysis to enhance the quality of anomaly segmentation in deep models, leading to an average 15% F1 improv…

This paper proposes a method to recover recoverability structure from failed traces of post-trained language models, enabling test-time routing and post-training analysis.

The paper introduces Test-Driven Forensics, an approach that treats forensic expectations as executable tests to detect and measure the degradation of repeatability and confidence in digital forensic…

The paper introduces the Image Reconstruction Game, a benchmark showing that the quality of the descriptive model is the primary determinant of image reconstruction success, while the generator's role…

The paper introduces the Reconstructive Authority Model (RAM), a novel framework that proves execution validity by assessing state coverage rather than just state integrity, showing that existing atte…

The paper introduces LL-Bench, a comprehensive benchmark for evaluating large-scale generative models on low-level vision tasks, and proposes LL-Score, an MLLM-based evaluator that better aligns quali…

The paper proposes RAMP, a multi-corruption augmentation framework, which significantly improves the robustness and reliability of CT segmentation deep learning models when deployed in real-world, deg…

The paper analyzes the security limits of verifiable decapsulation tests for Key Encapsulation Mechanisms (KEMs), establishing that the list-hit event is the primary black-box obstruction and deriving…

This paper introduces the CER framework to address the complex problem of reconstructing AI-mediated losses for insurance claims, moving beyond simple event reconstruction to analyze the system's oper…

The paper proposes MindDiffuser, a two-stage framework that significantly improves image reconstruction from brain activity by combining semantic guidance from text-to-image models with structural ref…