model evaluation & behavior

This work addresses the limitation of existing safety-critical systems, which typically evaluate only predictive accuracy while lacking rigorous validation of the overall calibration of predicted probability distributions. To bridge this gap, the authors propose a modular calibration testing framework that decouples the calibration process into four interchangeable components: data model, scoring rule, hypothesis formulation, and statistical test procedure. Built upon formal statistical hypothesis testing, the framework provides a single accept/reject decision for the entire predictive distribution. Crucially, it rejects only overly confident predictions while tolerating reasonable deviations, thereby balancing practicality with flexibility. Empirical evaluations on weather forecasting and robotic pose estimation tasks demonstrate that the framework effectively supports reliable deployment in safety-critical applications.

In safety-critical applications, evaluating uncertainty calibration of regression models is hindered by inconsistent metric definitions, conflicting assumptions, and incomparable scales—impeding interpretability and reproducibility. This work systematically surveys and categorizes existing calibration metrics, then conducts a model-agnostic benchmark across real-world, synthetic, and manually miscalibrated datasets. We empirically demonstrate—for the first time—that most metrics yield contradictory or even opposing conclusions for identical calibration states, confirming that metric choice critically influences research outcomes. To address this, we propose ENCE (Expected Normalized Calibration Error) and CWC (Weighted Coverage Confidence) as more robust and stable primary metrics. Experiments across diverse scenarios show that ENCE and CWC exhibit superior consistency, strong resilience to noise and distribution shifts, and enhanced interpretability. Our findings establish a reproducible methodological foundation for uncertainty calibration evaluation in regression.

This paper addresses the reliability of calibration evaluation for machine learning models, identifying systematic biases in the widely used Expected Calibration Error (ECE) under distributional shift and varying binning strategies. Methodologically, it clarifies the logical hierarchy among multi-level calibration definitions, and systematically exposes ECE’s limitations through visualization, binning-based statistical analysis, and theoretical derivation—demonstrating its failure to satisfy key requirements of robustness and consistency in calibration assessment. Building on this critique, the paper introduces and explicates emerging calibration paradigms—including distribution-level and instance-level calibration—alongside their corresponding evaluation methodologies, thereby constructing a rigorous, interpretable, and practice-oriented calibration knowledge framework. The results equip researchers with principled guidance for selecting appropriate evaluation metrics and advance calibration assessment from ad hoc, heuristic practices toward standardization and formalization.

Current evaluation practices for supervised learning models are often misleading due to an overreliance on single aggregate metrics, which neglect the alignment among data characteristics, task objectives, and real-world application contexts. This work reframes model evaluation as a context-dependent, decision-oriented process and systematically investigates—through controlled experiments—the impact of dataset properties, validation strategies, class imbalance, and asymmetric error costs on evaluation outcomes. Leveraging diverse benchmark datasets, multiple validation protocols, and multidimensional performance measures, the study uncovers common pitfalls such as the accuracy paradox, data leakage, and metric misuse. It proposes a structured evaluation framework explicitly aligned with operational goals, offering principled guidance for developing more robust, reliable, and trustworthy supervised learning systems.

This paper addresses the problem that conventional calibration evaluation of deep learning models is vulnerable to spurious recalibration—i.e., trivial post-hoc adjustments that improve calibration metrics without enhancing generalization. To tackle this, we propose a novel joint evaluation paradigm integrating calibration and generalization. First, we derive a Bregman-divergence-based decomposition of calibration error, establishing the first theoretical connection between calibration metrics and generalization objectives (e.g., negative log-likelihood). Second, we design a new reliability diagram that jointly visualizes calibration bias and estimated generalization error. Third, we characterize multiple “pseudo-optimal” calibration phenomena and provide theoretically grounded, detectable criteria for identifying trivial recalibration. Experiments on standard benchmarks demonstrate that our approach significantly improves model diagnostic capability, yielding a more reliable and interpretable evaluation framework for calibration research.

This study addresses the lack of systematic evaluation of existing test selection metrics under multi-objective settings, distribution shifts, and multimodal data—challenges that hinder practical metric selection. To bridge this gap, the authors construct the first unified benchmark encompassing three testing objectives (fault detection, performance estimation, and retraining guidance), five types of distribution shifts, three data modalities (images, text, and Android packages), and 13 deep learning models. Through a large-scale empirical study involving 1,640 experimental scenarios, they conduct rigorous statistical analyses to comprehensively compare the performance of 15 widely used metrics, elucidate their respective applicability boundaries, and provide reliable guidance and actionable recommendations for test selection in safety-critical systems.

This work addresses the misalignment between offline evaluation metrics and online performance objectives in industrial applications by establishing a unified theoretical framework that systematically quantifies the relationships among diverse evaluation metrics for the first time. By introducing the concepts of Bayes-optimal sets and regret transfer mechanisms, the study reveals structural asymmetries among metrics and provides a principled classification and relational modeling of metrics with varying mathematical forms. Theoretically characterizing metric consistency and transferability, this research offers novel insights and a methodological foundation for designing offline evaluation systems that are aligned with online objectives and backed by rigorous theoretical guarantees.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

This paper addresses the challenge of quantifying business impact from predictive model improvements in insurance pricing. We establish, for the first time, an analytical relationship between model performance and loss ratio. A novel metric—Loss Ratio Error (LRE)—is introduced, linking prediction accuracy to actual financial loss via Pearson correlation, thereby enabling quantitative mapping from model-level metrics (e.g., RMSE) to business-level KPIs. We develop a unified analytical framework integrating frequency, severity, and pure premium models, combining closed-form derivations with Monte Carlo simulation to achieve high-accuracy loss ratio estimation under realistic assumptions; model performance degrades gracefully under assumption shifts, ensuring decision robustness. Our key contribution is the formal identification of diminishing marginal returns in model optimization: incremental accuracy gains yield progressively smaller reductions in loss ratio. This insight shifts pricing model evaluation from heuristic judgment toward cost-benefit-driven, quantitative decision-making.

Fixed-size benchmarking in model evaluation often fails to balance efficiency, statistical reliability, and diverse objectives, leading to either excessive resource consumption or unreliable results. This work proposes the first adaptive framework that integrates sequential testing into AI model evaluation, dynamically allocating evaluation data based on stopping criteria tailored for model ranking and selection tasks. By combining sequential hypothesis testing, minimum detectable effect analysis, and diminishing returns detection, the method achieves substantial gains in efficiency without compromising rigor. Empirical validation on the Open VLM Leaderboard demonstrates an 80% reduction in computational cost while maintaining a confidence interval width of 2.5 points, significantly enhancing both the practicality and scalability of model evaluation.