evidential multi-view fusion

This paper addresses the modeling challenge of “unconceivable uncertainty”—events unforeseeable yet possible—arising in social and life sciences, where classical probability theory fails due to its inherent reliance on known, enumerable possibilities. We propose an extended evidence-theoretic framework that formally distinguishes between uncertainties within and beyond the agent’s cognitive boundary, integrating imprecise probabilities, subadditive measures, and non-standard information-theoretic approaches. Crucially, we establish a novel interface between this framework and multi-agent systems, rigorously differentiating representable from unrepresentable uncertainty sources. The resulting formalism provides a new mathematical foundation and analytical paradigm for studying complex socio-biological systems, particularly in risk perception and cultural information diffusion. (128 words)

This work addresses the limitations of the classical Dempster combination rule, which is constrained by its reliance on intersection-based semantics and struggles with complex evidence sources and diverse fusion scenarios. The authors propose an invertible transformation grounded in the principle of equal plausibility, mapping belief functions onto a possibility structure defined over the power set. By integrating a belief evolution network to model subset relationships, they develop an adaptive evidence fusion framework centered on families of t-norms. This approach transcends the semantic constraints of Dempster’s rule and provides a unified mechanism for fusing non-independent sources, managing conflict, enabling parameterized combination design, and integrating heterogeneous information. Consequently, it significantly enhances the flexibility and applicability of evidence theory in complex and high-conflict environments.

This work addresses the limitations of existing evidence fusion methods, which struggle to simultaneously capture inter-evidence conflict and intra-evidence uncertainty while neglecting the long-term reliability of evidence sources. To overcome these issues, the authors propose a unified evidential reasoning framework. It introduces a chaos-conflict joint measure satisfying five axioms to coherently quantify both conflict and nonspecificity. Furthermore, it incorporates a context-aware reliability assessment mechanism derived from historical fusion outcomes, leveraging spectral clustering and regret theory. This reliability estimate drives an adaptive combination rule and a belief-interval-based decision strategy. Evaluated on 16 real-world datasets, the method achieves an F1 score of 85.78% and an AUC of 93.30%, significantly outperforming eight Dempster–Shafer theory baselines and three gradient boosting approaches. Ablation studies confirm the contribution of each component.

This paper identifies and explains the “cognitive uncertainty collapse” phenomenon—where larger deep learning models exhibit degraded uncertainty quantification despite increased capacity—challenging the prevailing assumption that scale inherently improves uncertainty estimation. Method: The authors first systematically establish implicit ensembling as the primary cause of this collapse; they then propose an explicit multi-layer ensembling framework coupled with submodel decomposition to restore predictive diversity in large vision models (e.g., ViT), thereby recovering calibrated uncertainty estimation. Their approach integrates ViT interpretability analysis, theoretical modeling, and cross-architecture empirical validation (MLP, ResNet, ViT). Contribution/Results: The collapse is consistently reproduced across architectures; the proposed method significantly improves out-of-distribution detection and uncertainty calibration in safety-critical applications, demonstrating robust generalization and advancing principled uncertainty-aware scaling of vision models.

Integrating hypothesis testing results across heterogeneous multi-source studies—some reporting only binary significance decisions, others only FDR control levels—poses a fundamental challenge for rigorous, unified FDR control. Method: We propose the Integrated Ranking and Thresholding (IRT) framework, which operates solely on binary rejection decisions, a prespecified global FDR level, and the set of hypotheses—requiring neither raw data, p-values, nor effect sizes. IRT employs nonparametric evidence aggregation and a ranking-driven thresholding mechanism, circumventing traditional meta-analysis assumptions of statistical homogeneity and reliance on shared summary statistics. Contribution/Results: IRT is the first method to achieve theoretically guaranteed strong FDR control under non-shared statistical summaries. We prove its FDR control property rigorously; simulations demonstrate superior performance over state-of-the-art integration methods; and real-world application to multi-center genome-wide association studies confirms its practical utility and robustness.

This study addresses the critical challenge of effectively constructing belief functions to characterize uncertainty in data-scarce scenarios where accurate estimation of probability distributions is difficult. It presents a systematic review and, for the first time, a comprehensive integration of research at the intersection of statistical inference and Dempster–Shafer belief function theory. By tracing the evolution from classical to modern representative approaches, the work clarifies the theoretical foundations and applicability boundaries of various methods. Furthermore, it synthesizes multiple effective strategies for learning belief measures from limited data, thereby offering a coherent methodological framework and theoretical reference for uncertainty modeling and reasoning under data scarcity.

This study addresses the critical challenge of balancing provable validity and fusion efficiency when integrating multi-source information within inferential models (IMs). Focusing on possibility-measure-based IMs, the work proposes a general validity-preserving fusion framework applicable across diverse dependence structures—including independence, arbitrary dependence, and exchangeability. By employing a rank-and-calibrate construction, the framework achieves robust fusion while rigorously maintaining the theoretical validity guarantees inherent to IMs. The research establishes, for the first time, a universal mechanism for preserving validity under fusion, exposes the inefficiency of conventional fusion operators in the IM context, and offers superior alternatives that significantly enhance fusion efficiency without compromising statistical rigor.

This work addresses a key limitation in existing multi-agent belief fusion approaches, which typically assume a fixed cognitive partition structure and thus struggle to accommodate dynamic adjustments of the representation space caused by runtime changes in observational capabilities. The paper proposes a formal framework that enables dynamic evolution of cognitive partitions under continuous belief profiles, achieving for the first time interpretable belief reconciliation: refinement preserves admissibility, while coarsening ensures consistency through a unique mass-conserving repair mechanism, accompanied by complete explanations. The framework integrates the declarative constraint reasoning of Answer Set Programming (ASP) with Python’s numerical computation capabilities, leveraging ASP to manage structural changes and generate explanations. Empirical evaluation across 100 randomly generated topological change scenarios demonstrates 100% detection of constraint violations and full explanation coverage, confirming the method’s effectiveness and completeness.

This work addresses the challenge in multi-view classification that independently estimated view-specific uncertainties are incomparable due to the lack of cross-view consistency, causing fused predictions to be dominated by scale discrepancies among branches. To resolve this, the paper proposes a Trustworthy Multi-view Unified Routing framework (TMUR), which decouples view-specific evidence extraction from fusion arbitration and introduces a global context-aware unified router to generate sample-level expert weights for reliable uncertainty fusion. It is the first to reveal that independent evidence supervision fails to align evidence scales across views. The method further incorporates soft load balancing and diversity regularization to encourage expert specialization and balanced utilization. Theoretical analysis and experiments demonstrate that TMUR significantly outperforms local arbitration approaches in sample-dependent reliability scenarios, effectively enhancing both performance and trustworthiness in multi-view classification.

Existing methods struggle to consistently define and accurately evaluate the separation between aleatoric and epistemic uncertainty, often relying on imperfect proxy tasks due to the absence of ground-truth uncertainty targets. This work proposes a unified definition of uncertainty as the pointwise posterior risk—the expected loss of a predictor with respect to the true function distribution given observed data—thereby integrating Bayesian functional uncertainty with estimation bias. Building on this formulation, we introduce the first semi-synthetic benchmark that provides direct access to ground-truth uncertainty targets, eliminating dependence on proxy tasks. Experiments reveal that predictive accuracy does not necessarily correlate with uncertainty reliability, enabling clear identification of methods aligned with true uncertainty while exposing their sensitivity to data and modeling choices.