arXiv AI August 14, 2026 By Huiyu Li, Weibo Liu, Xinru Xu, Dongchen Gao, Meng Zhang, Junhua Hu

Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting

arXiv:2608. 13108v1 Announce Type: new Abstract: Multi-source evidence fusion under Dempster-Shafer theory faces two persistent challenges: existing conflict measures assess inter-evidence inconsistency and intra-evidence uncertainty independently, yielding incomplete evaluations, and current fusion methods evaluate evidence sources exclusively through instantaneous comparisns without exploiting their long-term reliability across diverse decision contexts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Sep 14

Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification

The paper introduces Trusted Multi-view learning with Unified Routing (TMUR), a method that separates view-specific evidence extraction from fusion arbitration in multi-view classification. TMUR employs view-private experts, a collaborative expert, and a unified router that assigns sample-level weights based on global context, along with soft load-balancing and diversity regularization to promote balanced and discriminative expert use. Experiments on 14 datasets show that TMUR consistently improves classification accuracy and reliability compared to 15 recent baselines.

By Yilin Zhang, Cai Xu, Haishun Chen, Ziyu Guan, Wei Zhao

safety

arXiv AI

Jul 31

Decision-oriented joint optimization of evidence fusion based on event-conditioned credibility

arXiv:2504. 04128v3 Announce Type: replace Abstract: In decision-level fusion tasks involving heterogeneous sources with unequal precision and potential anomalies, evidence deviating from the majority may be either critical evidence supporting the correct decision or anomalous evidence supporting an incorrect event.

By Chaoxiong Ma, Yan Liang, Huixia Zhang, Hao Sun

arXiv Machine Learning

Sep 22

Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning

arXiv:2509.00754v3 Announce Type: replace Abstract: Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends o...

By Qiying Hu, and Rui Sun, Yingying Liang, Qianli Zhou, Witold Pedrycz

benchmarks

arXiv AI

Aug 18

Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

arXiv:2608. 14617v1 Announce Type: cross Abstract: A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Dempster-Shafer combination, and conformal prediction) into one pipeline.

By Surya Saka

llms

arXiv Machine Learning

Jun 29

Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits

arXiv:2603. 26629v2 Announce Type: replace Abstract: Multimodal fusion requires integrating information from multiple sources that may conflict depending on context.

By Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan

multimodal benchmarks safety

arXiv Machine Learning

Jul 22

Signed Evidence Flow: Conflict-Aware and Stability-Calibrated Data Analysis

arXiv:2606. 21875v2 Announce Type: replace-cross Abstract: Modern data analysis usually gives a prediction without showing whether the evidence behind it is clear, conflicting, or stable.

By Jeffery Opoku, David Banahene

One email a morning, machine-written

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Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification

Decision-oriented joint optimization of evidence fusion based on event-conditioned credibility

Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning

Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion

Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits

Signed Evidence Flow: Conflict-Aware and Stability-Calibrated Data Analysis