arXiv AI

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.

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

arXiv AI

Aug 7

GAUGE: Granularity-Adaptive Counterfactual Gating of Evidence for Incomplete Multimodal Classification

arXiv:2608. 05608v1 Announce Type: cross Abstract: Multimodal classification typically assumes all modalities are available, yet real-world inputs are often incomplete.

By Yunping Shi, En Yu, Kairui Guo, Jie Lu

multimodal benchmarks

arXiv Machine Learning

Sep 15

Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

arXiv:2609.13514v1 Announce Type: new Abstract: Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when grou...

By Nikki Grens, Lu\'is F. Sim\~oes, Kai Hou Yip, Theresa Lueftinger

safety

arXiv Machine Learning

Jul 14

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

arXiv:2607. 10491v1 Announce Type: new Abstract: Retrieval-augmented generation grounds large language models in external evidence, but most pipelines still treat retrieved passages as deterministic and mutually consistent context.

By S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha

llms rag nlp safety

arXiv Machine Learning

Jun 8

Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring

arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).

By John Ayotunde, Qinghua Xu, Guancheng Wang, Lionel C. Briand

benchmarks safety

arXiv Machine Learning

Jul 24

Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.

By Mohammad Raahemi, Ali Sekhavati, Alireza Maleki, Hamid Nasiri

multimodal benchmarks safety data-engineering

arXiv AI

Jul 7

Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion

arXiv:2607. 02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems.

By Zhizhong Fu, Wei Zhou, Zhaoyang Jiang, Yulong Lin, Yifu Hou, Xiaorong Ding, Qiang Yan, Yifan Chen

safety

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