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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

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 AI

1d ago

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

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

Related stories

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

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

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

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