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.
By Huiyu Li, Weibo Liu, Xinru Xu, Dongchen Gao, Meng Zhang, Junhua Hu
arXiv:2605. 22259v2 Announce Type: replace Abstract: Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats.
By Jan Nausner, Michael Hubner
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
By Alireza Moayedikia
arXiv:2609.08271v1 Announce Type: new
Abstract: In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for repre...
By Zhenghua Pan
arXiv:2606. 28062v1 Announce Type: cross Abstract: Data fusion, also known as truth discovery, is a data integration problem that aims to determine the correct value or set of values for each attribute of an object when presented with potentially conflicting values from multiple sources.
By Hira Beril Kucuk, Norman W Paton, Jiaoyan Chen, Zhenyu Wu
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
The paper introduces a decision‑theoretic framework that splits a large language model’s decision loss into belief formation and action selection components. Using a synthetic benchmark, it evaluates how reinforcement‑learning interventions on beliefs, decisions, or both affect these components across three domains. The study finds that targeting a single component improves that part but may not transfer to others, while jointly targeting both improves both only when training and evaluation formats match.
By Huaman Sun, Dingcheng Wang, Jason Hartline, Jessica Hullman
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
Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit decision costs. We propose a decision-theoretic f...
arXiv:2601.10485v5 Announce Type: replace
Abstract: Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing t...
By Runhao Zhao, Weixin Zeng, Wentao Zhang, Chong Chen, Zhengpin Li, Xiang Zhao, Lei Chen
arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.
By Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius, Lasse Strothe, Andrii Lata, Jana Diesner
The paper introduces a probabilistic‑circuit framework for fusing opinions from multiple black‑box experts in noisy, conflict‑prone environments. It dynamically assigns context‑specific credibility to each expert, allowing reliable aggregation without needing access to their internal models or retraining. Experiments on multiple‑choice question answering with large language models show that this method outperforms individual models and static ensemble baselines, consistently improving predictive accuracy and decision reliability under disagreement.
By Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur, Vijayalakshmi Saravanan, Erik Blasch, Kristian Kersting, Sriraam Natarajan