The paper introduces Multi-View Evidential (MVE) learning for evaluating trustworthiness of collaborators in distributed systems. It models each task owner’s interaction as an independent view, uses the Mamba model to capture temporal trust dynamics, and applies evidential deep learning to quantify uncertainty. A dynamic fusion strategy then combines view-specific evidence to produce a final trust assessment, outperforming baselines in accuracy and task success rate.
By Botao Zhu, Xianbin Wang
arXiv:2607. 18561v1 Announce Type: new Abstract: In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views.
By Yuliang Yang, Hongzhe Zhang, Huiru Wang
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:2606. 31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.
By Mohamad Mestoukirdi, Vincent Corlay
arXiv:2609.39848v1 Announce Type: new
Abstract: Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundatio...
By Mingyue Ma, Zongbo Han, Changqing Zhang, Guangyu Wang
arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.
By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys
Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation models may encode the same input from multiple...
arXiv:2607. 20529v1 Announce Type: cross Abstract: Large Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs.
By Jiawei Zheng, Jiazhen Zhang
Backbone-Adaptive Evidence Routing (BAER) is a method that dynamically selects an evidence gathering protocol—evidence stacking, reliability-based expert routing, or candidate-blind reference verification—based on the judge backbone and benchmark during development, then locks that choice for testing. BAER maintains candidate symmetry, ensuring that swapping responses reverses preference but not its strength. In experiments across four benchmarks and two 8B judge backbones, BAER outperforms all compared methods, achieving the highest test accuracy in all eight conditions and improving over the strongest baseline by 0.87–7.32 points.
By Zeyan Li, Jing Peng, Jianfeng Xu
The paper introduces CELM, a data‑free framework for federated learning that estimates class‑wise contribution by maximizing logits. It constructs a cross‑client evidence matrix to quantify each client’s competence and coverage for each class, then uses this matrix to compute weighted aggregation that upweights clients offering strong evidence for underrepresented classes. The method maintains stability through simplex constraints and momentum smoothing, and it is compatible with standard FL pipelines, showing improved robustness to class imbalance and heterogeneity on vision benchmarks.
By Asim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye, Karthik Nandakumar
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
By Yu Chang, Anzhe Cheng, Chenwei Wu, Zhuoran Wang, Jiahao Chen, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Paul M. Thompson, Liyue Shen, Paul Bogdan
arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.
By Ange-Cl\'ement Akazan, Ineza Remy Mugenga, Abebe Geletu, Jean Medard Ngnotchouye, Issa Karambal