Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach
Assessment outcome
total 1/4 (actionability 1 + authority 0); currency unscored, so pro-rated to 1.5/6
Authorship & scope
Multiple named authors are listed at the beginning of the text.
Score 0: Individual practitioner academic researchers authored this preprint paper, which does not constitute binding law or an official institutional standard.
Issuers, who it governs, legal force and content are on the full record.
Eligibility gates
HTTP 200, 11260 words of full text extracted
The source is entirely about mobile embodied AI networks and graph Resfusion optimization models for AI.
Discusses security, trust mechanisms, risk, and verification frameworks within blockchain-enabled networks.
Multiple named authors are listed at the beginning of the text.
There is no retraction or supersession notice on the page.
Scored criteria
Actionability
1/2Score 1: Principles or direction; the text proposes a framework and mechanisms without mandating a specific binding individual compliance step.
Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation.
Authority
0/2Score 0: Individual practitioner academic researchers authored this preprint paper, which does not constitute binding law or an official institutional standard.
Efficient and Trustworthy Block Propagation for Blockchain-Enabled Mobile Embodied AI Networks: A Graph Resfusion Approach Jiawen Kang, Jiana Liao, Runquan Gao, Jinbo Wen, Huawei Huang, Maomao Zhang*, Changyan Yi, Tao Zhang, Dusit Niyato, Fellow, IEEE, and Zibin Zheng, Fellow, IEEE
Currency
—computed: no publish/modified date found on the page (never guessed)