The paper investigates when adding a second AI component—an AI supervisor—benefits network‑fraud decision making. Using a role‑aware Decider‑Supervisor framework with blockchain auditability, the authors evaluate four configurations combining centralized ML, federated meta‑models, and large language model variants. Results show that only specific setups (e.g., DS‑4 Base in extreme high‑fraud scenarios) reduce errors, while most supervised configurations either underperform or add operational burden.
By Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli
arXiv:2607. 20700v1 Announce Type: cross Abstract: The operation of blockchain is governed by consensus algorithms (CA).
By Bruno Ramos-Cruz, Javier Andreu-Perez, David Richerby, Luis Mart\'inez
The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
By Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem Ullah
arXiv:2607. 24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical resources to minimize costs and maximize performance.
By Mehryar Majd, Feng Cheng, Ali Pahlevan
arXiv:2512. 13666v2 Announce Type: replace-cross Abstract: The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems.
By Weihang Cao, Mustafa Doger, Sennur Ulukus
arXiv:2606. 02958v1 Announce Type: cross Abstract: Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters, activations, optimizer state, and per-device updates-cannot be exported outside an administrative boundary.
By Hina Dixit, Punit Kumar, Irene Tenison, Nevasini Sasikumar
arXiv:2607. 17460v1 Announce Type: new Abstract: In decentralized storage systems, audit results are often not used directly to guide later redundancy and shard-placement decisions, which can lead to inefficient resource allocation and delayed recovery.
By Shuaiwen Li, Weihang Yu, Ke Wang, Meng Han
arXiv:2608. 02986v1 Announce Type: cross Abstract: A software agent on a public blockchain accumulates authority and economic stakes, raising the engineering question of what makes it count as an individual.
By Keisuke Suzuki
arXiv:2607. 16109v1 Announce Type: new Abstract: State machine replication (SMR) and Byzantine fault-tolerant (BFT) consensus guarantee agreement despite a bounded number of arbitrary, colluding faulty participants.
By Jun He, Deying Yu
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
By Rabimba Karanjai (Larry), Yang Lu (Larry), Richard Williamson (Larry), Hemanth Hm (Larry), Prakhar Mehrotra (Larry), Lei Xu (Larry), Weidong (Larry), Shi
arXiv:2608.30050v1 Announce Type: new
Abstract: Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibr...
By Haoting Zhang, Haoxian Chen, Jiayuan Sheng, Donglin Zhan, Zeyu Zheng, David D. Yao, Wenpin Tang
As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions.