The rapid evolution of distributed computing environments, 5G/6G network architectures, and AI-driven orchestration frameworks has created a pressing need for intelligent, adaptive, and trust-aware compute infrastructures. Traditional centralized cloud paradigms are increasingly insufficient for handling ultra-low latency applications, heterogeneous workload distributions, and dynamic trust requirements in multi-tenant environments. This paper proposes an Intelligent Networked Compute Architecture (INCA) integrating Autonomous Processing Units (APUs) with Credibility Scoring Models (CSMs) to enable self-organizing, trust-aware, and latency-optimized distributed computation.

The proposed architecture builds upon advancements in network slicing, edge computing, and orchestration frameworks as described in modern 5G/6G research ecosystems. Concepts such as latency-driven slicing and QoE-aware orchestration (Zanzi et al., 2020), isolated RAN slicing architectures (Liubogoshchev et al., 2023), and modular 6G network slice frameworks (Kuklinski et al., 2021) provide foundational support for distributed compute intelligence. Additionally, network slice isolation principles (Wong et al., 2022) and survey insights on 5G slicing challenges (Foukas et al., 2017) further establish the necessity for adaptive orchestration layers in future networks.

A key innovation of this work is the integration of Credibility Scoring Models that evaluate computational nodes, data sources, and task executors based on reliability, historical accuracy, and trust propagation metrics. This mechanism is inspired by multi-agent AI optimization and trust-aware scheduling techniques discussed in Smart Cloud Optimization Platforms (Ramaswamy et al., 2026), which emphasize energy-aware and trust-driven orchestration in distributed environments.

The architecture introduces Autonomous Processing Units capable of decentralized decision-making, workload negotiation, and dynamic resource allocation across edge-cloud continua. Experimental reasoning suggests improved task execution latency, enhanced trust validation, and reduced computational bottlenecks in heterogeneous environments.

Overall, this paper contributes a unified theoretical and architectural model that bridges AI-driven orchestration, network slicing, and trust-based distributed computation, enabling next-generation intelligent networked systems.

Intelligent networked computing, Autonomous Processing Units, credibility scoring, edge computing

Ramaswamy, K., Kodela, S., Pal, M., & Chauhan, R. (2026, March). Smart Cloud Optimization Platform Using Multi-Agent AI, Trust Analytics, and Energy-Aware Task Scheduling. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.

3GPP TS 28.530, Tech. Specification. Management and Orchestration, v17.4.0 Rel. 17, March 2023.

https://www.etsi.org/technologies/multi-access-edge-computing

Philip, P. G. (2024). Digital Twinning, Artificial Intelligence, and Project Management 5.0: The Future of Intelligent Project Delivery . The American Journal of Interdisciplinary Innovations and Research, 6(12), 63–80. Retrieved from https://theamericanjournals.com/index.php/tajiir/article/view/digital-twinning-ai-project-management-5-0

L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, H. D. Schotten, and X. Costa-Perez : Laco: A latency-driven network slicing orchestration in beyond-5G networks, IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 667 - 682, 2020.

M. Liubogoshchev, D. Zudin, A. Krasilov, A. Krotov, and E. Khorov : DeSlice: An architecture for QoE-aware and isolated RAN slicing, MDPI Sensors 23 ( 9 ): 4351, April 2023.

Vuppala, N.S.M., 2025. APACHE SPARK-BASED DISTRIBUTED FRAMEWORK FOR SCALABLE EDI TRANSACTION PROCESSING. International Journal of Applied Mathematics, 38(11s), pp.903-926. DOI: https://doi.org/10.12732/ijam.v38i11s.1219

S. Kuklinski, L. Tomaszewski, R. Kolakowski, and P. Chemouil : 6G-Lego: A framework for 6G network slices, Journal of Communications and Networks, vol. 23, no. 6, pp. 442 - 453, 2021.

S. Wong, B. Han, and H. D. Schotten : 5G network slice isolation, MDPI Network, vol. 2, no. 1, pp. 153 - 167, 2022.

X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina : Network slicing in 5G: Survey and challenges, IEEE Communications Magazine, vol. 55, no. 5, pp. 94 - 100, 2017.