Cloud–edge collaborative inference has emerged as a promising paradigm to address the latency, energy, and privacy challenges of large language models (LLMs). However, current offloading mechanisms often struggle to efficiently capture the dynamic semantic dependencies between historical context and ongoing queries. Th...
Xianzhong Tian, Yu Wang, Guanpeng Zhu· IEEE Internet of Things Jour...· 0 citations
Due to limited computing resources and severe blockage in dense urban environments, uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) faces significant challenges in serving the Internet of Things (IoT) devices. In this article, by deploying multiple simultaneously transmitting and reflecting reconfigur...
Long Jiao, Ling Gao, Jie Zheng et al.· IEEE Internet of Things Jour...· 0 citations
Energy-efficient neuromorphic hardware with ultralow power consumption is increasingly promising for edge–artificial intelligence (AI) applications. Most digital neuromorphic hardware is designed with asynchronous circuits as they naturally match the sparsity and event-driven features of neuromorphic computing. However...
Jian Zhang, Yuan Hua, Ji-Lin Zhang et al.· IEEE Transactions on Compute...· 1 citation
This is the extended, definitive version (v16.2) of the P0_Distilled research programme, with full technical appendices — it supplements the distilled entry-point paper (P0_Distilled_v0.1) with the complete Lakatosian structure, derivations, and experimental protocol. The programme's hard core is a single metaphysical...
Francesco Iavarone· Zenodo (CERN European Organi...· 0 citations
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Predictive analysis techniques for crop and soil sensors have revolutionized agriculture by enabling data-driven decision-making and optimizing farming practices. This chapter begins with an overview of crop and soil stresses, highlighting their impact on agricultural productivity. It then examines enabling technologie...
Avani Vyas, Anil Pratap Singh, Shivani Sharma et al.· Internet of Things and Unman...· 0 citations
This publication establishes the unified Arcstone Executive Epistemic Whitepaper Series (EXEC01–EXEC03), providing the foundational theory and operational mechanics for machine-native execution boundaries and zero-operational-drag computing (C_ops = 0).INCLUDED PAPERS IN THIS BLOCK:1. ARC-PUB-2026-EXEC01: Human-First T...
Autonomous AI agents typically rely on multi-turn ReAct loops that demand repeated system-prompt evaluation, persistent state tracking, and frequent tool selection. On constrained edge hardware—especially CPU-only devices with approximately 8 GB of RAM—this style of orchestration creates two compounding failure modes:...
Siddardha Shayini· Zenodo (CERN European Organi...· 0 citations
This work addresses the challenge of performing distributed online convex optimization in resource-constrained systems, including IoT networks and wireless sensor networks, where communication resources are limited. We develop an edge-based event-triggered (EBET) distributed inexact gradient descent algorithm that depa...
Artificial intelligence can support climate mitigation, but the computing systems used to develop and operate it can also intensify energy demand, data movement, and greenhouse-gas emissions. This paper presents and evaluates CA-FMLOps—Carbon-Aware Frugal Machine Learning Operations—as a design-science framework for cl...
Savio Chacko Xavier· Zenodo (CERN European Organi...· 0 citations
Paper 1056 and Paper 1057 measured that the fine-structure constant does not move with the units but moves with energy, and why it stops at low energy. Why, then, is there a quantity built from the same charge and the same Planck constant that does not move at all? This paper answers that. No new theorem or law is clai...
Yuuki Yamagishi· Zenodo (CERN European Organi...· 0 citations
This deposit contains one finite symbolic word, the tool used to analyse it, and the report the tool produces. The word was made by applying the EOA beta-operator to a phonetic encoding called E5. The input was the spoken English letter name "a". The control parameter, LCR, was set to 3.14159. The word itself, its SHA-...
Bahaa Budargham· Zenodo (CERN European Organi...· 0 citations
This paper provides a unified and complete characterization of nucleons within the State-Relational Entropy (SRE) dynamic framework: nucleons are stable composite objects emerging from binary self-organizing networks on the tripartite Y-shaped coherent core, constrained by the two-state opening/closing of dormant edges...
Yue Lu· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
