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
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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
Synthetic Aperture Radar (SAR) provides all-weather and high-resolution imaging capabilities, making it an important data source for maritime ship detection. However, coherent speckle noise and complex background clutter can obscure weak target responses, while the limited computing resources of edge platforms impose...
Fei Lei, Xiang-Yu Peng, Dun Ao· Measurement science and tech...· 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
Traditional centralized energy management system (EMS) has some problems, such as delayed response, low reliability and difficult data sharing. This paper proposes a distributed EMS architecture that integrates the Internet of Things (IoT) and edge computing. The system adopts a three-layer architecture of perception l...
Healthcare is increasingly extending beyond episodic measurements obtained in hospitals and clinics toward continuous, context-aware observation at the point of care [...]
Photonic Universe Hypothesis (PUH) — Corrigendum. WHAT IS WITHDRAWN. T365 Result 365.1 — "the cosmological constant is the gradient energy of an incompletely relaxed lattice" — IS WITHDRAWN. T365 Result 365.4 — the prediction of w slightly greater than −1 — IS WITHDRAWN WITH IT. The error was not in the reading of T175...
Brian Martell· Zenodo (CERN European Organi...· 0 citations
A distributed IDS-based collaborative FL (IDS-CFL) across different learning levels: device and fog-cloud, to reduce data transfer and improve accuracy and enable fast processing is proposed, using a Deep Convolutional Generative Adversarial Network model to train data at each level.
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
