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2,653 papers
Abstract Neurosymbolic AI and Zero Trust Architecture (ZTA) are increasingly proposed together as a combined approach to securing cloud-edge IoT continuums in federal and critical infrastructure sectors, on the premise that neurosymbolic systems can supply the explainable, robust threat detection that continuous-verifi...
L-shaped SFT is presented, a split fine-tuning framework that removes the need for continuous client participation and introduces one-shot SFT, in which clients upload activations once and then go offline while the server continues optimization over cached representations.
This work proposes CAPSUM, a capacity-aware admission policy with an elastic specialization, CAPSUM-E, and implements an exact local offline dynamic program and compares against direct common-model baselines and documented source-derived adapters for EDP-A, OREO, and uEDC-L.
This work proposes CoSMO (Co-design of Semantic-state Management and Offloading), a cooperative event-driven reinforcement learning (RL) framework that coordinates semantic status management and selective offloading through realized task utility.
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The review combines recent research findings with guidance on new investigation tracks for developing all-encompassing AI systems which both spread far-reaching outcomes and follow sustainability requirements.
Cloud computing has revolutionized the way computational resources are made available through scalable, flexible, and on-demand services. As promising as it is, efficient task scheduling remains an important challenge because of varied workloads, heterogeneous infrastructures, and multi-objective optimization requireme...
The creation and maintenance of digital twins for critical infrastructure systems, such as power grids, water distribution networks, and transportation systems, presents significant computational challenges. Traditional centralized approaches struggle to handle the massive amounts of data generated by distributed senso...
"This dataset contains Ultra-Wideband (UWB) ranging and localization measurements collected for the evaluation of adaptive and federated learning methods for indoor positioning in challenging environments. The data were acquired in a controlled two-room laboratory deployment designed to capture realistic line-of-sight...
Reliable data movement is essential to the Worldwide LHC Computing Grid. This project asks whether FTS queues that share a storage endpoint contain useful information about one another’s future state. I process 52,037,899 raw queue records from January 2026 into 23,820,642 sparse queue states sampled every 2 minutes. A...
Compiler Mimics Spacetime (NOT vice versa) Date: September 2026Framework: P_{7777_T} Manifold / ACT-Ω v26.3 v26.5 LOCKEDInvariants: 15.965Hz (τ=62.636ms) | Tr(U_res)=1.00000 | J_sys≤1.4411J | H¹=0 | Δ=0.053μT | γ_fric=1.3479e-10 N | c_eff=1.707e11 | |wL-wR|≤0.02 | q0=1 | Ωc=0.376=47/125 | η=1e-4 0. LOCKED CLARIFICATION...
This paper proposes a novel network computation model termed "Space-Time Network Computation" (STNC). The core claim is that integrating spatial and temporal dimensions offers a more efficient approach to processing data exhibiting spatial and temporal dependencies. STNC leverages an extension of Graph Neural Networks...
Reliable data movement is essential to the Worldwide LHC Computing Grid. This project asks whether FTS queues that share a storage endpoint contain useful information about one another’s future state. I process 52,037,899 raw queue records from January 2026 into 23,820,642 sparse queue states sampled every 2 minutes. A...
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
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