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Distributed & Parallel Computing
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507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers, page 21
Adaptive Tiling for Least-Squares Phase Unwrapping: Runtime and Accuracy
Phase unwrapping estimates the missing multiples of $2π$ in measured phase images.Byzantine Causal Reliable Broadcast (BCRB) with Constant-Size Message Metadata
Asynchronous Byzantine Reliable Broadcast (BRB) is a fundamental primitive that guarantees agreement and validity in distributed systems subject to Byzantine faults, but it lacks ordering guarantees.MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents
As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet underexplored factor.A principled approach for energy-efficient training via phase-aware GPU frequency tuning
Modern AI model training imposes unprecedented computational demands, making it a key contributor to datacenter energy consumption.Conduit: An Experience Data Plane for Distributed Reinforcement Learning
Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer.SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture
LLM inference on edge devices is constrained by computational and memory resources, making efficient autoregressive decoding challenging.Data center cooling choices shift water impacts across the grid: An integrated water-energy model for sustainable data center development
Data centers are being developed at an unprecedented pace, yet their energy and water impacts, and the spatial and temporal distribution of these impacts, remain poorly characterized.WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning
Weight transfer - the propagation of updated parameters from trainers to rollout generators - is becoming an important performance bottleneck in reinforcement learning (RL) systems for LLMs.ZeroGate: Trust-Preserving Fast Paths for Governed AI Agent Runtimes
Moving authorization earlier can shorten an agent's dispatch boundary without removing authorization work.Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions
Reasoning-oriented reinforcement learning enables large language models to solve mathematical, coding, and other multi-step tasks, but shifts a substantial portion of the training cost to rollout…A Hybrid Rule-Based and AI-Augmented Framework for Automatic Failure Recovery in DevOps Deployments
Automatic failure recovery is another difficult area of DevOps deployments due to the complexity of the system, high workload and the limitations of the traditional rules-based or manual approach.Co-occurrence Patterns of LoRA Adapters in Production Diffusion Model Inference Services
Low-rank adaptation (LoRA) has become a key technology for serving large-scale personalized large language models and diffusion models in the cloud.Conflicting Pattern Formation by Teams of Anonymous, Fully Disoriented Robots
Two groups of autonomous, anonymous, and oblivious mobile robots are deployed in the two-dimensional Euclidean plane, each assigned a distinct task.TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send production logs to a hosted model at…SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning
Decentralised federated learning replaces server aggregation with peer-to-peer model exchange, making collaborator selection a local decision under uncertainty.Adaptive Determinantal Client Scheduling in Federated Learning
Scheduling clients for model training is critical in federated learning due to both data and system heterogeneity.Mask-Aware Execution for Efficient JEPA Training
Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models across vision, video, audio, brain dynamics, and…Scout: Open-World Species Recognition on the Edge
Large vision-language models (VLMs) enable recognition beyond a fixed class set, but their computational demands prevent them from running on many edge devices.Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend
Apple Silicon machines with large unified memory make it possible to hold large tensors on a desktop GPU.Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving
Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectories extend.