Systems & Networks · Computer Science & AI
Distributed & Parallel Computing
The newest papers in Distributed & Parallel Computing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers, page 11
Distributed $(Δ+1)$-Coloring in $O(\sqrtΔ+\log^* n)$ Rounds
We give a deterministic distributed algorithm for $(Δ+1)$-coloring an $n$-node graph of maximum degree $Δ$ in $O(\sqrtΔ+\log^* n)$ rounds in both the LOCAL and CONGEST models.Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning
Agentic reinforcement learning (RL) is commonly used to enhance the capabilities of large language models (LLMs) in solving complex multi-turn tasks.PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents
Cloud graphical user interface (GUI) agents send instructions and screenshots to remote planners, exposing personally identifiable information (PII).Improved Massively Parallel Shortest Path Computation
We present new algorithms for approximate shortest path computation in weighted undirected graphs within the Massively Parallel Computation (MPC) model.Efficient Resource-Shiftable RL Post-training with Generative Rewards
Reinforcement learning (RL) post-training with generative reward models (GenRM) has become a central recipe for improving large language models (LLMs), and it is also increasingly…FORGE: Procedural Weight Generation for Sparse MoE Inference
We introduce FORGE (Fast On-chip Rotor Generation for Experts), a procedural weight generation system for Sparse Mixture-of-Experts (MoE) inference.Mixture of GFlowNets
Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward $R(x)$.Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations
Federated learning allows hospitals to train a shared mortality model without pooling their records, but published comparisons of aggregation strategies rarely control the evaluation protocol.Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum
Emerging healthcare services, such as real-time diagnostics, remote health monitoring and computer-assisted surgery, require stringent Quality of Service (QoS) guarantees while operating under…GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference
As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive.Agency as a New Organizing Abstraction for Computer Science
Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate actions, and interact with open environments.Almost Lossless Transfer for Cuttable Distributed Problems
We study constant-round LOCAL approximation for additive local vertex-subset minimization problems.A Quantum Analogue of the Covering Lemma: Limits of Exact Anonymous Computation
Can quantum communication determine the size of an anonymous network when no size bound is known?DualColl: Throughput-Optimal Bandwidth-Bound Collective Communications via Fractional Tree Packing
Large-scale distributed training increasingly makes collective communication bandwidth-bound.A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks
Temporal graph neural networks (TGNNs) achieve superior performance in many dynamic graph learning tasks.Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry
Modern cloud platforms generate high-dimensional, noisy telemetry from thousands of microservices.Lightweight Scheduling Policies for Continuously Batched LLM Serving Across GPU Architectures
Large language model serving is increasingly limited not only by model size and GPU speed, but also by scheduling during autoregressive decoding, where continuous batching interleaves requests at…A Self-Defining Operator for Distributed Applications
To operate distributed applications with high availability, operators must rapidly diagnose and mitigate any incident.Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows
Physical systems and distributed AI workflows can retain past information, receive delayed feedback and combine conflicting signals.Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving
Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions.