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 17
When Correct Memory Goes Wrong: Fuzzing Persistent Memory Use in LLM Agents
Persistent memory helps LLM agents carry information across long interactions, but correct memory can still be used incorrectly when queries change or memory states evolve.Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation
Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers.Kafila: Serving Large Language Models on a Trusted Set of Heterogeneous Commodity Machines
Between them, the members of a research group or a circle of friends own several consumer computers, none large enough to run a capable large language model.Broken Symmetry in BF16 Attention: Why FlashAttention Gradients Blow Up Late in Training
BF16 is now standard in large-scale pretraining, including in fused attention kernels such as FlashAttention, and these kernels are widely trusted.Spexis: Speculative Lookahead Scheduling for LLM Inference
Spexis is a multi-GPU LLM inference framework that improves the efficiency of pipeline and tensor parallelism through speculative parallelism.Before Agents Act: Assurance-Aware Semantic Scheduling for Evidence Acquisition in Distributed Systems
Tool-using agents can initiate consequential infrastructure changes, yet evidence required for admission may expire while other checks run or depend on a shared fault domain.DPS: Dual-Mode Precision LLM Serving with Semi-Unified Memory
Existing LLM serving systems virtualize and optimize KV-cache memory, but treat model-weight memory as fixed throughout execution.AgentWare: Automating the Lifecycle of Agentic Applications across the Edge-to-Cloud Continuum
Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity, limited observability, and the lack of…Nereus: Adaptive Parallelism for LLM Post-Training
Reinforcement learning (RL) post-training for large language models (LLMs) coordinates multiple models across generation, inference, and training on GPU clusters.Distributed Lower Bounds via Automatic Self-Reduction
The development of round elimination into a general-purpose technique [PODC 2019] marked a turning point in our understanding of the hardness of many graph problems in the distributed setting and…Accelerator Choice Is Not Enough: AlphaFold2 Inference on Cloud TPUs
AlphaFold2 is written in JAX, so the same inference code compiles and runs unchanged on CPUs, GPUs and Google Cloud TPUs.TempoKV: Timely Staging of LLM KV Caches for Memory-Semantic Flash
Reusable prefix key-value (KV) caches can outgrow GPU memory in large language model (LLM) serving.E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU
We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU.EdgeCraft: Automated Model Crafting for Edge IoT
Machine learning (ML) increasingly powers Internet of Things (IoT) applications at the edge.WavePP: High-Throughput Pipeline Parallel LLM Prefill under Prefix Reuse
Pipeline parallelism can improve prefill throughput by processing multiple request chunks concurrently across different stages of the model.SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning
Large language models are trained with backpropagation, whose global gradient coordinates all layers but forces each to hold its activations and wait for the gradient to pass back through every…TopoEP: Topology-Aware Load Balancing for Expert-Parallel MoE Training
Dynamic routing creates severe load imbalance in large-scale expert-parallel Mixture-of-Experts (MoE) training, turning GPUs that host hot experts into stragglers.Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions
Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss.GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation
Writing fast GPU code for physical simulation is difficult: implementations must preserve numerical accuracy while handling irregular data access, synchronization, and iterative solvers.Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept stage.