Systems & Networks · Computer Science & AI
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 9
An Information-Geometric Framework for OCS-Reconfigurable Interconnect Topology in Large-Scale AI Clusters
Optical Circuit Switches (OCS) make inter-pod wiring in AI clusters programmable, posing design questions on wiring choice, symmetry departure, and reconfigurations.DROD: Dual-Ratio Ordered Dropout for Federated Continual Learning on Heterogeneous Devices
Federated learning for capacity-heterogeneous clients and federated continual learning are each well studied on their own, but their intersection -- clients holding differently sized models while…Federated Learning for Malware Image Classification under Data Heterogeneity
Malware image classification (MIC), in which binary files are converted into visual representations and deep neural networks are trained to identify malware families, has recently emerged as an…ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference
Test-time scaling (TTS) improves large language model (LLM) reasoning by sampling multiple answers, but fixed sampling budgets ignore both query difficulty and the changing energy cost of inference…StreamAhead: Real-Time Streaming Video Question Answering via Adaptive Precomputation
Real-time streaming video question answering with vision-language models must answer queries that arrive at any time with low latency while sustaining the video-processing rate required by the…Koi: Evidence-Guided, Self-Calibrating Planning for Multi-Cloud, Heterogeneous LLM Inference Fleets
Deploying a large language model (LLM) requires choosing the right accelerator, parallelization strategy, engine configuration, and replica count amongst ~140 other variables across clouds, regions…Adding Heavy-Ball Momentum to Distributed Constrained Optimization for Energy Resource Management Subject to Time-Delays and Quantization
This paper studies a hybrid discrete-time signed gradient-consensus method with heavy-ball momentum, paired communication delays, and fixed logarithmic quantization.Low-Bit Distributed Constrained Optimization with Application to Optimal Scheduling and Allocation
This paper proposes a distributed resource allocation algorithm in which each agent communicates only the sign of its local cost gradient, a single bit per directed link per iteration.TideKV: Delayed KV Eviction via Layer-Wise Chunked Prefill for Memory-Efficient Long-Context LLM Inference
Long-context large language model (LLM) inference is important for modern intelligent applications but remains constrained by peak memory during prompt prefill.One Communicator, Three Vendors: Extending the MoA Predictive Theory to a Real NVIDIA+AMD+CPU MPI Ensemble
The companion MPI-extension paper validated the first communication-bearing machine-shape ensemble: four NVIDIA A100 ranks and four CPU ranks on one NCSA Delta node under one Open MPI job…FederatedBytes: Tokenizer-Free, Byte-Level Small Language Models for Efficient Multimodal Federated Learning
Federated Learning (FL) is a powerful machine learning framework that allows building a global model from the contributions of multiple, distributed clients, while ensuring the privacy of their…Scalable Levenberg-Marquardt Training Framework for Deep Learning
The Levenberg-Marquardt (LMA) algorithm is a widely used nonlinear least-squares solver known for its rapid convergence.Encodings and Node Architecture: Lessons from the Fault-Tolerant Distributed Transversal Fanout
An encoding choice specifies the quantum error-correcting code used to encode logical qubits at a given node.TALLOP: Joint Optimization of Local Training and Communication Compression for Time-Constrained Federated Learning
Minimizing the wall-clock time required to reach a target accuracy is crucial in federated learning (FL), where many applications require frequent model updates as data distributions evolve over time.DIFFLET: A Fast Inference Engine for Diffusion Transformer Models on AWS Trainium
On AWS Trainium2, every component of a diffusion model runs as a program compiled ahead of time for fixed shapes, while pipelines differ across models, prompt lengths across requests, and caching…Flash RNN-T: Memory-Efficient Joint Network and Transducer Loss Computation
The recurrent neural network transducer (RNN-T) remains a practical choice for streaming speech recognition, but training requires large intermediate tensors.Coordination-Free Workload Regularization for Variable-Cost Distributed Training
Synchronous data-parallel training usually assigns every rank the same number of records, even when record costs vary by orders of magnitude.HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding
Device-cloud speculative decoding reduces server computation, but growing a deep draft tree with a small language model (SLM) requires repeated passes on a mobile neural processing unit (NPU).When the KV Cache Is Slower Than the GPU: Scaling Remote KV Tiers for Context-Parallel Prefill
Reusing the attention KV cache across requests is a standard way to cut prefill cost in long-context LLM serving, and production systems increasingly back the per-engine cache with a remote tier…ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training
Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency remains a major bottleneck.