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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 18
Mixture-of-Kittens: MoE Megakernel for NVL72s
AI accelerator systems are rapidly consolidating into scale-up architectures, where tens to thousands of GPUs communicate over high-bandwidth, single-hop fabrics.Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs
Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task.Quantifying Teleportation Overhead in Distributed Unitary Coupled-Cluster Ansätze
Distributed quantum computing (DQC) has been proposed as a way to scale quantum algorithms for practical applications beyond monolithic quantum processor architectures.SketchSSM: Write to the Full State, Read from a Compact Sketch
Hybrid-attention models replace most softmax attention layers with linear attention, reducing KV-cache growth and enabling larger decode batches where recurrent state access becomes a major…ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Coding
As coding assistants become increasingly autonomous, developers run multiple sessions in parallel, shifting the challenge from code generation alone to coordinating and monitoring concurrent agent…When Privacy Moves ML-Mediated Decisions On Device: Information and Incentive Misalignment in Auctions
Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference.OLED-MoE: Accelerating MoE-Based dLLM Inference via Inter-Iteration Locality-Aware Expert Offloading
Semi-autoregressive diffusion large language models (dLLMs) improve decoding parallelism through iterative block-wise denoising, but scaling them with mixture-of-experts (MoE) layers introduces a…FoldAttention: Declared-Reference Softmax for Fast Decode and Deterministic Backward
Autoregressive decode repeatedly streams a growing KV cache, making attention a major cost at long context.Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data
Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated.EfficientAgent: What Makes KV Cache Offloading Work for Concurrent Agents?
LLM agents resend their whole conversation on every turn, and most of it was already processed on the previous turn.QwenGyre: An Elastic Reinforcement Learning Framework for Training xLong-Horizon Agents
Large language model (LLM) agents increasingly undertake extreme-long (xlong) horizon tasks, where a single execution can span hours, hundreds of model--environment interactions, and nearly 1M…Validating Memory-Optimal Transformer Kernels on Real Hardware: From Formal Derivation to Measured Performance Across Two HPC Clusters
We validate memory-optimal cost functions for transformer kernels derived via the Mathematics of Arrays (MoA).ADPTNet: Adaptive with Prescriptive Timescales Non-Linear SSM for Sequence Modelling
A central aim of neuromorphic computing is to provide a viable alternative to highly energy-intensive Transformer-based AI.Empowering Hybrid Attention Models on NPUs
Hybrid attention models have emerged as a crucial architecture for Large Language Models (LLMs) (e.g., the Qwen3.5 and Kimi series).Federated 3D Gaussian Splatting for Large-Scale Scene Reconstruction at Wireless Edge
Three-dimensional (3D) Gaussian splatting (3D-GS) has emerged as a promising technique for large-scale scene reconstruction due to its high rendering efficiency and fidelity.SparSP: Exploiting Communication Sparsity for Sequence-Parallel Video DiTs
Diffusion Transformers have become the dominant architecture for video generation.Federated Subspace Guided Vision-Language-Action Policy Distillation for Non-IID Multi-Robot Manipulation
Federated learning offers a natural way for multiple robots to jointly improve manipulation policies without requiring centralized access to training demonstrations.SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation
Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons.Trapped by Their Own Rollouts: Understanding Aggregation--Rollout Feedback in Federated On-Policy Distillation
On-policy distillation (OPD) is a promising approach to language-model adaptation, aligning teacher supervision with the student's own generated trajectories.Ask Without Telling: Local SLMs Consult Cloud LLMs Without Revealing Task Intent
As local small language models (SLMs) increasingly collaborate with more capable cloud large language models (LLMs), a natural privacy question arises: Can a local SLM obtain cloud LLM guidance…