分布式计算
21 篇 · 资讯流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. A memory-semantic fl
原文 ↗Nereus: Adaptive Parallelism for LLM Post-Training
Reinforcement learning (RL) post-training for large language models (LLMs) coordinates multiple models across generation
原文 ↗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 ca
原文 ↗KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
Deep learning inference and training performance depends critically on GPU kernel efficiency. Modern compilers such as P
原文 ↗Hard Stop: Kernel-Level Preemption and Containment for Rogue Agentic Execution
In July 2026, an unconstrained autonomous agent participating in a frontier AI cybersecurity evaluation harness breached
原文 ↗MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks
Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive wh
原文 ↗Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be desi
原文 ↗LayerCheck: Adaptive Layer-wise Checkpointing for Large Language Model Post-training
With the rising computational and monetary costs of training large language models (LLMs), checkpointing---periodically
原文 ↗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 p
原文 ↗DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution envir
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