{"total":23,"rows":[{"id":"2503.12228","title":"Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments","created":"2025-03-15","cited_by_count":12,"area":null},{"id":"2512.06547","title":"A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation","created":"2025-12-06","cited_by_count":12,"area":null},{"id":"2411.19870","title":"DeMo: Decoupled Momentum Optimization","created":"2024-11-29","cited_by_count":11,"area":null},{"id":"2505.01043","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2412.04787","title":"Direct Quantized Training of Language Models with Stochastic Rounding","created":"2024-12-06","cited_by_count":6,"area":null},{"id":"2506.10911","title":"NoLoCo: No-all-reduce Low Communication Training Method for Large Models","created":"2025-06-12","cited_by_count":5,"area":null},{"id":"2607.09084","title":"A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design","created":"2026-07-10","cited_by_count":5,"area":null},{"id":"2504.08112","title":"Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2412.12487","title":"Echo: Simulating Distributed Training At Scale","created":"2024-12-17","cited_by_count":2,"area":null},{"id":"2504.03668","title":"Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2509.03047","title":"FlashRecovery: Fast and Low-Cost Recovery from Failures for Large-Scale Training of LLMs","created":"2025-09-03","cited_by_count":2,"area":null},{"id":"2512.01039","title":"Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI","created":"2025-11-30","cited_by_count":2,"area":null},{"id":"2601.19362","title":"Revisiting Parameter Server in LLM Post-Training","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2510.24061","title":"FALQON: Accelerating LoRA Fine-tuning with Low-Bit Floating-Point Arithmetic","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2606.13892","title":"Crypto x AI, AI x Crypto: A Survey","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2608.11919","title":"LazyTrain: Limited-resource Allocation toward Zero-waste Yield Optimization in Large Language Model Training","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2411.11560","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","created":"2024-11-18","cited_by_count":0,"area":null},{"id":"2509.02480","title":"MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2602.04816","title":"Horizon-LM: A RAM-Centric Architecture for LLM Training","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2602.21081","title":"Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2604.23467","title":"Hybrid JIT-CUDA Graph Optimization for Low-Latency Large Language Model Inference","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2604.24013","title":"CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2609.19924","title":"The Life of a Token: from Words to Bits on the Wire","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}