惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

罗磊的独立博客
Google DeepMind News
Google DeepMind News
MyScale Blog
MyScale Blog
A
About on SuperTechFans
Martin Fowler
Martin Fowler
M
MIT News - Artificial intelligence
Recent Announcements
Recent Announcements
D
DataBreaches.Net
B
Blog
博客园 - 【当耐特】
爱范儿
爱范儿
有赞技术团队
有赞技术团队
P
Proofpoint News Feed
WordPress大学
WordPress大学
小众软件
小众软件
Apple Machine Learning Research
Apple Machine Learning Research
I
InfoQ
Engineering at Meta
Engineering at Meta
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Last Week in AI
Last Week in AI
Microsoft Azure Blog
Microsoft Azure Blog
雷峰网
雷峰网
量子位
G
Google Developers Blog

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Model Checking and Synthesis for Optimal Use of Knowledge...
Kaya Alpturer, Gerald Huang, Ron van der Meyden · 2025-05-05 · via cs.DC updates on arXiv.org

Logics of knowledge and knowledge-based programs provide a way to give abstract descriptions of solutions to problems in fault-tolerant distributed computing, and have been used to derive optimal protocols for these problems with respect to a variety of failure models. Generally, these results have involved complex pencil and paper analyses with respect to the theoretical "full-information protocol" model of information exchange between network nodes. It is equally of interest to be able to establish the optimality of protocols using weaker, but more practical, models of information exchange, or else identify opportunities to improve their performance. Over the last 20 years, automated verification and synthesis tools for the logic of knowledge have been developed, such as the model checker MCK, that can be applied to this problem. This paper concerns the application of MCK to automated analyses of this kind. A number of information-exchange models are considered, for Simultaneous and Eventual variants of Byzantine Agreement under a range of failure types. MCK is used to automatically analyze these models. The results demonstrate that it is possible to automatically identify optimization opportunities, and to automatically synthesize optimal protocols. The paper provides performance measurements for the automated analysis, establishing a benchmark for epistemic model checking and synthesis tools.