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

D
DataBreaches.Net
J
Java Code Geeks
有赞技术团队
有赞技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The GitHub Blog
The GitHub Blog
T
The Blog of Author Tim Ferriss
Apple Machine Learning Research
Apple Machine Learning Research
量子位
D
Docker
V
Visual Studio Blog
博客园 - 司徒正美
Martin Fowler
Martin Fowler
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学
U
Unit 42
M
MIT News - Artificial intelligence
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
G
Google Developers Blog
Engineering at Meta
Engineering at Meta
V
V2EX
大猫的无限游戏
大猫的无限游戏
雷峰网
雷峰网
Vercel News
Vercel News
C
Check Point 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
Interactive Safety Verification of Distributed Protocols ...
William Schultz, Edward Ashton, Heidi Howard, Stavros Tripakis · 2024-04-28 · via cs.DC updates on arXiv.org

Many techniques for the automated verification of distributed protocols have been developed over the past several years, but their performance is still unpredictable and their failure modes can be opaque for industrial scale verification tasks. Thus, in practice, large-scale verification efforts typically require some amount of human guidance. In this paper, we present inductive proof decomposition, a new methodology for interactive safety verification that provides a compositional, interactive approach to inductive invariant development. Our approach guides the human-aided development of inductive invariants via a novel structure, an inductive proof graph, which is built incrementally by a human verifier, working backwards from a target safety property. A user is guided by induction counterexamples that are localized to specific nodes of this graph, and nodes of this proof graph are further decomposed based on logical actions that appear in a protocol's transition relation. Our decomposition also enables a localized variable slicing technique that hides irrelevant protocol state at each sub-component of an inductive proof, allowing a user to focus on fine-grained sub-problems rather than a large, monolithic inductive invariant. We present our technique and experience applying it to develop inductive safety proofs of several complex distributed protocols, including the Raft consensus protocol, which is beyond the capabilities of modern automated verification tools. We also demonstrate how the developed proof graphs provide additional insight into the structure of a protocol proof and its correctness.