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

Google DeepMind News
Google DeepMind News
罗磊的独立博客
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Last Week in AI
Last Week in AI
云风的 BLOG
云风的 BLOG
T
The Blog of Author Tim Ferriss
Y
Y Combinator Blog
A
About on SuperTechFans
WordPress大学
WordPress大学
B
Blog
Martin Fowler
Martin Fowler
Jina AI
Jina AI
I
InfoQ
P
Proofpoint News Feed
小众软件
小众软件
S
SegmentFault 最新的问题
V
V2EX
B
Blog RSS Feed
量子位
大猫的无限游戏
大猫的无限游戏
aimingoo的专栏
aimingoo的专栏
博客园 - 三生石上(FineUI控件)
MongoDB | Blog
MongoDB | Blog
美团技术团队

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
Dynamic Fraud Proof
Gabriele Picco, Andrea Fortugno · 2025-02-15 · via cs.DC updates on arXiv.org

In this paper, we present a novel fraud-proof mechanism that achieves fast finality and, when combined with optimistic execution, enables real-time transaction processing. State-of-the-art optimistic rollups typically adopt a 7-day challenge window, during which any honest party can raise a challenge in case of fraud. We propose a new assert/challenge construction called "Dynamic Fraud Proofs" that achieves sub-second finality in ideal scenarios, while dynamically delaying settlement in the event of fraud detection and challenge resolution. The system relies on 1) a dynamic challenge period and 2) a configurable number of randomly selected verifier nodes who must interactively approve a state commitment without raising a challenge. If these conditions are not met, the state is not finalized, and the challenge period and approval criteria are dynamically adjusted. We provide a detailed analysis of the system's design, explaining how it maintains the assumption of a single honest node and addresses censorship attacks by inverting the traditional challenge process. Additionally, we formalize the system's probabilistic security model and discuss how bonding, incentives, and slashing mechanisms can encourage honest behavior, thereby increasing the likelihood of fast settlement in ideal scenarios.