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

博客园 - Franky
U
Unit 42
MyScale Blog
MyScale Blog
B
Blog
阮一峰的网络日志
阮一峰的网络日志
量子位
IT之家
IT之家
The GitHub Blog
The GitHub Blog
F
Fortinet All Blogs
Recent Announcements
Recent Announcements
V
Visual Studio Blog
G
Google Developers Blog
Last Week in AI
Last Week in AI
雷峰网
雷峰网
博客园 - 聂微东
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
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
J
Java Code Geeks
博客园 - 司徒正美
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
月光博客
月光博客
aimingoo的专栏
aimingoo的专栏

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
Bitcoin-IPC: Scaling Bitcoin with a Network of Proof-of-S...
[Submitted on 29 Dec 2025 (v1), last revised 3 Jul 2026 (this ve · 2025-12-29 · via cs.DC updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper introduces Bitcoin-IPC, a protocol that scales Bitcoin through a network of permissionless, interconnected, programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. These subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security.
Subnets can communicate with each other and with Bitcoin: users deposit BTC from Bitcoin to a subnet and withdraw it back, and transfer wBTC directly between subnets. We provide formal definitions of these bridge protocols, incorporating a firewall property that limits the impact of malicious subnets on the security of the broader network.
Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB/tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary-transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.

Submission history

From: Orestis Alpos [view email]
[v1] Mon, 29 Dec 2025 12:58:41 UTC (1,390 KB)
[v2] Tue, 21 Apr 2026 14:21:35 UTC (1,390 KB)
[v3] Fri, 3 Jul 2026 08:14:25 UTC (1,350 KB)