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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
Where Should I Deploy My Contracts? A Practical Experienc...
Cătălina Lazăr, Gabriela Secrieru, Emanuel Onica · 2025-04-13 · via cs.DC updates on arXiv.org

Blockchain networks provide a reliable trust anchor to decentralized applications (DApps) backed by smart contracts. The Ethereum ecosystem now encompasses most blockchain networks that provide compatible support for smart contracts code. Recently, many Ethereum Layer 2 (L2) rollup solutions emerged, meant to scale the base Layer 1 (L1) network, consequently decreasing transaction fees and diversifying the usage scenarios. Furthermore, the number of blockchain providers that offer access to the network infrastructure for both L1 and L2 continuously increases. A developer is faced with a multitude of deployment options and must weigh between the gains in costs and the losses in trust that are still an issue with L2. A decisive factor in this trade-off can be the use case itself, depending on its security requirements. Still, the evaluation of costs and performance cannot be ignored and should rely on a set of measurable metrics, although choosing the right metrics can be complicated. In this practical experience report, we explore the relevance of several such metrics in choosing between different providers and rollups. For this purpose, we perform evaluations for two use cases of DApps: a voting DApp with high security demands, suited for L1 deployment, and a cost-sensitive supply chain DApp, where L2 can be an option. We analyze a set of basic metrics by comparing these between two highly used access providers, Alchemy and Infura, for the L1 deployment case, and between two of the most popular rollups, Arbitrum One and OP Mainnet (Optimism), for the L2 deployment scenario.