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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
Automated Market Makers for Cross-chain DeFi and Sharded ...
Jon Michael Aanes, Jesper Balman Gravgaard, Peter Bro Miltersen, · 2023-09-26 · via cs.DC updates on arXiv.org

We consider Uniswap-like automated market makers, and, specifically, constant product liquidity pools, operating on blockchains. An important feature of Uniswap is the ability for a trader to carry out a sequence of asset swaps atomically, without other traders changing the prices along the way. This atomic-execution feature is not immediately available in cross-chain or sharded blockchain settings, where different liquidity pools are distributed across different chains or shards. Our contribution is a description and suggested implementation of a new functionality that might be added to individual liquidity pools, the {\em lock-swap}. The lock-swap enables a trader to get a guarantee for the price associated with a swap but only decide later whether or not to carry out the swap. Applied across several liquidity pools, it guarantees the trader assured prices for all swaps in a swap sequence and lets these prices inform the trader's decision about whether or not to carry out the sequence, thus essentially giving the trader the same benefits an atomic execution of the sequence would have provided him. However, in contrast to an atomic execution, our functionality does not prevent other traders from doing swaps during the time where the sequence is planned and possibly carried out. Nor does it prevent liquidity providers from adding or removing liquidity to and from the liquidity pool in that time period.