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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
KRNC: New Foundations for Permissionless Byzantine Consen...
Clinton Ehrlich, Anna Guzova · 2019-09-17 · via cs.DC updates on arXiv.org

This paper applies biomimetic engineering to the problem of permissionless Byzantine consensus and achieves results that surpass the prior state of the art by four orders of magnitude. It introduces a biologically inspired asymmetric Sybil-resistance mechanism, Proof-of-Balance, which can replace symmetric Proof-of-Work and Proof-of-Stake weighting schemes. The biomimetic mechanism is incorporated into a permissionless blockchain protocol, Key Retroactivity Network Consensus ("KRNC"), which delivers ~40,000 times the security and speed of today's decentralized ledgers. KRNC allows the fiat money that the public already owns to be upgraded with cryptographic inflation protection, eliminating the problems inherent in bootstrapping new currencies like Bitcoin and Ethereum. The paper includes two independently significant contributions to the literature. First, it replaces the non-structural axioms invoked in prior work with a new formal method for reasoning about trust, liveness, and safety from first principles. Second, it demonstrates how two previously overlooked exploits, book-prize attacks and pseudo-transfer attacks, collectively undermine the security guarantees of all prior permissionless ledgers.