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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
No Cords Attached: Coordination-Free Concurrent Lock-Free...
Yusuf Motiwala · 2025-11-12 · via cs.DC updates on arXiv.org

The queue is conceptually one of the simplest data structures-a basic FIFO container. However, ensuring correctness in the presence of concurrency makes existing lock-free implementations significantly more complex than their original form. Coordination mechanisms introduced to prevent hazards such as ABA, use-after-free, and unsafe reclamation often dominate the design, overshadowing the queue itself. Many schemes compromise strict FIFO ordering, unbounded capacity, or lock-free progress to mask coordination overheads. Yet the true source of complexity lies in the pursuit of infinite protection against reclamation hazards--theoretically sound but impractical and costly. This pursuit not only drives unnecessary complexity but also creates a protection paradox where excessive protection reduces system resilience rather than improving it. While such costs may be tolerable in conventional workloads, the AI era has shifted the paradigm: training and inference pipelines involve hundreds to thousands of concurrent threads per node, and at this scale, protection and coordination overheads dominate, often far heavier than the basic queue operations themselves. This paper introduces Cyclic Memory Protection (CMP), a coordination-free queue that preserves strict FIFO semantics, unbounded capacity, and lock-free progress while restoring simplicity. CMP reclaims the strict FIFO that other approaches sacrificed through bounded protection windows that provide practical reclamation guarantees. We prove strict FIFO and safety via linearizability and bounded reclamation analysis, and show experimentally that CMP outperforms state-of-the-art lock-free queues by up to 1.72-4x under high contention while maintaining scalability to hundreds of threads. Our work demonstrates that highly concurrent queues can return to their fundamental simplicity without weakening queue semantics.