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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
The local complexity of certifying parity
Nicolas Bousquet, Laurent Feuilloley, Jorge Valenzuela, Sébastie · 2026-06-03 · via cs.DC updates on arXiv.org

In this paper, we consider the problem of locally certifying that the size of a network is even, or more generally, congruent to some fixed number. The parity property is one of the simplest global properties, and it plays an intriguing role in local certification. On the one hand, it is one of the simplest properties in cycles because it is equivalent to 2-colorability, and hence can be certified with a single bit. On the other hand, in general graphs, no non-trivial lower bound on the size of the certificates is known, and the known upper bound basically consists in certifying the \emph{exact} value of $n$. In addition, the nature of the problem makes all the known lower bound approaches fail. We uncover a surprising landscape for parity across different models and graph structures: * In general graphs equipped with identifiers, when allowing verification radius 2, parity can be certified with a constant number of bits. * But in the model of anonymous graphs and allowing verification radius only 1, parity requires $Ω(\log \log^*n)$ bits. * Finally, in bounded expansion graph classes (such as bounded-degree graphs and planar graphs), the lower bound does not apply: in the same restricted model we can design a constant-size certification. We introduce several new tools that we expect to be useful in other contexts, in particular ways to \emph{encode a parent at each node with a constant number of bits} (via implicit use of the IDs and conflict-free colorings) and a new lower bound technique, with complex topologies and higher-order Ramsey-type arguments.