惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

G
Google Developers Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
V
Visual Studio Blog
雷峰网
雷峰网
博客园_首页
The Cloudflare Blog
Hugging Face - Blog
Hugging Face - Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
爱范儿
爱范儿
小众软件
小众软件
D
Docker
P
Proofpoint News Feed
B
Blog
Vercel News
Vercel News
B
Blog RSS Feed
U
Unit 42
月光博客
月光博客
The GitHub Blog
The GitHub Blog
Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
I
InfoQ
Recent Announcements
Recent Announcements

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
A General Input-Dependent Colorless Computability Theorem...
Yannis Coutouly, Emmanuel Godard · 2025-11-06 · via cs.DC updates on arXiv.org

Distributed computing tasks can be presented with a triple $(\I,\Ou,Δ)$. The solvability of a colorless task on the Iterated Immediate Snapshot model (IIS) has been characterized by the Colorless Computability Theorem \cite[Th.4.3.1]{HKRbook}. A recent paper~\cite{CG-24} generalizes this theorem for any message adversaries $\ma \subseteq IIS$ by geometric methods. In 2001, Mostéfaoui, Rajsbaum, Raynal, and Roy \cite{condbased} introduced \emph{condition-based adversaries}. This setting considers a particular adversary that will be applied only to a subset of input configurations. In this setting, they studied the $k$-set agreement task with condition-based $t$-resilient adversaries and obtained a sufficient condition on the conditions that make $k$-Set Agreement solvable. In this paper we have three contributions: -We generalize the characterization of~\cite{CG-24} to \emph{input-dependent} adversaries, which means that the adversaries can change depending on the input configuration. - We show that core-resilient adversaries of $IIS_n$ have the same computability power as the core-resilient adversaries of $IIS_n$ where crashes only happen at the start. - Using the two previous contributions, we provide a necessary and sufficient characterization of the condition-based, core-dependent adversaries that can solve $k$-Set Agreement. We also distinguish four settings that may appear when presenting a distributed task as $(\I,\Ou,Δ)$. Finally, in a later section, we present structural properties on the carrier map $Δ$. Such properties allow simpler proof, without changing the computability power of the task. Most of the proofs in this article leverage the topological framework used in distributed computing by using simple geometric constructions.