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

Google DeepMind News
Google DeepMind News
L
LangChain Blog
H
Help Net Security
博客园_首页
T
Tailwind CSS Blog
Microsoft Security Blog
Microsoft Security Blog
T
The Blog of Author Tim Ferriss
雷峰网
雷峰网
Recent Announcements
Recent Announcements
D
DataBreaches.Net
U
Unit 42
Vercel News
Vercel News
I
InfoQ
Martin Fowler
Martin Fowler
Microsoft Azure Blog
Microsoft Azure Blog
Apple Machine Learning Research
Apple Machine Learning Research
S
SegmentFault 最新的问题
Jina AI
Jina AI
博客园 - 叶小钗
博客园 - 【当耐特】
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
月光博客
月光博客
Last Week in AI
Last Week in AI

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
On the Decidability of Distributed Tasks with Output Sets...
[Submitted on 8 Apr 2026 (v1), last revised 25 Aug 2026 (this ve · 2026-04-08 · via cs.DC updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper studies the decidability of task problems, i.e., distributed problems expressed as sets of distributed tasks. Specifically, we introduce a new class of task problems called Set of Output Sets (SOS) problems. An SOS problem $\Pi_O$ is defined by a set $O$ (called SOS), and requires that the set of sets of distinct output values produced across all executions corresponds exactly to $O$. We then demonstrate that this class of problems is decidable: there is a procedure determining whether any SOS problem is solvable asynchronously under $f$ crashes. The decision rule is as follows. Every SOS problem is solvable when $f=0$. For $f > 0$, an SOS problem is solvable if and only if the graph $G=(O,\subset)$ is connected. In this graph, each vertex is an output set in $O$, and two vertices are linked by an edge whenever one output set includes the other. One of the surprising implications of our results is that, replacing validity by a completeness property (which guarantees that all output sets of size at most $k$ are produced), $k$-set agreement is solvable under any number of crashes $f \geq 0$ for $k>1$, and unsolvable under $f>0$ crashes only for $k=1$ (consensus). Finally, we study a novel family of problems called $d$-disagreement, which requires the system to always produce $d$ different output values, and we show that its implementability condition is related to the harmonic series.

Submission history

From: Junlang Wang [view email]
[v1] Wed, 8 Apr 2026 10:21:01 UTC (129 KB)
[v2] Tue, 25 Aug 2026 12:06:22 UTC (132 KB)