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

大猫的无限游戏
大猫的无限游戏
MyScale Blog
MyScale Blog
雷峰网
雷峰网
量子位
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 叶小钗
T
Tailwind CSS Blog
月光博客
月光博客
博客园 - 【当耐特】
博客园_首页
罗磊的独立博客
博客园 - 三生石上(FineUI控件)
IT之家
IT之家
爱范儿
爱范儿
阮一峰的网络日志
阮一峰的网络日志
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
WordPress大学
WordPress大学
The Cloudflare Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
S
SegmentFault 最新的问题
人人都是产品经理
人人都是产品经理
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell

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 impact of unlimited computational power in OBLOT: ...
Serafino Cicerone, Alessia Di Fonso, Gabriele Di Stefano, Alfred · 2025-09-05 · via cs.DC updates on arXiv.org

The OBLOT model has been extensively studied in theoretical swarm robotics. It assumes weak capabilities for the involved mobile robots, such as they are anonymous, disoriented, no memory of past events (oblivious), and silent. Their only means of (implicit) communication is transferred to their positioning, i.e., stigmergic information. These limited capabilities make the design of distributed algorithms a challenging task. Over the last two decades, numerous research papers have addressed the question of which tasks can be accomplished within this model. Nevertheless, as it usually happens in distributed computing, also in OBLOT the computational power available to the robots is neglected as the main cost measures for the designed algorithms refer to the number of movements or the number of rounds required. In this paper, we prove that for synchronous robots moving on finite graphs, the unlimited computational power (other than finite time) has a significant impact. In fact, by exploiting it, we provide a definitive resolution algorithm that applies to a wide class of problems while guaranteeing the minimum number of moves and rounds.