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

Jina AI
Jina AI
N
Netflix TechBlog - Medium
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
DataBreaches.Net
人人都是产品经理
人人都是产品经理
aimingoo的专栏
aimingoo的专栏
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
I
InfoQ
F
Fortinet All Blogs
J
Java Code Geeks
Last Week in AI
Last Week in AI
美团技术团队
大猫的无限游戏
大猫的无限游戏
有赞技术团队
有赞技术团队
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园_首页
量子位
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件

cs updates on arXiv.org

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
Distributed Dominating Set With Optimal Rounds and Messag...
Sharareh Alipour, Ermiya Farokhnejad · 2026-06-14 · via cs updates on arXiv.org

We study the distributed minimum dominating set problem on graphs of arboricity $α$. Dory, Ghaffari, and Ilchi [PODC'22] showed that any algorithm achieving a constant or poly-logarithmic approximation factor needs at least $Ω(\logΔ/\log\logΔ)$ rounds in graphs of maximum degree $Δ$ and arboricity $α$, even when $α=2$ and even when the message sizes are unbounded. Although there is a variety of algorithms with a near-optimal round complexity of $O(\logΔ)$, it is natural to ask: What is the best approximation factor in the optimal round complexity of $O(\logΔ/\log\logΔ)$? We make progress in answering this question by describing a deterministic algorithm that obtains a $O\left( α\log Δ/ \log\log Δ\right)$ approximation without prior knowledge of $α$ with optimal round complexity of $O\left( \log Δ/ \log\log Δ\right)$ and optimal message size of $1$ bit per round. Among all of the previous results, the only algorithm that achieves the optimal round complexity of $O\left( \log Δ/ \log\log Δ\right)$ without prior knowledge of $α$ is due to Lenzen and Wattenhofer [DISC'10] that obtains a $O(α\log^{1+\varepsilon}Δ/ (\varepsilon\log\log Δ))$ approximation in $O(\logΔ/(\varepsilon\log\logΔ))$ rounds and $O(\log(\varepsilon^{-1}\logΔ))$ message size. Our algorithm simplifies and improves upon this result. The only downside of our algorithm compared to the algorithm of Lenzen and Wattenhofer is that it needs prior knowledge of $Δ$. The previous state-of-the-art algorithm by Dory, Ghaffari, and Ilchi [PODC'22] has a dependency on $\log n$ in the round complexity for unknown $α$, which is far from optimal.