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

N
Netflix TechBlog - Medium
博客园 - 三生石上(FineUI控件)
Martin Fowler
Martin Fowler
博客园 - 【当耐特】
雷峰网
雷峰网
宝玉的分享
宝玉的分享
IT之家
IT之家
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
博客园 - 叶小钗
V
Visual Studio Blog
Engineering at Meta
Engineering at Meta
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
月光博客
月光博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
美团技术团队
爱范儿
爱范儿
T
The Blog of Author Tim Ferriss
L
LangChain Blog
U
Unit 42
有赞技术团队
有赞技术团队
博客园_首页

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
RATIO: Redundancy-Controlled Stochastic Routing for Relia...
[Submitted on 16 Jun 2026] · 2026-06-17 · via cs updates on arXiv.org

View PDF HTML (experimental)

Abstract:Reliable, low-latency multi-hop data delivery in vehicular networks is increasingly demanded, yet remains challenging due to frequent route failures caused by high mobility and intermittent blockage. While redundancy-based routing enhances robustness by forwarding packets over multiple paths, over-replication intensifies contention and introduces additional delay, highlighting the need to carefully managing redundancy--reliability trade-off. However, conventional deterministic multi-path replication typically duplicates packets to an integer number of branches, making the redundancy level hard to tune and adapt to time-varying network dynamics in vehicular networks. To this end, Redundancy-Controlled Stochastic (RATIO) routing is proposed in this paper. For each active flow, RATIO constructs a weighted reduced directed acyclic graph (DAG) as the routing structure, where edge weights specify per-link forwarding probabilities. At fork nodes, the aggregate outgoing forwarding probability is allowed to exceed one and a modulo-based stochastic forwarding rule is employed to guarantee feasible forwarding, thereby enabling continuously controllable redundancy. An idealized RATIO design is formulated as a load-minimizing optimization subject to per-flow timely-reliability and link-capacity constraints, but the problem is generally intractable under time-varying wireless dynamics. Accordingly, a practical heuristic, termed H-RATIO, is developed. H-RATIO constructs a compact reduced DAG by taking the union of candidate paths and optimizes forwarding probabilities via local scoring and replication-adjustment iterations. Extensive trace-driven SUMO/ns-3 co-simulations demonstrate that RATIO/H-RATIO consistently achieves the highest timely PDR compared to baselines, while providing substantially better delivery efficiency, especially under high-load scenarios.

Submission history

From: Lei Lei [view email]
[v1] Tue, 16 Jun 2026 03:48:12 UTC (1,117 KB)