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VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
An Empirical Analysis of EOS Blockchain: Architecture, Co...
Haiyang Liu, Yingjie Mao, Xiaoqi Li · 2025-05-21 · via cs.SE updates on arXiv.org

With the rapid development of blockchain technology, various blockchain systems are exhibiting vitality and potential. As a representative of Blockchain 3.0, the EOS blockchain has been regarded as a strong competitor to Ethereum. Nevertheless, compared with Bitcoin and Ethereum, academic research and in-depth analyses of EOS remain scarce. To address this gap, this study conducts a comprehensive investigation of the EOS blockchain from five key dimensions: system architecture, decentralization, performance, smart contracts, and behavioral security. The architectural analysis focuses on six core components of the EOS system, detailing their functionalities and operational workflows. The decentralization and performance evaluations, based on data from the XBlock data-sharing platform, reveal several critical issues: low account activity, limited participation in the supernode election process, minimal variation in the set of block producers, and a substantial gap between actual throughput and the claimed million-level performance. Five types of contract vulnerabilities are identified in the smart contract dimension, and four mainstream vulnerability detection platforms are introduced and comparatively analyzed. In terms of behavioral security, four real-world attacks targeting the structural characteristics of EOS are summarized. This study contributes to the ongoing development of the EOS blockchain and provides valuable insights for enhancing the security and regulatory mechanisms of blockchain ecosystems.