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cs.SE updates on arXiv.org

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
Inconsistencies in TeX-Produced Documents
Jovyn Tan, Manuel Rigger · 2024-07-22 · via cs.SE updates on arXiv.org

TeX is a widely-used typesetting system adopted by most publishers and professional societies. While TeX is responsible for generating a significant number of documents, irregularities in the TeX ecosystem may produce inconsistent documents. These inconsistencies may occur across different TeX engines or different versions of TeX distributions, resulting in failures to adhere to formatting specifications, or the same document rendering differently for different authors. In this work, we investigate and quantify the robustness of the TeX ecosystem through a large-scale study of 432 documents. We developed an automated pipeline to evaluate the cross-engine and cross-version compatibility of the TeX ecosystem. We found significant inconsistencies in the outputs of different TeX engines: only 0.2% of documents compiled to identical output with XeTeX and PDFTeX due to a lack of cross-engine support in popular LaTeX packages and classes used in academic conferences. A smaller$\unicode{x2014}$yet significant$\unicode{x2014}$extent of inconsistencies was found across different TeX Live distributions, with only 42.1% of documents producing the same output from 2020 to 2023. Our automated pipeline additionally reduces the human effort in bug-finding: from a sample of 10 unique root causes of inconsistencies, we identified two new bugs in LaTeX packages and five existing bugs that were fixed independently of this study. We also observed potentially unintended inconsistencies across different TeX Live distributions beyond the updates listed in changelogs. We expect that this study will help authors of TeX documents to avoid unexpected outcomes by understanding how they may be affected by the often undocumented subtleties of the TeX ecosystem, while benefiting developers by demonstrating how different implementations result in unintended inconsistencies.