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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
VERT: Verified Equivalent Rust Transpilation with Large L...
Aidan Z. H. Yang, Yoshiki Takashima, Brandon Paulsen, Josiah Dod · 2024-04-30 · via cs.SE updates on arXiv.org

Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and correct transpiling of existing code-bases to Rust. Existing work falls into two categories: rule-based and large language model (LLM)-based. While rule-based approaches can theoretically produce correct transpilations that maintain input-output equivalence to the original, they often yield unreadable Rust code that uses unsafe subsets of the Rust language. On the other hand, while LLM-based approaches typically produce more readable, maintainable, and safe code, they do not provide any guarantees about correctness. In this work, we present VERT, a tool that can produce readable Rust transpilations with formal guarantees of correctness. VERT's only requirement is that there is Web Assembly compiler for the source language, which is true for most major languages. VERT first uses the Web Assembly compiler to obtain an oracle Rust program. In parallel, VERT uses an LLM to generate a readable candidate Rust program. This candidate is verified against the oracle, and if verification fails, we regenerate a new candidate transpilation until verification succeeds. We evaluate VERT by transpiling a suite of 1,394 programs taken from competitive programming style benchmarks. Combining Anthropic's Claude-2 and VERT increases Rust transpilations passing property-based testing from 31% to 54% and bounded model-checking from 1% to 42% compared to using Claude alone. In addition, we evaluate VERT's ability to generate non-trivial safe Rust on programs taken from real-world C projects that make significant use of pointers. Our results provide insights into the limitations of LLMs to write safe Rust.