# ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation arXiv: https://arxiv.org/abs/2604.12048 ยท HTML: https://mlsanity.com/paper/2604.12048/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 61 references that are in the map. - First posted on arXiv 13 Apr 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 27 of 27) - [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023) - [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024) - [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024) - [Towards Translating Real-World Code with LLMs: A Study of Translating to Rust](https://mlsanity.com/paper/2405.11514/) (2024) - [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026) - [AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation](https://mlsanity.com/paper/2410.24117/) (2024) - [VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learners](https://mlsanity.com/paper/2404.18852/) (2024) - [CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation](https://mlsanity.com/paper/2504.15254/) (2025) - [EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation](https://mlsanity.com/paper/2508.04295/) (2025) - [C2SaferRust: Transforming C Projects into Safer Rust with NeuroSymbolic Techniques](https://mlsanity.com/paper/2501.14257/) (2025) - [Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis](https://mlsanity.com/paper/2412.14234/) (2024) - [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025) - [Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy](https://mlsanity.com/paper/2402.03907/) (2024) - [Ownership guided C to Rust translation](https://mlsanity.com/paper/2303.10515/) (2023) - [Scalable, Validated Code Translation of Entire Projects using Large Language Models](https://mlsanity.com/paper/2412.08035/) (2024) - [To Tag, or Not to Tag: Translating C's Unions to Rust's Tagged Unions](https://mlsanity.com/paper/2408.11418/) (2024) - [Translating C To Rust: Lessons from a User Study](https://mlsanity.com/paper/2411.14174/) (2024) - [SafeTrans: LLM-assisted Transpilation from C to Rust](https://mlsanity.com/paper/2505.10708/) (2025) - [Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework](https://mlsanity.com/paper/2501.13778/) (2025) - [Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs](https://mlsanity.com/paper/2301.10943/) (2023) - [SmartC2Rust: Iterative, Feedback-Driven C-to-Rust Translation via Large Language Models for Safety and Equivalence](https://mlsanity.com/paper/2409.10506/) (2024) - [Raw Pointer Rewriting with LLMs for Translating C to Safer Rust](https://mlsanity.com/paper/2505.04852/) (2025) - [MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair](https://mlsanity.com/paper/2509.16187/) (2025) - [Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation](https://mlsanity.com/paper/2510.03879/) (2025) - [RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code](https://mlsanity.com/paper/2510.07604/) (2025) - [Lowering Barriers to CAD Adoption: A Comparative Study of Augmented Reality-Based CAD (AR-CAD) and a Traditional CAD tool](https://mlsanity.com/paper/2510.12146/) (2025) - [Cargo Sherlock: An SMT-Based Checker for Software Trust Costs](https://mlsanity.com/paper/2512.12553/) (2025) ## Built on by (most cited first, 1 of 1) - [Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation](https://mlsanity.com/paper/2609.15381/) (2026) ## Abstract (arXiv) Large-scale migration of legacy C code to Rust offers a promising path toward improving memory safety, but LLM-based C-to-Rust translation remains challenging due to limited context windows and hallucinations. Prior approaches are evaluated primarily on small programs or datasets skewed toward small codebases, providing limited insight into scalability on real-world systems. They also rely on static context construction, which breaks down in the presence of complex cross-module dependencies and often requires manual intervention. Recent coding agents offer a promising alternative through dynamic codebase navigation and context curation. When used out of the box, however, they frequently produce incomplete translations that appear superficially correct. We present ORBIT, an autonomous agentic framework for project-level C-to-Rust translation that combines dynamic context collection with dependency-guided orchestration and iterative verification. ORBIT constructs a dependency-aware translation graph, generates Rust interfaces, maps C functions to Rust counterparts, and coordinates multiple specialized agents. We evaluate ORBIT on 24 programs from CRUST-Bench, with 91.7% of the programs exceeding 1,000 lines of code. ORBIT achieves 100% compilation success and 91.7% test success in both expert-interface and automatically generated-interface settings, substantially outperforming C2Rust and CRUST-Bench, while reducing unsafe Rust code blocks to nearly zero. We further evaluate ORBIT on challenging cases from the DARPA TRACTOR benchmark, where it achieves competitive performance relative to participating systems.