CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation

• URL: http://arxiv.org/abs/2508.21256v1
• Date: Thu, 28 Aug 2025 23:00:08 GMT
• Title: CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation
• Abstract summary: We present CrossTL, a universal programming language enabling bidirectional translation between multiple languages.<n>Our system consists of: language-specific lexers/parsers converting source code to ASTs, bidirectional CrossGL translation modules.<n>We demonstrate effectiveness through comprehensive evaluation across programming domains, achieving successful compilation and execution across all backends.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present CrossTL, a universal programming language translator enabling bidirectional translation between multiple languages through a unified intermediate representation called CrossGL. Traditional approaches require separate translators for each language pair, leading to exponential complexity growth. CrossTL uses a single universal IR to facilitate translations between CUDA, HIP, Metal, DirectX HLSL, OpenGL GLSL, Vulkan SPIR-V, Rust, and Mojo, with Slang support in development. Our system consists of: language-specific lexers/parsers converting source code to ASTs, bidirectional CrossGL translation modules implementing ToCrossGLConverter classes for importing code and CodeGen classes for target generation, and comprehensive backend implementations handling full translation pipelines. We demonstrate effectiveness through comprehensive evaluation across programming domains, achieving successful compilation and execution across all supported backends. The universal IR design enables adding new languages with minimal effort, requiring only language-specific frontend/backend components. Our contributions include: (1) a unified IR capturing semantics of multiple programming paradigms, (2) a modular architecture enabling extensibility, (3) a comprehensive framework supporting GPU compute, graphics programming, and systems languages, and (4) empirical validation demonstrating practical viability of universal code translation. CrossTL represents a significant step toward language-agnostic programming, enabling write-once, deploy-everywhere development.

Related papers

• LLM Translation of Compiler Intermediate Representation [2.614444430580024]
  This paper presents IRIS-14B, a transformer model fine-tuned to translate GIMPLE to LLVM IR.<n>To the best of our knowledge, IRIS-14B is the first model trained explicitly for IR-to-IR translation.<n>It outperforms the accuracy of widely used models, including the largest state-of-the-art open models available today, ranging from 13 to 1,000 billion parameters, by up to 44 percentage points.
  arXiv  Detail & Related papers  (2026-05-07T13:22:23Z)
• Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees [0.03994567502796063]
  We introduce GG (Guaranteed Guess), an ISA-centric transpilation pipeline that combines the translation power of pre-trained large language models with the rigor of established software testing constructs.<n>Our method generates candidate translations using an LLM from one ISA to another, and embeds such translations within a software-testing framework to build quantifiable confidence in the translation.<n>We evaluate our GG approach over two diverse datasets, enforce high code coverage (>98%) across unit tests, and achieve functional/semantic correctness of 99% on HumanEval programs and 49% on BringupBench programs.
  arXiv  Detail & Related papers  (2025-06-17T15:06:54Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to better interpret the programming domain knowledge.<n>CodeGRAG significantly improves the code generation ability of LLMs and can even offer performance gain for cross-lingual code generation.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• Forklift: An Extensible Neural Lifter [11.633770744027682]
  We propose Forklift, the first neural lifter that learns how to translate assembly to LLVM IR using a token-level encoder-decoder Transformer. We collect millions of parallel LLVM IR, x86, ARM, and RISC-V programs across compilers and optimization levels to train Forklift and set up an input/output-based accuracy harness. We evaluate Forklift on two challenging benchmark suites and translate 2.5x more x86 programs than a state-of-the-art hand-written lifter and 4.4x more x86 programs than GPT-4 as well as enabling translation from new ISAs.
  arXiv  Detail & Related papers  (2024-04-01T17:27:58Z)
• IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators [49.903001442804594]
  This work investigates the prospect of leveraging compiler intermediate representations (IR) to improve the multilingual capabilities of Code-LMs. We first compile SLTrans, a parallel dataset consisting of nearly 4M self-contained source code files. Next, we carry out continued causal language modelling training on SLTrans, forcing the Code-LMs to learn the IR language. Our resulting models, dubbed IRCoder, display sizeable and consistent gains across a wide variety of code generation tasks and metrics.
  arXiv  Detail & Related papers  (2024-03-06T17:52:08Z)
• Cross-Lingual Transfer with Target Language-Ready Task Adapters [66.5336029324059]
  BAD-X, an extension of the MAD-X framework, achieves improved transfer at the cost of MAD-X's modularity. We aim to take the best of both worlds by fine-tuning task adapters adapted to the target language.
  arXiv  Detail & Related papers  (2023-06-05T10:46:33Z)
• ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation [40.376625939658354]
  ERNIE-UniX2 is a unified cross-lingual pre-training framework for both generation and understanding tasks. ERNIE-UniX2 integrates multiple pre-training paradigms based on encoder-decoder architecture. ERNIE-UniX2 can be seamlessly fine-tuned for varieties of generation and understanding downstream tasks.
  arXiv  Detail & Related papers  (2022-11-09T13:06:58Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer [136.09386219006123]
  We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning.
  arXiv  Detail & Related papers  (2020-04-30T18:54:43Z)
• Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders [20.063065730835874]
  State-of-the-art multilingual machine translation relies on a universal encoder-decoder. We propose an alternative approach that is based on language-specific encoder-decoders.
  arXiv  Detail & Related papers  (2020-04-14T15:02:24Z)
• Bi-Decoder Augmented Network for Neural Machine Translation [108.3931242633331]
  We propose a novel Bi-Decoder Augmented Network (BiDAN) for the neural machine translation task. Since each decoder transforms the representations of the input text into its corresponding language, jointly training with two target ends can make the shared encoder has the potential to produce a language-independent semantic space.
  arXiv  Detail & Related papers  (2020-01-14T02:05:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.