arXiv (OAI Expanded)
open access
Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation
C-to-Rust translation remains challenging due to the substantial semantic gap between the two languages. Recent experience-enhanced LLM translators improve translation quality by learning reusable insights from prior failures and repairs. Yet learned insights do not automatically constitute reusable
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation#919485
- Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders#1063459
- ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models#1132153
- Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation#972942
- Let AI Agents Translate Networks, Not Reason About Them#405597
Record · ID 1109401
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive