Generative artificial intelligence for reliable mechanistic reasoning for corrosion

The paper introduces a retrieval‑augmented generation framework that combines fine‑tuned open‑weight language models with a hybrid dense‑lexical retrieval pipeline to synthesize corrosion knowledge. Applied to magnesium alloy corrosion, the system achieves significant improvements in token‑level accuracy and high faithfulness and context recall, while an additional Reason Map framework constructs evidence graphs to detect causal errors that standard factuality metrics miss. The modular design is positioned as a general blueprint for trustworthy AI‑assisted knowledge synthesis across engineering domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• rag
• fine-tuning
• safety