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arXiv Machine Learning By Mingyu Li, Guorui Song, Jing Lin, Haoqian Wang

OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics

Read the original on arXiv Machine Learning →

OraclePhys is a fine‑tuning framework for large language models on structural mechanics, comprising a graded benchmark (OraclePhys‑Bench), a 30K supervision dataset (OraclePhys‑30K), and a controlled training study. The study shows that the form of the label’s answer, rather than its length, determines what the model learns, and that certain training objectives can produce models that match or exceed existing LLMs on spatial structural response tasks. The trained 8B model reaches the data‑precision frontier, outperforming zero‑shot and 32‑shot baselines at a specialist level.

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.

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