arXiv Machine Learning June 10, 2026 By Peter Pak, Amir Barati Farimani

Domain Adapted Large Language Models for Additive Manufacturing

Read the original on arXiv Machine Learning →

arXiv:2603. 22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.

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arXiv:2606. 30429v1 Announce Type: new Abstract: Text-to-3D systems can now synthesize a mechanical part from a single sentence, yet the result is a shape to render, not a design to edit.

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arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.

By Guangning Xu, Baoquan Zhang, Michael. K. Ng

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arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.

By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga

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Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling

SemiAdapt-Instruct: Extensible Instruction Tuning via Latent Domain-Specialised Adapters

Arko-T: A Foundation Model for Text-to-Structured 3D Generation

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Visual Instruction Tuning Aligns Modalities through Abstraction