arXiv AI
Jun 9
Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
arXiv:2606. 07602v1 Announce Type: cross Abstract: LLM-based LEGO assembly generation requires both semantic grounding and physical feasibility.
BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization
arXiv:2605. 26182v2 Announce Type: replace Abstract: Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability.
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arXiv AI
Jun 6
MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
arXiv Machine Learning
Jun 3
CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization
arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.
Hugging Face Trending Papers
Jul 6
ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved.
arXiv AI
Jun 6
Brick-Composer: Using MLLMs for Assembly with Diverse Bricks
arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.
arXiv AI
Jul 7
ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language
arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.
arXiv AI
Jul 28
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.
arXiv AI
Jul 1
Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling
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.
arXiv AI
Jun 2
ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models
arXiv:2502. 08884v3 Announce Type: replace-cross Abstract: We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstractions.
arXiv AI
Jul 8
DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
arXiv:2603. 19216v2 Announce Type: replace-cross Abstract: Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning.
arXiv Machine Learning
Jun 30
Arko-T: A Foundation Model for Text-to-Structured 3D Generation
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.
Hugging Face Trending Papers
6d ago
SCULPT: Subtractive Composition for 3D Part Generation
Part-aware 3D generation aims to create digital assets that are coherent as complete objects while exposing structural parts for editing, material assignment, animation, and reuse. Existing methods impose this structure outside the native generation loop: segmentation-based methods partition an already generated shape, while additive methods synthesize parts from predefined layouts, boxes, or tokens and then reconcile them into a whole.