neuralCAD-Edit benchmark shows even the best foundation model (GPT 5.2) scores 53% lower than human CAD experts in acceptance trials for multimodal-instructed 3D model edits.
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Text-to-CadQuery: A New Paradigm for CADgenerationwithscalablelargemodelcapabilities
17 Pith papers cite this work. Polarity classification is still indexing.
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P3D-Bench is a benchmark with three task families that scores MLLMs on generating executable parametric 3D programs, finding failures in precise geometry and part assembly.
UniCAD supplies a unified multi-modal benchmark and an end-to-end MLLM that performs reconstruction, generation, and QA on CAD data, reporting SOTA results on UniCAD and Fusion360.
Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
CADBench is a new multimodal benchmark for CAD program generation that combines 18k samples from DeepCAD, Fusion 360, ABC, MCB, and Objaverse across clean/noisy meshes and various renders, used to test 11 models and reveal failure modes.
ArtiCAD presents the first training-free multi-agent framework that generates articulated, editable CAD assemblies from text or images by predicting assembly relationships early and using validation with rollback.
PR-CAD unifies text-to-CAD generation and editing via progressive refinement with LLMs, a new interaction dataset, and RL-enhanced reasoning to achieve better controllability and faithfulness.
IterCAD is a multimodal agent framework using progressive SFT and geometry-aware RL for CAD tasks, with a new data pipeline, IterCAD-Bench, and CD-TR metric showing outperformance in executability and precision.
CAD generation agents are augmented with FEA feedback plus text blueprint and 21-view image signals, raising Box-IoU on S2O and Fusion360 while showing that base models produce no strict-passing FEA artifacts.
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
Pointer-CAD unifies B-Rep geometry with command sequences via pointer-based entity selection, allowing LLMs to perform complex CAD edits while cutting topological errors from quantization.
Pointer-CAD v2 decouples planning from construction in LLM-based CAD generation by using a pointer mechanism to reference continuous parameters from a design plan, paired with new hierarchical accuracy metrics.
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.
CADDesigner is an LLM agent that generates conceptual CAD models from text and sketches via requirement analysis, the ECIP paradigm, and iterative visual feedback, outperforming baselines in experiments.
Binarization of SEM inputs improves VLM-based visual program synthesis for semiconductor geometries, lifting mean Dice coefficient from 0.4393 to 0.5256 on MIIC data.
citing papers explorer
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neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing
neuralCAD-Edit benchmark shows even the best foundation model (GPT 5.2) scores 53% lower than human CAD experts in acceptance trials for multimodal-instructed 3D model edits.
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P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning
P3D-Bench is a benchmark with three task families that scores MLLMs on generating executable parametric 3D programs, finding failures in precise geometry and part assembly.
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UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
UniCAD supplies a unified multi-modal benchmark and an end-to-end MLLM that performs reconstruction, generation, and QA on CAD data, reporting SOTA results on UniCAD and Fusion360.
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Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
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CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
CADBench is a new multimodal benchmark for CAD program generation that combines 18k samples from DeepCAD, Fusion 360, ABC, MCB, and Objaverse across clean/noisy meshes and various renders, used to test 11 models and reveal failure modes.
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ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation
ArtiCAD presents the first training-free multi-agent framework that generates articulated, editable CAD assemblies from text or images by predicting assembly relationships early and using validation with rollback.
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PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models
PR-CAD unifies text-to-CAD generation and editing via progressive refinement with LLMs, a new interaction dataset, and RL-enhanced reasoning to achieve better controllability and faithfulness.
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IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing
IterCAD is a multimodal agent framework using progressive SFT and geometry-aware RL for CAD tasks, with a new data pipeline, IterCAD-Bench, and CD-TR metric showing outperformance in executability and precision.
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Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback
CAD generation agents are augmented with FEA feedback plus text blueprint and 21-view image signals, raising Box-IoU on S2O and Fusion360 while showing that base models produce no strict-passing FEA artifacts.
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Agent-Aided Design for Dynamic CAD Models
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
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COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
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Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selection
Pointer-CAD unifies B-Rep geometry with command sequences via pointer-based entity selection, allowing LLMs to perform complex CAD edits while cutting topological errors from quantization.
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Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision
Pointer-CAD v2 decouples planning from construction in LLM-based CAD generation by using a pointer mechanism to reference continuous parameters from a design plan, paired with new hierarchical accuracy metrics.
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Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.
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CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent
CADDesigner is an LLM agent that generates conceptual CAD models from text and sketches via requirement analysis, the ECIP paradigm, and iterative visual feedback, outperforming baselines in experiments.
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Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization
Binarization of SEM inputs improves VLM-based visual program synthesis for semiconductor geometries, lifting mean Dice coefficient from 0.4393 to 0.5256 on MIIC data.
- HistCAD: A Constraint-Aware Parametric History-Based CAD Representation, Dataset, and Benchmark with Industrial Complexity