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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PointLLM-R is a 3D multimodal model fine-tuned on the new 55K-sample PoCoTI CoT dataset built via VLM-based refinement and Human-in-the-Loop Prompt Optimization, achieving SOTA on generative 3D classification and captioning.
Memory-augmented RL agent with case and skill libraries plus dynamic retrieval improves success rate and geometric consistency for complex CAD model generation.
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.
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.
citing papers explorer
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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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PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought
PointLLM-R is a 3D multimodal model fine-tuned on the new 55K-sample PoCoTI CoT dataset built via VLM-based refinement and Human-in-the-Loop Prompt Optimization, achieving SOTA on generative 3D classification and captioning.
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Memory-Augmented Reinforcement Learning Agent for CAD Generation
Memory-augmented RL agent with case and skill libraries plus dynamic retrieval improves success rate and geometric consistency for complex CAD model generation.
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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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Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.