Bi'an Du, Fanpeng Meng, Jiemin Fang, Chen Yang, Pufan Li, Daizong Liu, Qi Tian, Wei Hu
Generating interactive 3D assets from a single image requires realistic appearance, fidelity to the depicted object, and plausible physical behavior. Yet detailed geometry alone does not determine how an object should move or deform. We introduce PhysAg3D, which couples 3D generative models with agentic reasoning and tool use. A vision-language agent first infers semantic parts, candidate interactions, and physical priors from the image; a 3D generator then produces a textured whole-object mesh from the same image. A hierarchical interaction representation links these revisable priors to mechanisms and the generated surfaces they control, organizing interaction construction and refinement. Guided by this representation and observations of the mesh, the agent uses geometric and physical tools to construct parts, joints, deformable regions, and surface bindings while retaining useful generated geometry and materials. Rendered interactions and execution measurements guide targeted revisions within a fixed budget. The framework accommodates both rigid articulation and compliant deformation. Quantitative and qualitative evaluations on real photographs and external benchmarks demonstrate detailed, image-aligned assets with plausible interactions. Ablations support bounded refinement and source-surface preservation. Project Page: supra-3d.github.io/PhysAg3D.
Ziji Sheng, Guiyao Tie, Weidong Wang, Pan Zhou Huazhong University of Science and Technology, Daizong Liu Wuhan University
Existing adaptive learning systems struggle to simultaneously achieve deep personalization, dynamic adaptability, and content trustworthiness, particularly in logically rigorous STEM fields where Large Language Models (LLMs) are prone to “hallucination”. This paper introduces LearnerCoMPASS (Cognitive Multi-model Planning Adaptive System), an integrated, end-to-end framework for adaptive learning. At its core, the framework features a novel multi-model path planning algorithm that orchestrates and fuses the outputs of heterogeneous LLM experts to generate and optimize learning sequences. To enable deep personalization, we design a dynamic cognitive diagnosis module that employs an innovative encoder-decoder architecture to generate precise, multi-dimensional cognitive state vectors for learners. To ensure trustworthiness, the system leverages an adaptively constructed dynamic knowledge graph and a Graph-RAG mechanism to provide factual anchors and logical constraints for LLM reasoning, thereby mitigating hallucinations. Extensive experiments demonstrate that LearnerCoMPASS significantly outperforms state-of-the-art baselines in generating high-quality personalized learning paths. Furthermore, ablation studies validate the critical contributions of our dynamic cognitive diagnosis and multi-model planning components.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表