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提出 StruPO，一种结构感知的提示编辑器，利用校准场景图作为共享表示，将结构反馈转化为可复用的编辑策略训练信号，在保持场景忠实性的同时提升文生图的美学质量。
RelateAnything is a lightweight, real-time open-vocabulary relation prediction model that accepts arbitrary predicate vocabularies and region sources, trained on a large geometrically verified dataset and evaluated on new cross-dataset benchmarks, showing significant performance gains over comparable methods.
Presents Thea, a harness for embodied agents that orchestrates robot capabilities as callable tools, introducing Scene Graph as Context and Evaluation as Exit Codes to close the loop between agent and physical world.
GraphThink is a framework that integrates task graphs and scene graphs to enhance LLM-based planning for long-horizon embodied tasks, achieving state-of-the-art results on the ALFRED benchmark and improving generalization and closed-loop replanning.
This paper introduces Agentic RAG-VLM, a unified framework that integrates retrieval-augmented generation with vision-language models and self-reflective planning for generalizable robotic grasping in cluttered environments, achieving 78.3% success rate.
Physics Question Scene Graph (PQSG) is a hierarchical question-based pipeline using VLMs to evaluate video generation models' physical plausibility with fine-grained violation detection. It introduces the FinePhyEval dataset and shows higher correlation with human judgments than prior work.
PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from natural language by combining LLM-based scene understanding with a deterministic constraint solver and a VLM-based verify loop, outperforming existing models on a benchmark of physics problems.
This paper introduces HSG (Hyperbolic Scene Graph), a scene graph model that leverages hyperbolic geometry for representing hierarchical scene structures. It is hosted on Hugging Face and referenced via arXiv:2604.17454.