Tootfinder

3Dify: a Framework for Procedural 3D-CG Generation Assisted by LLMs Using MCP and RAG
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Satoshi Ohshima, Takahiro Katagiri
https://arxiv.org/abs/2510.04536

3Dify: a Framework for Procedural 3D-CG Generation Assisted by LLMs Using MCP and RAG
This paper proposes "3Dify," a procedural 3D computer graphics (3D-CG) generation framework utilizing Large Language Models (LLMs). The framework enables users to generate 3D-CG content solely through natural language instructions. 3Dify is built upon Dify, an open-source platform for AI application development, and incorporates several state-of-the-art LLM-related technologies such as the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG). For 3D-CG generation support, 3Dify…

A graphical diagnostic of topological order using ZX calculus
Sergi Mas-Mendoza, Richard D. P. East, Michele Filippone, Adolfo G. Grushin
https://arxiv.org/abs/2509.12355 https:…

A graphical diagnostic of topological order using ZX calculus
Establishing a universal diagnostic of topological order remains an open theoretical challenge. In particular, diagnosing long-range entanglement through the entropic area law suffers from spurious contributions, failing to unambiguously identify topological order. Here we devise a protocol based on the ZX calculus, a graphical tensor network, to determine the topological order of a state circumventing entropy calculations. The protocol takes as input real-space bipartitions of a state and retu…