{"ID":603180,"CreatedAt":"2026-03-04T20:59:41Z","UpdatedAt":"2026-03-04T20:59:41Z","DeletedAt":null,"paper_url":"https://paperswithcode.com/paper/cephalo-multi-modal-vision-language-models","arxiv_id":"2405.19076","title":"Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design","abstract":"We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and science-focused Wikipedia data demonstrates can interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The combination of a vision encoder with an autoregressive transformer supports multimodal natural language understanding, which can be coupled with other generative methods to create an image-to-text-to-3D pipeline. To develop more capable models from smaller ones, we report both mixture-of-expert methods and model merging. We examine the models in diverse use cases that incorporate biological materials, fracture and engineering analysis, protein biophysics, and bio-inspired design based on insect behavior. Generative applications include bio-inspired designs, including pollen-inspired architected materials, as well as the synthesis of bio-inspired material microstructures from a photograph of a solar eclipse. Additional model fine-tuning with a series of molecular dynamics results demonstrate Cephalo's enhanced capabilities to accurately predict statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials.","short_abstract":"We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding.","url_abs":"https://arxiv.org/abs/2405.19076v3","url_pdf":"https://arxiv.org/pdf/2405.19076v3.pdf","authors":"[\"Markus J. Buehler\"]","published":"2024-05-29T00:00:00Z","tasks":"[\"Dataset Generation\", \"Image to text\", \"Natural Language Understanding\", \"Text to 3D\"]","methods":"[]","has_code":false,"code_links":[{"ID":434096,"CreatedAt":"2026-03-04T21:00:12Z","UpdatedAt":"2026-03-04T21:00:12Z","DeletedAt":null,"paper_id":603180,"paper_url":"https://paperswithcode.com/paper/cephalo-multi-modal-vision-language-models","paper_title":"Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design","repo_url":"https://github.com/lamm-mit/Cephalo-Phi-3-MoE","is_official":true,"mentioned_in_paper":true,"mentioned_in_github":false,"framework":"pytorch","github_stars":0},{"ID":461053,"CreatedAt":"2026-03-04T21:00:12Z","UpdatedAt":"2026-03-04T21:00:12Z","DeletedAt":null,"paper_id":603180,"paper_url":"https://paperswithcode.com/paper/cephalo-multi-modal-vision-language-models","paper_title":"Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design","repo_url":"https://huggingface.co/lamm-mit/cephalo","is_official":true,"mentioned_in_paper":false,"mentioned_in_github":false,"framework":"none","github_stars":0},{"ID":580944,"CreatedAt":"2026-03-04T21:00:12Z","UpdatedAt":"2026-03-04T21:00:12Z","DeletedAt":null,"paper_id":603180,"paper_url":"https://paperswithcode.com/paper/cephalo-multi-modal-vision-language-models","paper_title":"Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design","repo_url":"https://github.com/lamm-mit/Cephalo","is_official":true,"mentioned_in_paper":true,"mentioned_in_github":true,"framework":"none","github_stars":0}]}