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To enhance its utility, Vinci incorporates a memory module for processing long video streams in real time while retaining contextual history, a generation module for producing visual action demonstrations, and a retrieval module that bridges egocentric and third-person perspectives to provide relevant how-to videos for skill acquisition. Unlike existing systems that often depend on specialized hardware, Vinci is hardware-agnostic, supporting deployment across a wide range of devices, including smartphones and wearable cameras. In our experiments, we first demonstrate the superior performance of EgoVideo-VL on multiple public benchmarks, showcasing its vision-language reasoning and contextual understanding capabilities. We then conduct a series of user studies to evaluate the real-world effectiveness of Vinci, highlighting its adaptability and usability in diverse scenarios. We hope Vinci can establish a new framework for portable, real-time egocentric AI systems, empowering users with contextual and actionable insights. Including the frontend, backend, and models, all code of Vinci is publicly available at https:\/\/github.com\/OpenGVLab\/vinci.<\/jats:p>","DOI":"10.1145\/3749513","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:15:45Z","timestamp":1756919745000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Vinci: A Real-time Smart Assistant Based on Egocentric Vision-language Model for Portable Devices"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8067-6227","authenticated-orcid":false,"given":"Yifei","family":"Huang","sequence":"first","affiliation":[{"name":"The University of Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8208-2067","authenticated-orcid":false,"given":"Jilan","family":"Xu","sequence":"additional","affiliation":[{"name":"Fudan University, 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