Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition

[2608.14600] Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14600 (cs) [Submitted on 1 Jul 2026] Title: Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition Authors: Xinkai Liu , Mahdi Boloursaz Mashhadi , Yi Ma , Rahim Tafazolli View a PDF of the paper titled Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition, by Xinkai Liu and 3 other authors View PDF HTML (experimental) Abstract: We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition. At the transmitter, DNN-based segmentation extracts a semantic map from the source video, decomposing it into multiple sub-signal classes based on multi-user receiver intents. The transmitter broadcasts the semantic map to all users over shared wireless/network resources, thereby utilizing orthogonal resources only to transmit the sub-signal classes intended for each user. Users partially reconstruct and partially synthesize the signal by combining the received intended classes with non-intended classes locally synthesized by a generative model from the semantic map. We derive the rate-distortion/perception curves for reconstruction/synthesis with the generative model, to adaptively set compression rates for the semantic map and intended classes. Generative multicasting significantly reduces the wireless/network resources required for existing/emerging multimedia multicasting applications. The system is real-time on a Google Coral Edge TPU with 4 TOPS (int8). This is the first demonstration of generative multicasting representing a substantial advancement in on-device generative SemCom. Subjects: Networking and Internet Architecture (cs.NI) ; Machine Learning (cs.LG); Multimedia (cs.MM) Cite as: arXiv:2608.14600 [cs.NI] (or arXiv:2608.14600v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14600 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xinkai Liu [ view email ] [v1] Wed, 1 Jul 2026 12:49:16 UTC (1,269 KB) Full-text links: Access Paper: View a PDF of the paper titled Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition, by Xinkai Liu and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• EventMe: Location-Based Event Content Distribution through Human Centric Device-to-Device Communications#648514
• GaussAnything: Semantic Intent-Driven Refinement of Evolving Gaussian Scenes for Standalone VR#1083353
• Administratively Scoped IP Multicast#137047
• LOCAL: Enabling Learning On-device Contiguously for Agent LLMs#615220
• LOCAL: Enabling Learning On-device Contiguously for Agent LLMs#614253