mlsanityData to 14 Oct 2026

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

arXiv 2608.14600 ↗First posted 1 Jul 2026cs.NI cs.LG cs.MMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 1 Jul 2026.

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3 of 4 references.

References · 3

  1. 2019Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoffcited by 211
  2. 2024Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Modelscited by 25
  3. 2024Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocationcited by 10

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