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

arXiv: https://arxiv.org/abs/2608.14600 · HTML: https://mlsanity.com/paper/2608.14600/

## 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.

## Builds on (most cited first, 3 of 3)

- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2403.17256/) (2024)
- [Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation](https://mlsanity.com/paper/2411.04575/) (2024)

## 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.