# Foundation Models for Generalizable Semantic and Goal-Oriented Communication

arXiv: https://arxiv.org/abs/2609.07853 · HTML: https://mlsanity.com/paper/2609.07853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission](https://mlsanity.com/paper/2404.17736/) (2024)
- [Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications](https://mlsanity.com/paper/2310.19460/) (2023)
- [Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation](https://mlsanity.com/paper/2411.04575/) (2024)
- [SQ-GAN: Semantic Image Communications Using Masked Vector Quantization](https://mlsanity.com/paper/2502.09520/) (2025)
- [SC-GIR: Goal-oriented Semantic Communication via Invariant Representation Learning](https://mlsanity.com/paper/2509.01119/) (2025)

## Abstract (arXiv)

Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.