# Semantic Communication with Hopfield Memories

arXiv: https://arxiv.org/abs/2511.10302 · HTML: https://mlsanity.com/paper/2511.10302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications](https://mlsanity.com/paper/2210.12040/) (2022)
- [Vector Quantized Semantic Communication System](https://mlsanity.com/paper/2209.11519/) (2022)
- [Semantic Communication with Memory](https://mlsanity.com/paper/2303.12335/) (2023)
- [Knowledge Base Enabled Semantic Communication: A Generative Perspective](https://mlsanity.com/paper/2311.12443/) (2023)
- [Semantic Communication Enhanced by Knowledge Graph Representation Learning](https://mlsanity.com/paper/2407.19338/) (2024)
- [VQ-DeepVSC: A Dual-Stage Vector Quantization Framework for Video Semantic Communication](https://mlsanity.com/paper/2409.03393/) (2024)

## Abstract (arXiv)

Traditional joint source-channel coding employs static learned semantic representations that cannot dynamically adapt to evolving source distributions. Shared semantic memories between transmitter and receiver can potentially enable bandwidth savings by reusing previously transmitted concepts as context to reconstruct data, but require effective mechanisms to determine when current content is similar enough to stored patterns. However, existing hard quantization approaches based on variational autoencoders are limited by frequent memory updates even under small changes in data dynamics, which leads to inefficient usage of bandwidth.To address this challenge, in this paper, a memory-augmented semantic communication framework is proposed where both transmitter and receiver maintain a shared memory of semantic concepts using modern Hopfield networks (MHNs). The proposed framework employs soft attention-based retrieval that smoothly adjusts stored semantic prototype weights as data evolves that enables stable matching decisions under gradual data dynamics. A joint optimization of encoder, decoder, and memory retrieval mechanism is performed with the objective of maximizing a reasoning capacity metric that quantifies semantic efficiency as the product of memory reuse rate and compression ratio. Theoretical analysis establishes the fundamental rate-distortion-reuse tradeoff and proves that soft retrieval reduces unnecessary transmissions compared to hard quantization under bounded semantic drift. Extensive simulations over diverse video scenarios demonstrate that the proposed MHN-based approach achieves substantial bit reductions around 14% on average and up to 70% in scenarios with gradual content changes compared to baseline.