# Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication

arXiv: https://arxiv.org/abs/2306.06403 · HTML: https://mlsanity.com/paper/2306.06403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 10 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [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)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [A mathematical theory of cooperative communication](https://mlsanity.com/paper/1910.02822/) (2019)
- [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021)
- [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019)
- [A Rate-Distortion view of human pragmatic reasoning](https://mlsanity.com/paper/2005.06641/) (2020)
- [A new communication paradigm: from bit accuracy to semantic fidelity](https://mlsanity.com/paper/2101.12649/) (2021)
- [Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language](https://mlsanity.com/paper/2204.10429/) (2022)
- [Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges](https://mlsanity.com/paper/2109.11885/) (2021)
- [Life-long Learning for Reasoning-based Semantic Communication](https://mlsanity.com/paper/2202.01952/) (2022)
- [Attention Based Communication and Control for Multi-UAV Path Planning](https://mlsanity.com/paper/2112.12584/) (2021)

## Built on by (most cited first, 3 of 3)

- [Semantic Communication meets System 2 ML: How Abstraction, Compositionality and Emergent Languages Shape Intelligence](https://mlsanity.com/paper/2505.20964/) (2025)
- [Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same](https://mlsanity.com/paper/2402.12412/) (2024)
- [Reasoning-Native Agentic Communication for 6G](https://mlsanity.com/paper/2602.17738/) (2026)

## Abstract (arXiv)

This work deals with the heterogeneous semantic-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents' context. This article proposes a novel framework for solving the inverse problem of CR in SNC using two Bayesian inference methods, namely: Bayesian inverse CR (iCR) and Bayesian inverse linearized CR (iLCR). The first proposed Bayesian iCR method utilizes Markov Chain Monte Carlo (MCMC) sampling to infer the agent's context while being computationally expensive. To address this issue, a Bayesian iLCR method is leveraged which obtains a linearized CR (LCR) model by training a linear neural network. Experimental results show that the Bayesian iLCR method requires less computation and achieves higher inference accuracy compared to Bayesian iCR. Additionally, heterogeneous SNC based on the context obtained through the Bayesian iLCR method shows better communication effectiveness than that of Bayesian iCR. Overall, this work provides valuable insights and methods to improve the effectiveness of SNC in situations where agents have different contexts.