# Semantics-Native Communication with Contextual Reasoning arXiv: https://arxiv.org/abs/2108.05681 ยท HTML: https://mlsanity.com/paper/2108.05681/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 44 references that are in the map. - First posted on arXiv 12 Aug 2021. ## Builds on (most cited first, 20 of 20) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013) - [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017) - [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016) - [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020) - [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020) - [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016) - [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020) - [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020) - [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019) - [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) - [Federated Knowledge Distillation](https://mlsanity.com/paper/2011.02367/) (2020) - [A mathematical theory of cooperative communication](https://mlsanity.com/paper/1910.02822/) (2019) - [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) - [Incorporating Pragmatic Reasoning Communication into Emergent Language](https://mlsanity.com/paper/2006.04109/) (2020) - [Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication](https://mlsanity.com/paper/2105.10716/) (2021) - [Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction](https://mlsanity.com/paper/2104.08109/) (2021) ## Built on by (most cited first, 22 of 22) - [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021) - [Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation](https://mlsanity.com/paper/2309.11127/) (2023) - [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021) - [Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language](https://mlsanity.com/paper/2204.10429/) (2022) - [Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework](https://mlsanity.com/paper/2111.08051/) (2021) - [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022) - [Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication](https://mlsanity.com/paper/2205.10768/) (2022) - [Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks](https://mlsanity.com/paper/2406.04853/) (2024) - [An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation](https://mlsanity.com/paper/2201.12477/) (2022) - [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022) - [Enabling the Wireless Metaverse via Semantic Multiverse Communication](https://mlsanity.com/paper/2212.06908/) (2022) - [How to Evaluate Semantic Communications for Images with ViTScore Metric?](https://mlsanity.com/paper/2309.04891/) (2023) - [Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication](https://mlsanity.com/paper/2306.06403/) (2023) - [Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication](https://mlsanity.com/paper/2310.09394/) (2023) - [Semantic-Native Communication: A Simplicial Complex Perspective](https://mlsanity.com/paper/2210.16970/) (2022) - [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) - [CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection](https://mlsanity.com/paper/2308.11066/) (2023) - [Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures](https://mlsanity.com/paper/2308.16789/) (2023) - [Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics](https://mlsanity.com/paper/2410.02303/) (2024) - [Logic-Driven Semantic Communication for Resilient Multi-Agent Systems](https://mlsanity.com/paper/2601.06733/) (2026) - [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026) ## Abstract (arXiv) Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks. In this article, inspired by human communication, we propose a novel stochastic model of System 1 semantics-native communication (SNC) for generic tasks, where a speaker has an intention of referring to an entity, extracts the semantics, and communicates its symbolic representation to a target listener. To further reach its full potential, we additionally infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the physical listener's unique way of coding its semantics, i.e., communication context. The resultant System 2 SNC allows the speaker to extract the most effective semantics for its listener. Leveraging the proposed stochastic model, we show that the reliability of System 2 SNC increases with the number of meaningful concepts, and derive the expected semantic representation (SR) bit length which quantifies the extracted effective semantics. It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.