用抽象、组合与自生语言构建能推理协作的智能系统
Semantic Communication meets System 2 ML: How Abstraction, Compositionality and Emergent Languages Shape Intelligence
- 基于系统2认知，让智能体从感官数据中学习世界模型
- 通过概念组合实现跨任务能力迁移与灵活应变
- 支持智能体自创适应性语言，适合多智能体协同研究
6G与AI的发展正迎来关键交汇。当前6G构想仍停留在5G的渐进式演进，而AI进展受限于脆弱且依赖大量数据的模型，缺乏稳健推理能力。本文提出根本性范式转变：超越纯技术通信，构建具备语义理解与目标导向交互能力的系统。核心理念基于系统2认知，依托三大支柱——抽象（使智能体从原始传感运动数据中学习有意义的世界模型）、组合性（提供概念与子系统组合的代数工具）和涌现通信（允许智能体创建自适应、具身的语言）。三者融合为真正具备推理、适应与协作能力的智能系统奠定基础，统一无线通信、机器学习与机器人学的前沿进展。
原文摘要 · Abstract (English)
The trajectories of 6G and AI are set for a creative collision. However, current visions for 6G remain largely incremental evolutions of 5G, while progress in AI is hampered by brittle, data-hungry models that lack robust reasoning capabilities. This paper argues for a foundational paradigm shift, moving beyond the purely technical level of communication toward systems capable of semantic understanding and effective, goal-oriented interaction. We propose a unified research vision rooted in the principles of System-2 cognition, built upon three pillars: Abstraction, enabling agents to learn meaningful world models from raw sensorimotor data; Compositionality, providing the algebraic tools to combine learned concepts and subsystems; and Emergent Communication, allowing intelligent agents to create their own adaptive and grounded languages. By integrating these principles, we lay the groundwork for truly intelligent systems that can reason, adapt, and collaborate, unifying advances in wireless communications, machine learning, and robotics under a single coherent framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。