GENESIS解决了6G RAN研发中手动迭代耗时的核心痛点，做通信系统开发或6G标准研究的团队可以直接用这个框架加速从规范到验证的全流程。
GENESIS是一个AI智能体框架，旨在解决6G无线接入网（RAN）研发中六个结构性瓶颈，包括从标准合成代码、一致性测试、现场异常处理、数据驱动优化、新波形原型设计到安全加固。传统LLM在RAN场景中会幻觉API、误读规范，且依赖仿真导致硬件迁移失败。GENESIS通过三个可组合原语（智能体、技能、钩子）和持久知识层SYNAPSE，将意图（如规范条款、遥测异常）转化为经过空中实验验证的解决方案，并回馈到知识库。该框架使能力随运行次数累积，显著压缩R&D周期。
GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities. Although Large Language Models (LLMs) have compressed comparable R&D work in general software engineering from days to minutes, their known pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake, and they heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware. To address these challenges, we present GENESIS, an agentic Artificial Intelligence (AI) framework that converts intents (e.g., a specification clause, a telemetry anomaly, or a research hypothesis) into solutions validated with over-the-air experiments, fed back into a persistent knowledge base. GENESIS is built on three composable primitives (agents, skills, hooks) and a knowledge layer (SYNAPSE) that doubles as the source of ground truth and the recipient of every artifact the framework produces, making capabilities compound across runs.