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English(EN) Recursive Code Evolution for Task-Oriented Semantic Communications

LLM辅助框架递归演化通信程序

研究人员开发了ComRSI框架,该框架使用大型语言模型递归演化语义通信系统的通信程序。该方法允许在反馈和资源约束的指导下,对编码器、解码器和训练策略等组件进行迭代改进。在CIFAR-100上的实验中,ComRSI在传输符号少于初始设计的情况下达到了74.3%的准确率,并且在衰落信道和向Tiny-ImageNet的迁移方面也显示出改进。比较研究表明,ComRSI的表现优于非递归代理,尽管其相对于具有完整文本历史的代理的优势在统计上不确定。 AI

影响 引入了一种使用LLM优化通信系统的新颖方法,有望提高数据传输的效率和准确性。

排序理由 详细介绍语义通信新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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LLM辅助框架递归演化通信程序

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详细介绍语义通信新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yan Gong, Zheng Chu, Wei Koong Chai, Hari Mohan Pandey, Jingrui Hou, Miao Zhang, Yuanjian Li, Pei Xiao ·

    面向任务的语义通信的递归代码演化

    arXiv:2610.29641v1 Announce Type: cross Abstract: Semantic communication jointly optimizes inference accuracy, channel use, and computation, yet the underlying communication program is typically specified before training. This paper presents ComRSI (Communications-Oriented Recurs…