LLM-assisted framework recursively evolves communication programs

Researchers have developed ComRSI, a framework that uses large language models to recursively evolve communication programs for semantic communication systems. This method allows for the iterative improvement of components like encoders, decoders, and training policies, guided by feedback and resource constraints. In experiments on CIFAR-100, ComRSI achieved a 74.3% accuracy with fewer transmission symbols compared to the initial design, and also showed improvements on fading channels and transferability to Tiny-ImageNet. Comparative studies indicated ComRSI outperforms non-recursive agents, though its advantage over agents with full textual history was not statistically conclusive. AI

IMPACT Introduces a novel method for optimizing communication systems using LLMs, potentially improving efficiency and accuracy in data transmission.

RANK_REASON Academic paper detailing a new framework for semantic communication. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• CIFAR-100
• ComRSI
• Tiny-ImageNet

• paper
• infra

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