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LLM-assisted framework recursively evolves communication programs

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-assisted framework recursively evolves communication programs

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

    Recursive Code Evolution for Task-Oriented Semantic Communications

    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…

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