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arXiv Machine Learning By 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

Read the original on arXiv Machine Learning →

The paper introduces ComRSI, a framework that recursively refines a semantic communication program using large‑language‑model‑assisted code edits. By iteratively modifying the encoder, channel adapters, decoder, loss, and training policy, the method achieves significant performance gains—e.g., 74.3% test accuracy on CIFAR‑100 over an AWGN channel with only 32 symbols per image, compared to 67.2% with 256 symbols in the baseline. Experiments across ten generations and repeated searches demonstrate that recursive optimization outperforms non‑recursive agents, though the advantage over a history‑equipped agent remains statistically inconclusive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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