Spectral-Aligned Pruning for Universal Error-Correcting Code Transformers
Sanghyeon Cho, Taewoo Park, Seong-Joon Park, Dae-Young Yun, Hee-Youl Kwak, Sang-Hyo Kim, Yongjune Kim

TL;DR
This paper introduces Spectral-Aligned Pruning (SAP), a graph-spectrum-based structured pruning method for universal error-correcting code transformers that reduces complexity while maintaining decoding performance.
Contribution
SAP leverages spectral properties of code bipartite graphs to enable cross-code pruning mask reuse and combines it with low-rank adaptation for efficient code-specific recovery.
Findings
SAP achieves comparable decoding performance to dedicated pruning.
Substantial reductions in computational cost and model memory footprint.
Spectral signatures effectively guide pruning mask selection.
Abstract
Universal channel decoders based on transformers-such as the Foundation Error Correction Code Transformer (FECCT)-achieve competitive decoding performance across diverse code families with a single shared backbone, optionally followed by code-specific finetuning. However, the high computational complexity and large parameter footprint of FECCT present substantial obstacles to practical deployment. To address these challenges, we investigate structured pruning for FECCT and propose Spectral-Aligned Pruning (SAP), a structure-aware framework that enables cross-code reuse of structured pruning masks by leveraging the spectrum of the corresponding bipartite graph. SAP is grounded in classical graph analysis of codes: the two algebraically largest adjacency eigenvalues provide compact spectral proxies for degree scale, expansion ratio, and minimum-distance lower bounds. These quantities are…
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