# GLCR

Electromagnetic signal processing has long trained a separate expert per task, limiting cross-task reuse, while electromagnetic foundation models pay for large-scale pretraining and shared-backbone inference. GLCR instead routes over a frozen heterogeneous expert pool: a lightweight one-dimensional CNN with an additive-margin head makes the global dispatch, and a local re-evaluation with four physical statistics handles confusable task pairs. On the HeteroSignal-7 benchmark it reaches 96.16% routing accuracy and a 76.30% system score, above all 19 baselines, at 1.43 ms routing latency per sample.

Electromagnetic signal processing has long trained a separate expert per task, limiting cross-task reuse, while electromagnetic foundation models pay for large-scale pretraining and shared-backbone inference. GLCR instead routes over a frozen heterogeneous expert pool: a lightweight one-dimensional CNN with an additive-margin head makes the global dispatch, and a local re-evaluation with four physical statistics handles confusable task pairs. On the HeteroSignal-7 benchmark it reaches 96.16% routing accuracy and a 76.30% system score, above all 19 baselines, at 1.43 ms routing latency per sample.