Rotary embeddings create bandwidth-dependent attention decay during graph linearization; GaLA corrects this at inference time to boost performance on text-attributed graphs.
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Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning
Rotary embeddings create bandwidth-dependent attention decay during graph linearization; GaLA corrects this at inference time to boost performance on text-attributed graphs.