AI RESEARCH
Supernodes and Halos: Loss-Critical Hubs in LLM Feed-Forward Layers
arXiv CS.LG
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ArXi:2604.23475v1 Announce Type: new We study the organization of channel-level importance in transformer feed-forward networks (FFNs). Using a Fisher-style loss proxy (LP) based on activation-gradient second moments, we show that loss sensitivity is concentrated in a small set of channels within each layer. In Llama-3.1-8B, the top 1% of channels per layer accounts for a median of 58.7% of LP mass, with a range of 33.0% to 86.1%. We call these loss-critical channels supernodes.