AINeutralarXiv – CS AI · 10h ago6/10
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Scaling Limits of Long-Context Transformers
Researchers present a theoretical analysis of how transformer attention mechanisms scale with context length, identifying a critical threshold where attention shifts from uniform averaging to focusing on individual keys. The findings establish that this transition point depends on local geometric properties of the key distribution rather than global features, with implications for understanding transformer behavior at extreme context lengths.