AI · 1h ago
Attention Sinks: Why LLMs Break When You Evict Token 0
Attention sinks are early tokens that absorb excess attention due to softmax normalization. Evicting them from a sliding-window KV cache causes perplexity to spike and generation to fail. The fix is to keep ~4 initial sink tokens pinned in cache alongside the rolling window.
Meridian48 take
This is a critical architectural insight for anyone building streaming or long-context LLMs, but the fix is trivial—so the real story is how many production systems are silently broken.
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