Liana Patel, Rishabh Ranjan, Ion Stoica, Matei Zaharia, Carlos Guestrin · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26754 · open-access PDF
Expert human readers process large inputs (e.g., code repositories and long documents) by skimming to closely read only what matters. On the other hand, language models process all input tokens through each model layer, resulting in significant processing costs for long-context inputs. We introduce TokenGate, a small, drop-in component that extends existing trained models to enable end-to-end learnable token pruning over long-context inputs for fast and memory-efficient prefill. To enable end-to-end learning despite the non-differentiable top-K operator for token pruning, we introduce token-ga
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