Token Prioritization in Vcc: Perplexity Analysis on PG-19 for Long Sequences

The computational burden of attention in long-context language models has motivated two largely independent lines of work: sparse attention mechanisms that reduce complexity by attending to selected tokens, and gated attention variants that improve training sta-bility while mitigating the attention sink phenomenon. We observe that these approaches address complementary weaknesses and propose Gated Sparse Attention (GSA), an architecture that realizes the benefits of both. GSA incorporates a gated lightning indexer with sigmoid activations that produce bounded, interpretable selection scores, a

Research goal: How does the token prioritization strategy in Vcc affect perplexity scores on the PG-19 benchmark compared to sparse attention patterns like those in LongNet for sequences exceeding 64K tokens?

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