REDDIT · REDDIT// 8d agoRESEARCH PAPER

Multiscreen architecture replaces softmax with absolute relevance screening

Multiscreen introduces "screening," a mechanism that evaluates query-key relevance against an absolute threshold instead of relative mass redistribution. This enables 3.2x faster inference and 40% parameter reduction while maintaining strong performance in long-context retrieval and perplexity.

// ANALYSIS

Softmax's relative weight distribution has been the fundamental bottleneck for sparsity and long-context scaling; Multiscreen's thresholding finally solves the "always attending" problem.

–40% fewer parameters with comparable validation loss to traditional Transformers.
–3.2x inference speedup at 100k context length via sparse computation skipping.
–Enables stable training at significantly higher learning rates (up to 0.0625).
–Spartan attention: models learn to attend only to relevant keys, often screening out 95% of the context.

// TAGS

multiscreenllmresearchinferenceopen-source

DISCOVERED

8d ago

2026-04-03

PUBLISHED

8d ago

2026-04-03

RELEVANCE

9/ 10

AUTHOR

Thrumpwart