FBS: Modeling Native Parallel Reading inside a Transformer

Tongxi Wang

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arXiv · 2601.21708

FBS: Modeling Native Parallel Reading inside a Transformer

Tongxi Wang

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Abstract

Large language models (LLMs) excel across many tasks, yet inference is still dominated by strictly token-by-token autoregression. Existing acceleration methods largely patch this pipeline and miss core human-reading ingredients: content-adaptive foresight, chunk-structure-aware compute allocation, and train-test consistency for preview/skimming. We propose the Fovea-Block-Skip Transformer (FBS), which injects a causal, trainable loop into Transformers via Parafovea-Attention Window (PAW), Chunk-Head (CH), and Skip-Gate (SG). Across diverse benchmarks, FBS improves the quality-efficiency trade-off without increasing parameters, and ablations show the three modules are complementary.

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Tongxi Wang. 2026-01-29. FBS: Modeling Native Parallel Reading inside a Transformer. https://arxiv.org/abs/2601.21708

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