The Context-Ready Transformer
A new recurrent neural network architecture that pre-contextualizes tokens using a transformer block and correction network, achieving significant speedups over standard transformers while maintaining or improving performance.
[2606.27538] The Context-Ready Transformer
[Submitted on 25 Jun 2026]
Title:The Context-Ready Transformer
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Abstract:We introduce the context-ready transformer, a new recurrent neural network architecture built from a D-layer transformer block that pre-contextualizes each token before it enters the block. During left-to-right generation, a correction network combines the previous position's block output -- a cached summary of past context -- with the current token embedding, so the tokenenters the block already contextualized rather than as a raw embedding. At sequential inference, the correction chain makes the architecture a recurrent neural network. For training, we unroll the correction process K times over the full sequence, processing all positions in parallel at each step. A pretrained transformer can also be converted to a context-ready model by adding a zero-initialized correction FFN and fine-tuning. We evaluate across widths, depths, block sizes, and two datasets, with all comparisons against standard transformers, variants, and ablations. A D=5 model beats a 12-layer transformer while generating 1.7x faster on an A100. With K=10, a single-layermodel (D=1) beats a 6-layer transformer with a 2.6x inference speedup, and sequential inference matches parallel K=10 to within 0.01 PPL. The architecture benefits most from wide representations and long contexts. On a pointer-chasing task, D=1 trained with BPTT solves all 10 composition levels, while standard transformers exhibit staircase-like depth dependence.
Comments: NeurIPS, 22 pages
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
MSC classes: 68T07
ACM classes: I.2.6; I.5.1
Cite as: arXiv:2606.27538 [cs.CL]
(or arXiv:2606.27538v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.27538
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mahesh Godavarti [view email] [v1] Thu, 25 Jun 2026 20:39:26 UTC (31 KB)
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