mlsanityData to 14 Oct 2026

The State-Prediction Separation Hypothesis

arXiv 2607.01218 ↗First posted 1 Jul 2026cs.CL cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions. We formulate the state-prediction separation hypothesis: disentangling the two roles yields better language modeling performance. We design a Transformer variant that uses two computation streams to separate the two functions, and conduct pretraining experiments across various scales. Our experiments show that state-prediction separation consistently offers better data and compute efficiencies, improving validation loss and outperforming standard Transformers by 2--3 percentage points on average on downstream tasks. We also conduct extensive empirical analysis that rules out potential confounders and demonstrates the fundamental difference in the gradients our design entails.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 40 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

34 of 40 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  4. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  6. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  7. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  8. 2016Pointer Sentinel Mixture Modelscited by 3,096
  9. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  10. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  11. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  12. 2020GLU Variants Improve Transformercited by 1,804
22 more
  1. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  2. 2019Root Mean Square Layer Normalizationcited by 1,480
  3. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
  4. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
  5. 2016The LAMBADA dataset: Word prediction requiring a broad discourse contextcited by 992
  6. 2017Crowdsourcing Multiple Choice Science Questionscited by 765
  7. 2024Better & Faster Large Language Models via Multi-token Predictioncited by 360
  8. 2021Efficient Attentions for Long Document Summarizationcited by 345
  9. 2018Blockwise Parallel Decoding for Deep Autoregressive Modelscited by 323
  10. 2023Think before you speak: Training Language Models With Pause Tokenscited by 264
  11. 2022Will we run out of data? Limits of LLM scaling based on human-generated datacited by 195
  12. 2024Let's Think Dot by Dot: Hidden Computation in Transformer Language Modelscited by 191
  13. 2024Flex Attention: A Programming Model for Generating Optimized Attention Kernelscited by 185
  14. 2024Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durationscited by 170
  15. 2024The pitfalls of next-token predictioncited by 160
  16. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 121
  17. 2023PaSS: Parallel Speculative Samplingcited by 49
  18. 2024Do language models plan ahead for future tokens?cited by 49
  19. 2025Next-Latent Prediction Transformers Learn Compact World Modelscited by 31
  20. 2025Multi-Token Prediction Needs Registerscited by 16
  21. 2024The Belief State Transformercited by 12
  22. 2025Efficient Joint Prediction of Multiple Future Tokenscited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Almost Free State Prediction Separationcited by 1
  2. 2026Decoupling Token Roles in Autoregressive Pretrainingcited by 0
  3. 2026The Surprising Effectiveness of Shared Memory in Looped Transformerscited by 0