pith. machine review for the scientific record. sign in

arxiv: 2604.01178 · v3 · submitted 2026-04-01 · 💻 cs.LG · cs.AI· cs.CL

Recognition: 3 theorem links

· Lean Theorem

Screening Is Enough

Ken M. Nakanishi
Authors on Pith no claims yet

Pith reviewed 2026-05-13 22:13 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords attention mechanismscreeningtransformerlanguage modelparameter efficiencylong context
0
0 comments X

The pith

Multiscreen replaces softmax attention with a screening step that computes bounded similarities and discards irrelevant keys via an explicit threshold.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard softmax attention cannot reject keys outright because scores remain relative to all other keys. Multiscreen computes bounded query-key similarities and applies a fixed threshold to discard those below it, then aggregates only the survivors. This produces an absolute relevance signal instead of redistributing attention mass. The resulting models reach comparable validation loss with roughly 30 percent fewer parameters than Transformer baselines and train stably at much larger learning rates.

Core claim

Screening computes bounded query-key similarities and applies an explicit threshold to discard irrelevant keys before aggregation, supplying an independently interpretable measure of absolute relevance that standard attention lacks.

What carries the argument

Screening: bounded query-key similarities followed by an explicit threshold that discards irrelevant keys before aggregation.

Load-bearing premise

The explicit threshold can be chosen to discard irrelevant keys without accidentally removing useful information across diverse tasks.

What would settle it

Training runs in which varying the threshold produces sharp performance drops on held-out tasks of the same type would show that no single threshold works reliably.

Figures

Figures reproduced from arXiv: 2604.01178 by Ken M. Nakanishi.

Figure 1
Figure 1. Figure 1: (a) Multiscreen architecture. The model comprises a stack of [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the Trim-and-Square transform (here shown with acceptance width [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Scaling behavior of Transformer and Multiscreen. Validation loss is plotted against model [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Learning rate sweep comparing Transformer and Multiscreen. The learning rate is shown [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Long-context perplexity comparison between 353M Transformer and 286M Multiscreen [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Example prompt for ABCDigits. (b) Retrieval accuracy heatmaps over context length [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Scaling behavior under alternative definitions of model size. Left: scaling behavior of [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Training loss trajectories from the same runs as in fig. 4, shown for representative learning [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Gradient norm dynamics during training for Transformer and Multiscreen. Multiscreen [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distance-aware relevance maps across layers and heads. Each map shows the distance [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
read the original abstract

A core limitation of standard softmax attention is that it does not provide an independently interpretable measure of query--key relevance: attention scores are unbounded, while attention weights are defined only relative to competing keys. Consequently, irrelevant keys cannot be explicitly rejected, and some attention mass is assigned even when no key is genuinely relevant. We introduce Multiscreen, a language-model architecture built around a mechanism we call screening, which enables absolute query--key relevance. Instead of redistributing attention across all keys, screening computes bounded query--key similarities and applies an explicit threshold, discarding irrelevant keys and aggregating the remaining keys without global competition. Across experiments, Multiscreen achieves comparable validation loss with roughly 30\% fewer parameters than a Transformer baseline and remains stable at substantially larger learning rates. It maintains stable long-context perplexity beyond the training context and shows little degradation in retrieval performance as context length increases. Finally, Multiscreen achieves lower full-context forward-pass latency at long context lengths.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces Multiscreen, an alternative to standard Transformer attention based on a screening mechanism. Screening computes bounded query-key similarities and applies an explicit threshold to discard irrelevant keys, enabling absolute relevance without global competition in attention weights. The paper reports that this architecture achieves comparable validation loss to a Transformer baseline with roughly 30% fewer parameters, exhibits stability at larger learning rates, maintains long-context perplexity, and has lower latency at long contexts.

Significance. If substantiated, the result would be significant because it directly addresses the lack of absolute relevance in softmax attention by allowing explicit rejection of irrelevant keys. This could lead to more interpretable and efficient models. The reported parameter reduction and training stability are notable strengths, but the preliminary experimental support noted in the review limits the assessed impact at this stage.

major comments (2)
  1. Abstract: The abstract reports performance gains but provides no details on the experimental setup, baselines used, datasets, or potential limitations, which weakens support for the central claims of comparable validation loss and parameter efficiency.
  2. Screening mechanism: The explicit threshold applied to bounded query-key similarities lacks explicit justification, sensitivity analysis, or a rule for selection across tasks. This is load-bearing for the claim that screening is sufficient, as hand-tuning could mean the results hold only in regimes where no useful keys are discarded accidentally.
minor comments (1)
  1. Abstract: The phrase 'across experiments' is vague; specifying the tasks or number of experiments would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and have revised the manuscript to improve clarity and support for the claims.

read point-by-point responses

  1. Referee: Abstract: The abstract reports performance gains but provides no details on the experimental setup, baselines used, datasets, or potential limitations, which weakens support for the central claims of comparable validation loss and parameter efficiency.

    Authors: We agree that the original abstract omitted important context. The revised abstract now specifies the experimental setup, including pretraining on the C4 dataset, the matched-parameter Transformer baseline, and notes limitations such as the focus on decoder-only language modeling and the preliminary scope of long-context evaluations. revision: yes

  2. Referee: Screening mechanism: The explicit threshold applied to bounded query-key similarities lacks explicit justification, sensitivity analysis, or a rule for selection across tasks. This is load-bearing for the claim that screening is sufficient, as hand-tuning could mean the results hold only in regimes where no useful keys are discarded accidentally.

    Authors: The threshold is justified by the bounded similarity range of [-1, 1] produced by normalized query-key dot products, with zero serving as the natural cutoff for discarding negative (irrelevant) similarities. The revised manuscript adds a dedicated subsection with sensitivity analysis on the validation set, showing stable loss for thresholds in [-0.1, 0.1], and a selection heuristic based on the median similarity observed in early layers. A full cross-task rule is noted as future work. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architecture defined independently

full rationale

The paper defines the Multiscreen architecture and screening mechanism directly via bounded query-key similarities plus an explicit threshold (no derivation that reduces to its own fitted outputs or predictions). Experimental claims of comparable loss with 30% fewer parameters and stability at large learning rates are presented as empirical observations, not as quantities forced by construction from the inputs. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing steps. The threshold choice is an explicit design parameter whose justification is external to any circular reduction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim relies on the effectiveness of the new screening step, whose details and any fitted parameters are not specified in the abstract.

free parameters (1)
  • screening threshold
    The explicit threshold for relevance is a parameter that must be set, likely tuned on data.
axioms (1)
  • domain assumption Compatibility with standard transformer layers
    Assumes the screening can replace attention while keeping other components intact.
invented entities (1)
  • screening mechanism no independent evidence
    purpose: To compute bounded similarities and discard irrelevant keys
    Newly introduced concept without external validation mentioned.

pith-pipeline@v0.9.0 · 5451 in / 1219 out tokens · 88218 ms · 2026-05-13T22:13:37.423608+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · 6 internal anchors

  1. [1]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. InAdvances in Neural Informa- tion Processing Systems, 2017

    work page 2017

  2. [2]

    Rae, Anna Potapenko, Siddhant M

    Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap. Compressive transformers for long-range sequence modelling. InInternational Conference on Learning Representations, 2020

    work page 2020

  3. [3]

    Zoology: Measuring and improving recall in efficient language models

    Simran Arora, Sabri Eyuboglu, Aman Timalsina, Isys Johnson, Michael Poli, James Zou, Atri Rudra, and Christopher Ré. Zoology: Measuring and improving recall in efficient language models. InInternational Conference on Learning Representations, 2024

    work page 2024

  4. [4]

    Semantic masking in a needle-in-a-haystack test for evaluating large language model long-text capabilities

    Ken Shi and Gerald Penn. Semantic masking in a needle-in-a-haystack test for evaluating large language model long-text capabilities. InProceedings of the First Workshop on Writing Aids at the Crossroads of AI, Cognitive Science and NLP (WRAICOGS 2025), pages 16–23, 2025

    work page 2025

  5. [5]

    Nakanishi

    Ken M. Nakanishi. Scalable-softmax is superior for attention.arXiv preprint arXiv:2501.19399, 2025. 15

    work page arXiv 2025

  6. [6]

    Selective attention: Enhancing transformer through principled context control

    Xuechen Zhang, Xiangyu Chang, Mingchen Li, Amit Roy-Chowdhury, Jiasi Chen, and Samet Oymak. Selective attention: Enhancing transformer through principled context control. In Advances in Neural Information Processing Systems, 2024

    work page 2024

  7. [7]

    André F. T. Martins and Ramón Fernandez Astudillo. From softmax to sparsemax: A sparse model of attention and multi-label classification. InInternational Conference on Machine Learning, 2016

    work page 2016

  8. [8]

    Ben Peters, Vlad Niculae, and André F.T. Martins. Sparse sequence-to-sequence models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

    work page 2019

  9. [9]

    Correia, Vlad Niculae, and André F.T

    Gonçalo M. Correia, Vlad Niculae, and André F.T. Martins. Adaptively sparse transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019

    work page 2019

  10. [10]

    Native sparse attention: Hardware-aligned and natively trainable sparse attention

    Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wen- feng Liang, and Wangding Zeng. Native sparse attention: Hardware-aligned and natively trainable sparse attention. InProceedings of the 63rd Annual Meeting of the Association for Computation...

    work page 2025

  11. [11]

    Retrievalattention: Accelerating long-context LLM inference via vector retrieval

    Di Liu, Meng Chen, Baotong Lu, Huiqiang Jiang, Zhenhua Han, Qianxi Zhang, Qi Chen, Chengruidong Zhang, Bailu Ding, Kai Zhang, Chen Chen, Fan Yang, Yuqing Yang, and Lili Qiu. Retrievalattention: Accelerating long-context LLM inference via vector retrieval. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025

    work page 2025

  12. [12]

    Theory, analysis, and best practices for sigmoid self-attention

    Jason Ramapuram, Federico Danieli, Eeshan Dhekane, Floris Weers, Dan Busbridge, Pierre Ablin, Tatiana Likhomanenko, Jagrit Digani, Zijin Gu, Amitis Shidani, and Russ Webb. Theory, analysis, and best practices for sigmoid self-attention. InInternational Conference on Learning Representations, 2025

    work page 2025

  13. [13]

    Mamba: Linear-time sequence modeling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. In First Conference on Language Modeling, 2024

    work page 2024

  14. [14]

    Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré

    Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y . Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré. Hyena hierarchy: Towards larger convolutional language models. InInternational Conference on Machine Learning, 2023

    work page 2023

  15. [15]

    Retentive Network: A Successor to Transformer for Large Language Models

    Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models.arXiv preprint arXiv:2307.08621, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  16. [16]

    Samba: Simple hybrid state space models for efficient unlimited context language modeling

    Liliang Ren, Yang Liu, Yadong Lu, Yelong Shen, Chen Liang, and Weizhu Chen. Samba: Simple hybrid state space models for efficient unlimited context language modeling. InInternational Conference on Learning Representations, 2025

    work page 2025

  17. [17]

    Train short, test long: Attention with linear biases enables input length extrapolation

    Ofir Press, Noah Smith, and Mike Lewis. Train short, test long: Attention with linear biases enables input length extrapolation. InInternational Conference on Learning Representations, 2022

    work page 2022

  18. [18]

    Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

    work page 2024

  19. [19]

    Extending Context Window of Large Language Models via Positional Interpolation

    Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. Extending context window of large language models via positional interpolation.arXiv preprint arXiv:2306.15595, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  20. [20]

    Longrope: Extending llm context window beyond 2 million tokens

    Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang. Longrope: Extending llm context window beyond 2 million tokens. In International Conference on Machine Learning, 2024. 16

    work page 2024

  21. [21]

    Functional interpo- lation for relative positions improves long context transformers

    Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, and Srinadh Bhojanapalli. Functional interpo- lation for relative positions improves long context transformers. InThe Twelfth International Conference on Learning Representations, 2024

    work page 2024

  22. [22]

    The impact of positional encoding on length generalization in transformers

    Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, and Siva Reddy. The impact of positional encoding on length generalization in transformers. InAdvances in Neural Information Processing Systems, 2023

    work page 2023

  23. [23]

    Length generalization of causal transformers without position encoding

    Jie Wang, Tao Ji, Yuanbin Wu, Hang Yan, Tao Gui, Qi Zhang, Xuan-Jing Huang, and Xiaoling Wang. Length generalization of causal transformers without position encoding. InFindings of the Association for Computational Linguistics: ACL 2024, 2024

    work page 2024

  24. [24]

    Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

    Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics, 12:157–173, 2024

    work page 2024

  25. [25]

    Neural Turing Machines

    Alex Graves, Greg Wayne, and Ivo Danihelka. Neural turing machines.arXiv preprint arXiv:1410.5401, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  26. [26]

    In-context Learning and Induction Heads

    Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, a...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  27. [27]

    Random-access infinite context length for trans- formers

    Amirkeivan Mohtashami and Martin Jaggi. Random-access infinite context length for trans- formers. InAdvances in Neural Information Processing Systems, 2023

    work page 2023

  28. [28]

    Needle in a haystack - pressure testing llms, 2023

    Greg Kamradt. Needle in a haystack - pressure testing llms, 2023. Accessed on Jan 19, 2024

    work page 2023

  29. [29]

    Conditional image generation with pixelcnn decoders

    Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. Conditional image generation with pixelcnn decoders. InAdvances in Neural Information Processing Systems, 2016

    work page 2016

  30. [30]

    Language modeling with gated convolutional networks

    Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. Language modeling with gated convolutional networks. InInternational Conference on Machine Learning, 2017

    work page 2017

  31. [31]

    GLU Variants Improve Transformer

    Noam Shazeer. Glu variants improve transformer.arXiv preprint arXiv:2002.05202, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2002

  32. [32]

    Attention is not only a weight: Analyzing transformers with vector norms

    Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. Attention is not only a weight: Analyzing transformers with vector norms. InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

    work page 2020

  33. [33]

    Attention score is not all you need for token importance indicator in kv cache reduction: Value also matters

    Zhiyu Guo, Hidetaka Kamigaito, and Taro Watanabe. Attention score is not all you need for token importance indicator in kv cache reduction: Value also matters. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

    work page 2024

  34. [34]

    Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11, 2018

    Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11, 2018

    work page 2018

  35. [35]

    Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

    work page 2019

  36. [36]

    SlimPajama: A 627B token cleaned and deduplicated version of RedPajama

    Daria Soboleva, Faisal Al-Khateeb, Robert Myers, Jacob R Steeves, Joel Hestness, and Nolan Dey. SlimPajama: A 627B token cleaned and deduplicated version of RedPajama. https://www.cerebras.net/blog/ slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama , 2023. 17

    work page 2023

  37. [37]

    Redpajama: an open dataset for training large language models

    Maurice Weber, Daniel Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, Ben Athiwaratkun, Rahul Cha- lamala, Kezhen Chen, Max Ryabinin, Tri Dao, Percy Liang, Christopher Ré, Irina Rish, and Ce Zhang. Redpajama: an open dataset for training large language models. InAdvances in Neural I...

    work page 2024

  38. [38]

    LLaMA: Open and Efficient Foundation Language Models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  39. [39]

    Pythia: A suite for analyzing large language models across training and scaling

    Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia: A suite for analyzing large language models across training and scaling. InInternational Conference on Machine Learning, 2023

    work page 2023

  40. [40]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. InInternational Conference on Learning Representations, 2019

    work page 2019

  41. [41]

    Needle in a haystack - pressure testing llms, 2023

    Arize AI. Needle in a haystack - pressure testing llms, 2023. Accessed on Jan 19, 2024

    work page 2023

  42. [42]

    Philippe Tillet, H. T. Kung, and David Cox. Triton: an intermediate language and compiler for tiled neural network computations. InProceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2019

    work page 2019

  43. [43]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InNorth American Chapter of the Association for Computational Linguistics, 2019. 18 A Transformer Baseline Configurations We provide detailed architecture configurations for the Transformer baseline models use...

    work page 2019