Paper Citation Record · LEDGER

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models

As of 12 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2501.10322.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.10322 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:16:20.792657Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T03:42:21.307552Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-11T03:47:48.559145Z

Reference resolution

51 of 51 outbound references displayed

verified exact3
verified fuzzy14
unresolved32
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3de18d85-dfa7-4811-aa6a-c90d5856cd2a · outbound

This paper cites write newline.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models write newline

Reference 1

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Observation cbeb6181-a3c8-489b-9edd-16cc8ea47f3f · outbound

This paper cites C har2 S ubword: Extending the subword embedding space using robust character compositionality.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models C har2 S ubword: Extending the subword embedding space using robust character compositionality

Reference 2

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Observation 933d638a-7d56-4add-b6e5-84d415df53d7 · outbound

This paper cites Character-level language modeling with deeper self-attention.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Character-level language modeling with deeper self-attention

Reference 3

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Unresolved cited work

Reference 4

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This paper cites Semantic parsing on F reebase from question-answer pairs.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Semantic parsing on F reebase from question-answer pairs

Reference 5

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Observation 1220c9c1-7765-4959-b42b-3d103c8aa7e6 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Piqa: Reasoning about physical commonsense in natural language

Reference 6

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Observation 414fed55-78ec-4436-92e8-2150c3d30aa3 · outbound

This paper cites Occiglot fineweb v0.5, 2024.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Occiglot fineweb v0.5, 2024

Reference 7

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Observation c0102fa2-af1d-4228-8664-4be39b1f8aa5 · outbound

This paper cites Bridging the Gap for Tokenizer-Free Language Models.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Bridging the Gap for Tokenizer-Free Language Models

Reference 8

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This paper cites B ool Q : Exploring the surprising difficulty of natural yes/no questions.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models B ool Q : Exploring the surprising difficulty of natural yes/no questions

Reference 9

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Observation e54a72f7-d208-4f53-ba61-ca3ff6cbd064 · outbound

This paper cites Bowman, Holger Schwenk, and Veselin Stoyanov.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Bowman, Holger Schwenk, and Veselin Stoyanov

Reference 11

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Observation d9904d11-5bd0-4444-b023-d8eae768ce5a · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher R \' e.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Fu, Stefano Ermon, Atri Rudra, and Christopher R \' e

Reference 12

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Observation 8a168cc1-d65a-4b9d-aa61-d38ce01d6f32 · outbound

This paper cites T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings

Reference 13

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This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models BERT: pre-training of deep bidirectional transformers for language understanding

Reference 14

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This paper cites The Llama 3 Herd of Models.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models The Llama 3 Herd of Models

Reference 15

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Observation 1b555442-5b81-4784-aa2a-f7b709d46e53 · outbound

This paper cites C haracter BERT : Reconciling ELM o and BERT for word-level open-vocabulary representations from characters.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models C haracter BERT : Reconciling ELM o and BERT for word-level open-vocabulary representations from characters

Reference 16

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Observation 73b683fb-60de-4cce-92ee-48bb9f33c1c8 · outbound

This paper cites A new algorithm for data compression.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models A new algorithm for data compression

Reference 17

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This paper cites A framework for few-shot language model evaluation, 07 2024.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models A framework for few-shot language model evaluation, 07 2024

Reference 18

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This paper cites Better & faster large language models via multi-token prediction.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Better & faster large language models via multi-token prediction

Reference 19

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This paper cites Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean - Baptiste Alayrac, Jo \ a o Carreira, and Jesse H.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean - Baptiste Alayrac, Jo \ a o Carreira, and Jesse H

Reference 20

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This paper cites Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

Reference 21

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Measuring massive multitask language understanding

Reference 22

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models T rivia QA : A large scale distantly supervised challenge dataset for reading comprehension

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models DataComp-LM: In search of the next generation of training sets for language models

Reference 24

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Hashimoto

Reference 25

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Truthfulqa: Measuring how models mimic human falsehoods

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Decoupled weight decay regularization

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models C har BERT : Character-aware pre-trained language model

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

Reference 29

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 30

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Mmmlu, 2024

Reference 31

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models The LAMBADA dataset: Word prediction requiring a broad discourse context

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Openwebmath: An open dataset of high-quality mathematical web text, 2023

Reference 33

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 34

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Language model tokenizers introduce unfairness between languages

Reference 35

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models W i C : the word-in-context dataset for evaluating context-sensitive meaning representations

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Germanbenchmark, 2024

Reference 37

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Efficiently scaling transformer inference

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Winogrande: an adversarial winograd schema challenge at scale

Reference 39

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Neural machine translation of rare words with subword units

Reference 40

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models SpaceByte: Towards Deleting Tokenization from Large Language Modeling

Reference 41

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models From characters to words: Hierarchical pre-trained language model for open-vocabulary language understanding

Reference 42

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Tran, Sebastian Ruder, Jai Prakash Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, and Donald Metzler

Reference 43

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Observation e6ffb928-35e1-4086-96e2-56637c7195a6 · outbound

This paper cites Learn your tokens: Word-pooled tokenization for language modeling.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Learn your tokens: Word-pooled tokenization for language modeling

Reference 44

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 45

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Skywork: A more open bilingual foundation model, 2023

Reference 46

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models B y T 5: Towards a token-free future with pre-trained byte-to-byte models

Reference 47

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models MEGABYTE: predicting million-byte sequences with multiscale transformers

Reference 48

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This paper cites Hellaswag: Can a machine really finish your sentence? In Anna Korhonen, David R.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Hellaswag: Can a machine really finish your sentence? In Anna Korhonen, David R

Reference 49

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models @esa (Ref

Reference 50

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Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Unresolved cited work

Reference 51

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 52

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Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models cites this paper.

Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models

Reference 69

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Observation c8705c67-22f6-4b94-8b48-f2e04376b670 · inbound

Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES cites this paper.

Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-11T03:47:48.584286Z

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