Paper Citation Record · LEDGER

Byte Latent Transformer: Patches Scale Better Than Tokens

As of 20 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 44 inbound Pith citation observations for arXiv:2412.09871.

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

pith.paper-citation-record.v1
2412.09871 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:45:39.195688Z

measured 96 of 96 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:33:18.628297Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T03:47:48.461365Z

Reference resolution

52 of 52 outbound references displayed

verified exact0
verified fuzzy41
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d369c27-aa05-4d61-af2b-cfa6d1fbc0a6 · outbound

This paper cites write newline.

Byte Latent Transformer: Patches Scale Better Than Tokens write newline

Reference 1

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Observation 693a9cad-1457-40e7-a424-220f9a8df050 · outbound

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

Byte Latent Transformer: Patches Scale Better Than Tokens Character-level language modeling with deeper self-attention

Reference 2

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Observation cba0e444-0c7b-49fd-9f14-e15af1736dcf · outbound

This paper cites Program synthesis with large language models, 2021.

Byte Latent Transformer: Patches Scale Better Than Tokens Program synthesis with large language models, 2021

Reference 3

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Observation 90c30d31-d107-4ede-8a6b-85e9ee2efc52 · outbound

This paper cites Learning to rank with (a lot of) word features.

Byte Latent Transformer: Patches Scale Better Than Tokens Learning to rank with (a lot of) word features

Reference 4

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Observation cf52b182-05b7-4f9d-ad8b-e0ead61a17f0 · outbound

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

Byte Latent Transformer: Patches Scale Better Than Tokens Piqa: Reasoning about physical commonsense in natural language

Reference 5

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Observation a8fe8fa5-940d-4194-9c0c-affa3b7d8ae8 · outbound

This paper cites Transformer flops, 2023.

Byte Latent Transformer: Patches Scale Better Than Tokens Transformer flops, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.548252Z

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Observation 5b7afab3-5a82-4bb1-81d1-6e72a14c32a8 · outbound

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Byte Latent Transformer: Patches Scale Better Than Tokens Unresolved cited work

Reference 7

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Observation 9dc9a6f5-578e-466b-ac02-96b8bbde30ce · outbound

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

Byte Latent Transformer: Patches Scale Better Than Tokens Bridging the Gap for Tokenizer-Free Language Models

Reference 8

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Observation ce895d6c-f361-463f-bf89-011fb947120d · outbound

This paper cites Hierarchical multiscale recurrent neural networks.

Byte Latent Transformer: Patches Scale Better Than Tokens Hierarchical multiscale recurrent neural networks

Reference 9

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Observation 4b746304-68f1-4a60-ae44-b8c384b897fe · outbound

This paper cites Canine: Pre-training an efficient tokenization-free encoder for language representation.

Byte Latent Transformer: Patches Scale Better Than Tokens Canine: Pre-training an efficient tokenization-free encoder for language representation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.525024Z

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Observation 0cdafcf7-2570-430d-b90b-f30140e021a5 · outbound

This paper cites Think you have solved question answering? T ry ARC , the AI2 reasoning challenge.

Byte Latent Transformer: Patches Scale Better Than Tokens Think you have solved question answering? T ry ARC , the AI2 reasoning challenge

Reference 11

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Observation 14bcd589-c480-4199-b336-ab787b060119 · outbound

This paper cites Getting the most out of your tokenizer for pre-training and domain adaptation.

Byte Latent Transformer: Patches Scale Better Than Tokens Getting the most out of your tokenizer for pre-training and domain adaptation

Reference 12

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This paper cites Flash A ttention: Fast and memory-efficient exact attention with io-awareness.

Byte Latent Transformer: Patches Scale Better Than Tokens Flash A ttention: Fast and memory-efficient exact attention with io-awareness

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.499152Z

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Observation 4fe1d1f3-2faa-48e1-a66e-9903e32e0482 · outbound

This paper cites The llama 3 herd of models.

Byte Latent Transformer: Patches Scale Better Than Tokens The llama 3 herd of models

Reference 14

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Observation b8f49ea3-17dc-469a-a24c-c31278ade781 · outbound

This paper cites CUTE : Measuring llms' understanding of their tokens.

Byte Latent Transformer: Patches Scale Better Than Tokens CUTE : Measuring llms' understanding of their tokens

Reference 15

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Observation b5acf611-f4dc-492a-94e7-45bb832d4937 · outbound

This paper cites CharacterBERT : Reconciling elmo and bert for word-level open-vocabulary representations from characters.

Byte Latent Transformer: Patches Scale Better Than Tokens CharacterBERT : Reconciling elmo and bert for word-level open-vocabulary representations from characters

Reference 16

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Observation 87143f23-eccb-4df1-88fb-bde3c2bab50c · outbound

This paper cites A new algorithm for data compression.

Byte Latent Transformer: Patches Scale Better Than Tokens A new algorithm for data compression

Reference 17

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Source-reported events for the cited work

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Observation d71886da-cd17-4312-820e-719cd3150153 · outbound

This paper cites The F lores-101 evaluation benchmark for low-resource and multilingual machine translation.

Byte Latent Transformer: Patches Scale Better Than Tokens The F lores-101 evaluation benchmark for low-resource and multilingual machine translation

Reference 18

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Observation db7ffea0-0c71-4771-85c2-a2db1606698b · outbound

This paper cites Generating sequences with recurrent neural networks.

Byte Latent Transformer: Patches Scale Better Than Tokens Generating sequences with recurrent neural networks

Reference 19

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This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

Byte Latent Transformer: Patches Scale Better Than Tokens Mamba: Linear-time sequence modeling with selective state spaces

Reference 20

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Observation 72238b77-86f6-45d8-9856-b16fe44bfff0 · outbound

This paper cites Measuring massive multitask language understanding.

Byte Latent Transformer: Patches Scale Better Than Tokens Measuring massive multitask language understanding

Reference 21

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Observation 380e68d6-1f27-4146-87f7-dfdc9a458a85 · outbound

This paper cites Training compute-optimal large language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Training compute-optimal large language models

Reference 22

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Observation 5f0a86ce-4196-445b-8f88-ee07e797f445 · outbound

This paper cites Perceiver: General perception with iterative attention.

Byte Latent Transformer: Patches Scale Better Than Tokens Perceiver: General perception with iterative attention

Reference 23

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This paper cites Neural machine translation in linear time.

Byte Latent Transformer: Patches Scale Better Than Tokens Neural machine translation in linear time

Reference 24

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This paper cites Scaling laws for neural language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Scaling laws for neural language models

Reference 25

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This paper cites Byte-level machine reading across morphologically varied languages.

Byte Latent Transformer: Patches Scale Better Than Tokens Byte-level machine reading across morphologically varied languages

Reference 26

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Observation b0f522bc-7e01-4e2f-b88b-5a0b38957714 · outbound

This paper cites Character-aware neural language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Character-aware neural language models

Reference 27

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This paper cites Training llms over neurally compressed text.

Byte Latent Transformer: Patches Scale Better Than Tokens Training llms over neurally compressed text

Reference 28

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This paper cites Datacomp-lm: In search of the next generation of training sets for language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Datacomp-lm: In search of the next generation of training sets for language models

Reference 29

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This paper cites Xlm-v: Overcoming the vocabulary bottleneck in multilingual masked language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Xlm-v: Overcoming the vocabulary bottleneck in multilingual masked language models

Reference 30

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This paper cites Myte: Morphology-driven byte encoding for better and fairer multilingual language modeling.

Byte Latent Transformer: Patches Scale Better Than Tokens Myte: Morphology-driven byte encoding for better and fairer multilingual language modeling

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.379218Z

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Byte Latent Transformer: Patches Scale Better Than Tokens Decoupled weight decay regularization

Reference 32

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This paper cites Subword language modeling with neural networks.

Byte Latent Transformer: Patches Scale Better Than Tokens Subword language modeling with neural networks

Reference 33

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This paper cites Hierarchical transformers are more efficient language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Hierarchical transformers are more efficient language models

Reference 34

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This paper cites Efficient transformers with dynamic token pooling.

Byte Latent Transformer: Patches Scale Better Than Tokens Efficient transformers with dynamic token pooling

Reference 35

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This paper cites Language model tokenizers introduce unfairness between languages.

Byte Latent Transformer: Patches Scale Better Than Tokens Language model tokenizers introduce unfairness between languages

Reference 36

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This paper cites Language models are unsupervised multitask learners.

Byte Latent Transformer: Patches Scale Better Than Tokens Language models are unsupervised multitask learners

Reference 37

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This paper cites Neural machine translation of rare words with subword units.

Byte Latent Transformer: Patches Scale Better Than Tokens Neural machine translation of rare words with subword units

Reference 38

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Byte Latent Transformer: Patches Scale Better Than Tokens GLU variants improve transformer

Reference 39

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This paper cites Spacebyte: Towards deleting tokenization from large language modeling.

Byte Latent Transformer: Patches Scale Better Than Tokens Spacebyte: Towards deleting tokenization from large language modeling

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.316186Z

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This paper cites RoFormer : Enhanced transformer with rotary position embedding.

Byte Latent Transformer: Patches Scale Better Than Tokens RoFormer : Enhanced transformer with rotary position embedding

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.308897Z

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This paper cites Generating text with recurrent neural networks.

Byte Latent Transformer: Patches Scale Better Than Tokens Generating text with recurrent neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.301286Z

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This paper cites Phonologybench: Evaluating phonological skills of large language models.

Byte Latent Transformer: Patches Scale Better Than Tokens Phonologybench: Evaluating phonological skills of large language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.293961Z

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This paper cites Llama 2: Open foundation and fine-tuned chat models.

Byte Latent Transformer: Patches Scale Better Than Tokens Llama 2: Open foundation and fine-tuned chat models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.286302Z

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This paper cites Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.

Byte Latent Transformer: Patches Scale Better Than Tokens Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 45

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This paper cites Mambabyte: Token-free selective state space model.

Byte Latent Transformer: Patches Scale Better Than Tokens Mambabyte: Token-free selective state space model

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.274226Z

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This paper cites Effective long-context scaling of foundation models.

Byte Latent Transformer: Patches Scale Better Than Tokens Effective long-context scaling of foundation models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.266582Z

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Observation 5a8c4775-7903-4985-a682-18a2366f5f28 · outbound

This paper cites Byt5: Towards a token-free future with pre-trained byte-to-byte models.

Byte Latent Transformer: Patches Scale Better Than Tokens Byt5: Towards a token-free future with pre-trained byte-to-byte models

Reference 48

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This paper cites Megabyte: Predicting million-byte sequences with multiscale transformers.

Byte Latent Transformer: Patches Scale Better Than Tokens Megabyte: Predicting million-byte sequences with multiscale transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.254543Z

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Observation a695aa24-681d-451a-8939-add0cd7362bf · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? arXiv, 2019.

Byte Latent Transformer: Patches Scale Better Than Tokens Hellaswag: Can a machine really finish your sentence? arXiv, 2019

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.246521Z

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Observation fdbcdc63-6a07-4fc5-8fe5-83cbfe55d578 · outbound

This paper cites Root mean square layer normalization.

Byte Latent Transformer: Patches Scale Better Than Tokens Root mean square layer normalization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.237864Z

Source-reported events for the cited work

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Observation 7d0a80c5-90c5-4e1b-9c44-a43cfab602d5 · outbound

This paper cites Character-level convolutional networks for text classification.

Byte Latent Transformer: Patches Scale Better Than Tokens Character-level convolutional networks for text classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:45:39.229061Z

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Pith citing papers

Observation 8a56eef1-ae4b-46b4-907c-c68d04da976d · inbound

Tokenisation is NP-Complete cites this paper.

Tokenisation is NP-Complete Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4326a6ca-de80-49ce-a5ac-fd0fff6d75e1 · inbound

Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers cites this paper.

Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 72

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Unavailable: canonical work link unavailable.

Observation 126eaf8b-4455-492c-9284-6290608b28d0 · inbound

Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling cites this paper.

Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 18

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Unavailable: canonical work link unavailable.

Observation 3d88bde0-ea82-48b9-b831-7338928bc81f · inbound

LLM-based event log analysis techniques: A survey cites this paper.

LLM-based event log analysis techniques: A survey Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 63

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Observation c8b92a72-72c2-4424-bec4-17a25157877c · inbound

Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning cites this paper.

Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 34

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Unavailable: canonical work link unavailable.

Observation 5bb9f469-f237-4676-8224-e6cc4e59f5f3 · inbound

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning cites this paper.

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 25

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Observation 21f1b5c5-f89a-4c3c-a615-48294aef94ca · inbound

An Uncertainty Principle for Linear Recurrent Neural Networks cites this paper.

An Uncertainty Principle for Linear Recurrent Neural Networks Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 6

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Observation ca9a8266-a026-4ed1-be69-2768a92a6298 · inbound

ALFEE: Adaptive Large Foundation Model for EEG Representation cites this paper.

ALFEE: Adaptive Large Foundation Model for EEG Representation Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 51

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Observation 5a162554-a44c-43b1-b0a0-d211fd6668d2 · inbound

FreeMesh: Boosting Mesh Generation with Coordinates Merging cites this paper.

FreeMesh: Boosting Mesh Generation with Coordinates Merging Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 19

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Observation 4f9c47a7-419b-464e-a43d-3d6ef8f0130a · inbound

EXECUTE: A Multilingual Benchmark for LLM Token Understanding cites this paper.

EXECUTE: A Multilingual Benchmark for LLM Token Understanding Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 14

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Observation 48674664-e288-4d0f-a3a2-a70e1a776f46 · inbound

Improving Language and Modality Transfer in Translation by Character-level Modeling cites this paper.

Improving Language and Modality Transfer in Translation by Character-level Modeling Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 31

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E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models cites this paper.

E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 11

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Observation 435b1c62-d240-45d8-be4e-5163e52aa492 · inbound

Sampling from Your Language Model One Byte at a Time cites this paper.

Sampling from Your Language Model One Byte at a Time Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 51

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Observation 13953cd7-5da7-420e-bdb4-4ca577edf254 · inbound

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets cites this paper.

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 2022

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Observation 42ade3c6-115f-48e0-99a6-2e3a237063e1 · inbound

Entropy-Driven Pre-Tokenization for Byte-Pair Encoding cites this paper.

Entropy-Driven Pre-Tokenization for Byte-Pair Encoding Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 1999

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Unavailable: canonical work link unavailable.

Observation 1db18ae7-1465-44d1-bff7-ba515ca024bd · inbound

ByteSpan: Information-Driven Subword Tokenisation cites this paper.

ByteSpan: Information-Driven Subword Tokenisation Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 30

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Unavailable: canonical work link unavailable.

Observation 2a6722b8-8bcf-42d5-ba56-645dc9e209fc · inbound

Learning to Skip the Middle Layers of Transformers cites this paper.

Learning to Skip the Middle Layers of Transformers Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:55.730794Z

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Unavailable: canonical work link unavailable.

Observation baca7f72-cda5-452d-9c71-b893ffbde6cf · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 280

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:46.494957Z

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Observation 62add196-6752-4877-bae9-35fb7daa884e · inbound

Energy-Based Transformers are Scalable Learners and Thinkers cites this paper.

Energy-Based Transformers are Scalable Learners and Thinkers Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-06T20:42:36.683346Z

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Observation 8e67bcc0-2611-495d-9d96-e7291394e431 · inbound

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling cites this paper.

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:02.209101Z

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Unavailable: canonical work link unavailable.

Observation 13ab9f17-58fd-4f22-91c6-1c85f375d120 · inbound

FLEXITOKENS: Flexible Tokenization for Evolving Language Models cites this paper.

FLEXITOKENS: Flexible Tokenization for Evolving Language Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 18

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6c96cd5-26b4-46c5-a8f7-55e1c4d53548 · inbound

Synergy: End-to-end Concept Model cites this paper.

Synergy: End-to-end Concept Model Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 245cef04-c4a6-4041-b171-e7fca218e328 · inbound

SpeLLM: Character-Level Multi-Head Decoding cites this paper.

SpeLLM: Character-Level Multi-Head Decoding Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:16.943166Z

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Unavailable: canonical work link unavailable.

Observation 38465e18-0e65-420d-a0d2-dcc00bdde284 · inbound

Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music cites this paper.

Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T14:59:36.545581Z

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Unavailable: canonical work link unavailable.

Observation a868451e-b1a5-421b-aa4e-550ebee901fb · inbound

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights cites this paper.

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:21:15.070824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 800d5deb-5edf-4a87-b26e-47a6a5381726 · inbound

DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking cites this paper.

DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T15:10:05.833399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 91b7b0a1-0333-4256-9c9d-5b51041eeb5e · inbound

Lost in State Space: Probing Frozen Mamba Representations cites this paper.

Lost in State Space: Probing Frozen Mamba Representations Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:26:13.005369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 53a577bf-b816-4a10-ad63-442b74c0309b · inbound

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 Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:36:29.184901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7df2ea19-0fd7-49aa-9205-13bf95949310 · inbound

Bin Latent Transformer (BiLT): A shift-invariant autoencoder for calibration-free spectral unmixing of turbid media cites this paper.

Bin Latent Transformer (BiLT): A shift-invariant autoencoder for calibration-free spectral unmixing of turbid media Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:12:17.583602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b84543b0-8be2-479c-9859-cf5a6484eceb · inbound

Towards Understanding Self-Pretraining for Sequence Classification cites this paper.

Towards Understanding Self-Pretraining for Sequence Classification Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 181

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba6156fc-1975-4c98-86fe-078c7e3ebb5c · inbound

Kronecker Embeddings: Byte-Level Structured Token Representations for Parameter-Efficient Language Models cites this paper.

Kronecker Embeddings: Byte-Level Structured Token Representations for Parameter-Efficient Language Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:53:13.401957Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3c62183c-42a5-4164-9825-5951dd2d4e5b · inbound

Large Byte Model: Teaching Language Models About Compiled Code cites this paper.

Large Byte Model: Teaching Language Models About Compiled Code Byte Latent Transformer: Patches Scale Better Than Tokens

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Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T23:56:24.019070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation beafb45c-a498-40d1-a858-65063d412e83 · inbound

MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments cites this paper.

MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T04:26:35.422286Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9283bf06-bb4d-4426-a496-a5bb87b54656 · inbound

Beyond Perplexity: UTF-8 Validity in Byte-aware Language Models cites this paper.

Beyond Perplexity: UTF-8 Validity in Byte-aware Language Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-27T05:20:35.905563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22acbaf7-26c8-4e4d-93bd-f5e7789f4c1e · inbound

User as Engram: Internalizing Per-User Memory as Local Parametric Edits cites this paper.

User as Engram: Internalizing Per-User Memory as Local Parametric Edits Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:09:19.466162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b81a4b1-4169-4196-acf0-2eccc0eae93e · inbound

Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models cites this paper.

Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T19:25:00.475146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9cc5f5e6-a47f-4b00-8cf0-083dd64b678e · inbound

Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet cites this paper.

Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:39:34.623582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2cdd0554-5906-4ba3-933a-f63d452445c0 · inbound

EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction cites this paper.

EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-01T18:35:59.085284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13391aad-58d9-4548-a284-6bc4e487ded6 · inbound

Cybersecurity is the True Frontier for Generative AI Success or Failure cites this paper.

Cybersecurity is the True Frontier for Generative AI Success or Failure Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T09:54:35.389902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c451fb12-4662-40ad-9d8e-929f82ac05e3 · inbound

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking cites this paper.

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:28:18.347271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8755bc1c-bb44-4e9b-a4f2-7bf298987e22 · inbound

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking cites this paper.

SUNTA: Hierarchical Video Prediction with Surprise-based Chunking Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-14T16:42:04.979573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05d66f14-9838-4e25-9a40-c438a24c5c58 · 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 Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-07-11T03:47:48.485819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0799d3ef-482e-4656-ad44-e5616d0dca69 · inbound

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization cites this paper.

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T05:09:56.046733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d6c47de-2dd6-444f-a2bb-d543f28ab848 · inbound

Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustness cites this paper.

Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustness Byte Latent Transformer: Patches Scale Better Than Tokens

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T11:50:27.271301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.