Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content | Paper Citation Record

Observation 4afdfa54-f88a-46e0-9a15-bcbed05c5422 · outbound

This paper cites Alabi, Yanke Mao, Haonan Gao, and En-Shiun Annie Lee.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Alabi, Yanke Mao, Haonan Gao, and En-Shiun Annie Lee

Reference 1

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Observation 7ebeb8e9-7f88-4a7f-b891-d2444363f441 · outbound

This paper cites Do all languages cost the same? tokenization in the era of commercial language models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Do all languages cost the same? tokenization in the era of commercial language models

Reference 2

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Observation 690b540f-d575-476a-8e7d-c6a90a6cc88c · outbound

This paper cites Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond

Reference 3

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Observation 89d56c74-8406-4bfb-a1c7-5fb00e869092 · outbound

This paper cites MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies

Reference 4

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Observation d9a50f12-f94e-496d-a3b0-c902f0c676c2 · outbound

This paper cites Longformer: The Long-Document Transformer.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Longformer: The Long-Document Transformer

Reference 5

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Observation 1db98461-7eb8-4595-bc6f-c35a2a4aa943 · outbound

This paper cites A massively parallel corpus: the bible in 100 languages.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content A massively parallel corpus: the bible in 100 languages

Reference 6

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Observation 56cfdd58-8a93-49ce-9d91-b3cb70c42225 · outbound

This paper cites Clark, Dan Garrette, Iulia Turc, and John Wieting.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Clark, Dan Garrette, Iulia Turc, and John Wieting

Reference 7

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Observation 1c1471e1-2942-487b-a3f8-870c9090db12 · outbound

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

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 8

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Observation f84267cb-b37d-44e6-a32e-f03f760090f7 · outbound

This paper cites Multiscale byte language models -- a hierarchical architecture for causal million-length sequence modeling.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Multiscale byte language models -- a hierarchical architecture for causal million-length sequence modeling

Reference 9

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Observation af455e76-ba7b-4c17-a00b-03e5f6650175 · outbound

This paper cites O mer Veysel C a g atan, Akash Kundu, Martin Bernstorff, Shitao Xiao, Akshita Sukhlecha, Bhavish Pahwa, Rafa Po \'s wiata, Kranthi Kiran GV, Shawon Ashraf, Daniel Auras, Bj \.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content O mer Veysel C a g atan, Akash Kundu, Martin Bernstorff, Shitao Xiao, Akshita Sukhlecha, Bhavish Pahwa, Rafa Po \'s wiata, Kranthi Kiran GV, Shawon Ashraf, Daniel Auras, Bj \

Reference 10

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Observation 9b48849d-5b01-4662-8309-4b87b91a1977 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content GLM-5: from Vibe Coding to Agentic Engineering

Reference 11

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Observation 3c9eeacf-439a-4216-9443-06f56ccc44ce · outbound

This paper cites The Llama 3 Herd of Models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content The Llama 3 Herd of Models

Reference 12

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Observation 57a89f98-0c42-4bb8-affa-5c4801d5e21e · outbound

This paper cites Bitext mining using distilled sentence representations for low-resource languages.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Bitext mining using distilled sentence representations for low-resource languages

Reference 13

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Observation 66cc1f93-437d-4249-a7c2-cc3e3cfc178e · outbound

This paper cites HTML Bible Index -- Ukrainian.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content HTML Bible Index -- Ukrainian

Reference 14

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Observation 4e885ffe-ebd8-4dfa-812c-11fbb4ae657e · outbound

This paper cites Perceiver: General perception with iterative attention.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Perceiver: General perception with iterative attention

Reference 15

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Observation 51b4d924-55d4-48cf-a363-b90deaada197 · outbound

This paper cites Ticls: Tightly coupled language text spotter.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Ticls: Tightly coupled language text spotter

Reference 16

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Observation 91cc27fb-f243-4255-b5ae-5eb7e8441060 · outbound

This paper cites Lotz, Ingo Ziegler, Phillip Rust, and Desmond Elliott.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Lotz, Ingo Ziegler, Phillip Rust, and Desmond Elliott

Reference 17

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Observation 471917f3-ca0e-4399-8062-c12b98fd00ba · outbound

This paper cites Supervised contrastive learning.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Supervised contrastive learning

Reference 18

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Observation b4e2f324-9862-4c83-bdeb-5478d9520292 · outbound

This paper cites Ocr-free document understanding transformer.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Ocr-free document understanding transformer

Reference 19

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Observation ab8c803d-d344-4aa5-a89c-107a7c3ec915 · outbound

This paper cites Adam: A method for stochastic optimization.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Adam: A method for stochastic optimization

Reference 20

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Observation c89ecd3e-30f7-4298-baeb-c123b5584fa9 · outbound

This paper cites E uroparl: A parallel corpus for statistical machine translation.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content E uroparl: A parallel corpus for statistical machine translation

Reference 21

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Observation b48456d1-f3bd-4221-b18a-c26cb4e44827 · outbound

This paper cites Subword regularization: Improving neural network translation models with multiple subword candidates.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Subword regularization: Improving neural network translation models with multiple subword candidates

Reference 22

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Observation 7ba85ef0-8e0b-4cd5-bbe4-627f9a986fef · outbound

This paper cites S entence P iece: A simple and language independent subword tokenizer and detokenizer for neural text processing.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content S entence P iece: A simple and language independent subword tokenizer and detokenizer for neural text processing

Reference 23

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Observation 493f913f-00d2-4540-b3bc-2b1c2c5420a3 · outbound

This paper cites P ix2 S truct: Screenshot parsing as pretraining for visual language understanding.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content P ix2 S truct: Screenshot parsing as pretraining for visual language understanding

Reference 24

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Observation dedbb092-5bd6-4977-9bf3-ec1232f7e99f · outbound

This paper cites Trocr: transformer-based optical character recognition with pre-trained models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Trocr: transformer-based optical character recognition with pre-trained models

Reference 25

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Observation de52aea0-b594-4c14-b241-c165b657feee · outbound

This paper cites Tokenization impacts multilingual language modeling: Assessing vocabulary allocation and overlap across languages.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Tokenization impacts multilingual language modeling: Assessing vocabulary allocation and overlap across languages

Reference 26

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Observation ac2178cf-71ee-42c3-82be-3dd35c4887e3 · outbound

This paper cites Character-aware models improve visual text rendering.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Character-aware models improve visual text rendering

Reference 27

Resolution: verified exact; doi, observed 2026-08-01T21:23:49.478668Z

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Observation 1de230ae-c90c-4808-848b-05ba9dfb49e1 · outbound

This paper cites Glyph-byt5: A customized text encoder for accurate visual text rendering.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Glyph-byt5: A customized text encoder for accurate visual text rendering

Reference 28

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Observation cae33c4a-309e-4b18-8237-aa5ce9036c2f · outbound

This paper cites Decoupled weight decay regularization.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Decoupled weight decay regularization

Reference 29

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Observation 7152fbb3-9129-4a9b-971d-25084ce08c86 · outbound

This paper cites Beyond fertility: STRR as a metric for multilingual tokenization evaluation.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Beyond fertility: STRR as a metric for multilingual tokenization evaluation

Reference 30

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Observation 4fac4be3-b216-46be-81f3-7fa56dff9d7a · outbound

This paper cites Hierarchical autoregressive transformers: Combining byte- and word-level processing for robust, adaptable language models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Hierarchical autoregressive transformers: Combining byte- and word-level processing for robust, adaptable language models

Reference 31

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Observation 664c8bad-32ff-47e9-ab49-1e76f9ff9703 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 32

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Observation aefc44b6-41f4-4a9f-b909-64291cf33feb · outbound

This paper cites Byte latent transformer: Patches scale better than tokens.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Byte latent transformer: Patches scale better than tokens

Reference 33

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Observation 46aa5a51-013f-4128-b092-16f4a6a767c0 · outbound

This paper cites Language model tokenizers introduce unfairness between languages.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Language model tokenizers introduce unfairness between languages

Reference 34

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Observation 1990d8bf-9dc6-4202-9278-839befd4f4fe · outbound

This paper cites Language Models are Unsupervised Multitask Learners , 2019.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Language Models are Unsupervised Multitask Learners , 2019

Reference 35

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Observation 6e76818f-677f-4f88-aa77-f5dbdd3cfe6b · outbound

This paper cites Learning transferable visual models from natural language supervision.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Learning transferable visual models from natural language supervision

Reference 36

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Observation 4122f4c8-1c4b-47c0-93ea-8717ca75631b · outbound

This paper cites When every token counts: Optimal segmentation for low-resource language models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content When every token counts: Optimal segmentation for low-resource language models

Reference 37

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Observation fbd82c04-164b-4c27-a563-ae7bf0763a07 · outbound

This paper cites Making monolingual sentence embeddings multilingual using knowledge distillation.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Making monolingual sentence embeddings multilingual using knowledge distillation

Reference 38

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Observation 708f2e87-c438-477a-a744-7abc754436c6 · outbound

This paper cites How good is your tokenizer? on the monolingual performance of multilingual language models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content How good is your tokenizer? on the monolingual performance of multilingual language models

Reference 39

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Observation e93f06ce-3a4d-43f8-893d-0a810a01ee8b · outbound

This paper cites Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, and Desmond Elliott.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, and Desmond Elliott

Reference 40

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Observation e818e859-dcd0-4289-8205-6ec2795292d9 · outbound

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Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Robust open-vocabulary translation from visual text representations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:41.122188Z

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Observation 1365610e-2bd3-485b-a54e-c061e205b892 · outbound

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Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Neural machine translation of rare words with subword units

Reference 42

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Observation 3e0a7752-7c68-436e-908b-176d6a0ff37c · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Dropout: A simple way to prevent neural networks from overfitting

Reference 43

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Observation c9fffd0b-2f2a-4b62-9e69-cd84c0bacdff · outbound

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Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Roformer: Enhanced transformer with rotary position embedding

Reference 44

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Observation acfe7422-013c-4cc1-9c27-d9272676e2dc · outbound

This paper cites Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, and Donald Metzler.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, and Donald Metzler

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Observation 29f73f0c-4deb-4a60-9613-bf83de9ab48c · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Representation Learning with Contrastive Predictive Coding

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Observation 617299bd-5135-4124-9555-716636fe1476 · outbound

This paper cites Attention is all you need.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Attention is all you need

Reference 47

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Observation 11952a5d-fd7e-4996-987c-c84521da349f · outbound

This paper cites Error patterns in historical ocr: A comparative analysis of trocr and a vision-language model, 2026.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Error patterns in historical ocr: A comparative analysis of trocr and a vision-language model, 2026

Reference 48

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Observation 2b1bc23a-7e06-44ad-ad71-17b0b215c266 · outbound

This paper cites an unresolved cited work.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Unresolved cited work

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Observation 0f8bf97d-6fe7-460e-892e-9b1b9dbaa99f · outbound

This paper cites The Holy Bible international: Text and audio bibles.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content The Holy Bible international: Text and audio bibles

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:41.836533Z

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Observation 708009d9-68af-40ca-b455-d605ac09cc3d · outbound

This paper cites On layer normalization in the transformer architecture.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content On layer normalization in the transformer architecture

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Observation 478fbc81-65c8-4675-8d82-87983efe1de8 · outbound

This paper cites B y T 5: Towards a token-free future with pre-trained byte-to-byte models.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content B y T 5: Towards a token-free future with pre-trained byte-to-byte models

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Observation 298aecb2-20e4-4f5e-b979-36255ca1d114 · outbound

This paper cites Qwen3 Technical Report.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Qwen3 Technical Report

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Observation 53396375-1549-4551-9fa7-5bac8d3560ea · outbound

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

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Megabyte: Predicting million-byte sequences with multiscale transformers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:42.124243Z

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Observation 2c442f75-bb28-49ac-a844-8fb21ebba32d · outbound

This paper cites Big bird: Transformers for longer sequences.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Big bird: Transformers for longer sequences

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:42.199436Z

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Observation 072e5444-8eeb-43ff-90a2-3b30afa727cb · outbound

This paper cites Root mean square layer normalization.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content Root mean square layer normalization

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:42.285016Z

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Observation 5b5aa7f9-d62b-4d8d-8579-5c62997a6ba5 · outbound

This paper cites MIRACL : A multilingual retrieval dataset covering 18 diverse languages.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content MIRACL : A multilingual retrieval dataset covering 18 diverse languages

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:42.357323Z

Source-reported events for the cited work

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Observation d1798f42-01cd-43cb-938c-cf700bd193e7 · outbound

This paper cites The U nited N ations parallel corpus v1.0.

Rate-Utility Frontiers for Language Encodings: Comparing Tokens, Bytes, and Pixels Under Controlled Linguistic Content The U nited N ations parallel corpus v1.0

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T21:22:42.436125Z

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