Paper Citation Record · LEDGER

In-Place Tokenizer Expansion for Pre-trained LLMs

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2607.15232.

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

pith.paper-citation-record.v1
2607.15232 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:50:17.987906Z

measured 43 of 43 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

43 of 43 outbound references displayed

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Outbound references

Observation cf5b3712-064f-4da3-af85-7839bd8ff29d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

In-Place Tokenizer Expansion for Pre-trained LLMs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:14.205257Z

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Observation 388f9141-6904-4258-aa13-eb0a526551ad · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

In-Place Tokenizer Expansion for Pre-trained LLMs Training Verifiers to Solve Math Word Problems

Reference 4

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Observation d04aa6d4-4a75-48c0-8333-d70e8c144124 · outbound

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

In-Place Tokenizer Expansion for Pre-trained LLMs Getting the most out of your tokenizer for pre-training and domain adaptation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:14.734727Z

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Observation a9ac9973-c0d4-46de-a51f-ccbd3f3e8d61 · outbound

This paper cites Token distillation: Attention-aware input em- beddings for new tokens.arXiv preprint arXiv:2505.20133,.

In-Place Tokenizer Expansion for Pre-trained LLMs Token distillation: Attention-aware input em- beddings for new tokens.arXiv preprint arXiv:2505.20133,

Reference 7

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Observation 1b8d0d90-5de9-4746-96ec-4f0e3608f8da · outbound

This paper cites Sailor: Open Language Models for South-East Asia.

In-Place Tokenizer Expansion for Pre-trained LLMs Sailor: Open Language Models for South-East Asia

Reference 9

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Observation 04464007-c7da-4791-9430-4eb37113238b · outbound

This paper cites Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities.

In-Place Tokenizer Expansion for Pre-trained LLMs Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.109835Z

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Observation 0900b006-84b7-4846-a024-4497586a2d37 · outbound

This paper cites Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit.

In-Place Tokenizer Expansion for Pre-trained LLMs Training-Free Tokenizer Transplantation via Orthogonal Matching Pursuit

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.197912Z

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Observation 1948917c-3699-49d6-85c4-5440c92a8391 · outbound

This paper cites The Llama 3 Herd of Models.

In-Place Tokenizer Expansion for Pre-trained LLMs The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.276274Z

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Observation 938266b8-643d-4ffd-92d1-7230a5756e4d · outbound

This paper cites ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model.

In-Place Tokenizer Expansion for Pre-trained LLMs ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.357730Z

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Observation cab3315e-dfe5-43a9-830d-37610feac956 · outbound

This paper cites Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following.

In-Place Tokenizer Expansion for Pre-trained LLMs Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following

Reference 14

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Observation 94c09525-8a6c-4248-b807-1a8bab64d848 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

In-Place Tokenizer Expansion for Pre-trained LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.518068Z

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Observation 3293b783-0028-4a41-b754-8b7453a8ef18 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

In-Place Tokenizer Expansion for Pre-trained LLMs LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.611343Z

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Observation fd963db7-8803-49aa-b137-ec61ab57d8d8 · outbound

This paper cites Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.

In-Place Tokenizer Expansion for Pre-trained LLMs Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.667569Z

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Observation c3e2d6db-0ba2-426c-8812-52262f7417e0 · outbound

This paper cites TokAlign: Efficient Vocabulary Adaptation via Token Alignment.

In-Place Tokenizer Expansion for Pre-trained LLMs TokAlign: Efficient Vocabulary Adaptation via Token Alignment

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:15.766936Z

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Observation 106f1fc8-3e33-4744-a3a2-939966ea043e · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.

In-Place Tokenizer Expansion for Pre-trained LLMs doi: 10.18653/v1/2024.acl-long

Reference 19

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Observation 61ab653b-ccb1-46df-93d9-de795a0531f5 · outbound

This paper cites Let's Verify Step by Step.

In-Place Tokenizer Expansion for Pre-trained LLMs Let's Verify Step by Step

Reference 20

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Observation a553e361-61d8-44dc-b874-88787803b766 · outbound

This paper cites LFM2 technical report.arXiv preprint arXiv:2511.23404,.

In-Place Tokenizer Expansion for Pre-trained LLMs LFM2 technical report.arXiv preprint arXiv:2511.23404,

Reference 21

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Observation 5acf4e6d-2eaa-4133-858a-412334be0a1d · outbound

This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

In-Place Tokenizer Expansion for Pre-trained LLMs Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 22

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Observation 1b05f5d2-02e8-4df7-9b44-3ff484999259 · outbound

This paper cites Nandini Mundra, Aditya Nanda Kishore Khandavally, Raj Dabre, Ratish Puduppully, Anoop Kunchukut- tan, and Mitesh M.

In-Place Tokenizer Expansion for Pre-trained LLMs Nandini Mundra, Aditya Nanda Kishore Khandavally, Raj Dabre, Ratish Puduppully, Anoop Kunchukut- tan, and Mitesh M

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.200437Z

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Observation 892cae98-8bbc-446a-9d92-c75d44097134 · outbound

This paper cites An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models.

In-Place Tokenizer Expansion for Pre-trained LLMs An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.259225Z

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Observation cd508f2c-7658-47dd-b894-9c75674811c2 · outbound

This paper cites AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation.

In-Place Tokenizer Expansion for Pre-trained LLMs AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.366515Z

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Observation 91b7392c-684f-4381-84d1-860805078905 · outbound

This paper cites Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning.

In-Place Tokenizer Expansion for Pre-trained LLMs Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.551996Z

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Observation a12afaeb-34ce-473a-91f9-9ad1b79e3171 · outbound

This paper cites FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language.

In-Place Tokenizer Expansion for Pre-trained LLMs FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.706422Z

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Observation 9508b108-dc2a-4c07-9d6d-88c92ac8f8c9 · outbound

This paper cites Yamshchikov, and Mark Fishel.

In-Place Tokenizer Expansion for Pre-trained LLMs Yamshchikov, and Mark Fishel

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:16.828605Z

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Observation 1b58d0ec-ee68-4219-8ae6-7c166a46e79f · outbound

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In-Place Tokenizer Expansion for Pre-trained LLMs Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP

Reference 30

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In-Place Tokenizer Expansion for Pre-trained LLMs Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

Reference 32

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Observation b73e10c2-9ce7-4601-b604-06344521055a · outbound

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In-Place Tokenizer Expansion for Pre-trained LLMs Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model

Reference 33

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This paper cites On-Device Language Models: A Comprehensive Review.

In-Place Tokenizer Expansion for Pre-trained LLMs On-Device Language Models: A Comprehensive Review

Reference 34

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In-Place Tokenizer Expansion for Pre-trained LLMs An empirical study on cross-lingual vocab- ulary adaptation for efficient language model inference

Reference 35

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In-Place Tokenizer Expansion for Pre-trained LLMs An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference

Reference 36

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In-Place Tokenizer Expansion for Pre-trained LLMs arXiv:2406.11477

Reference 37

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In-Place Tokenizer Expansion for Pre-trained LLMs Instruction-Following Evaluation for Large Language Models

Reference 38

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In-Place Tokenizer Expansion for Pre-trained LLMs •Math (3 benchmarks).GSM8K (Cobbe et al., 2021), MATH500 (the 500-problem test subset of MATH (Hendrycks et al.,

Reference 39

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In-Place Tokenizer Expansion for Pre-trained LLMs (2024)), and GSM-Plus (Li et al., 2024)

Reference 40

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In-Place Tokenizer Expansion for Pre-trained LLMs •Multilingual (2 benchmarks).MMMLU (OpenAI,

Reference 41

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In-Place Tokenizer Expansion for Pre-trained LLMs Unresolved cited work

Reference 42

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In-Place Tokenizer Expansion for Pre-trained LLMs Zero-shot

Reference 43

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This paper cites Achieving Tokenizer Flexibility in Language Models through Heuristic Adaptation and Supertoken Learning.

In-Place Tokenizer Expansion for Pre-trained LLMs Achieving Tokenizer Flexibility in Language Models through Heuristic Adaptation and Supertoken Learning

Reference 2016

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In-Place Tokenizer Expansion for Pre-trained LLMs Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca

Reference 2021

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In-Place Tokenizer Expansion for Pre-trained LLMs Tower: An Open Multilingual Large Language Model for Translation-Related Tasks

Reference 2023

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

In-Place Tokenizer Expansion for Pre-trained LLMs Do all languages cost the same? Tokenization in the era of commercial language models

Reference 2024

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In-Place Tokenizer Expansion for Pre-trained LLMs Sailor: Open language models for South-East Asia

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T23:50:14.948905Z

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Observation cda33b7d-680d-4834-b29b-413bed8bdb87 · outbound

This paper cites doi: 10.18653/v1/2026.findings-eacl.341.

In-Place Tokenizer Expansion for Pre-trained LLMs doi: 10.18653/v1/2026.findings-eacl.341

Reference 2026

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Pith citing papers

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