Paper Citation Record · LEDGER

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length

As of 20 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2504.15544.

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

pith.paper-citation-record.v1
2504.15544 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:27:56.792317Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy7
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42577d99-41fe-4702-b2c8-b16f3414ebbd · outbound

This paper cites an unresolved cited work.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:27:57.036909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd932fb7-3e10-473f-bde5-63cd751611c4 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.748956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad92e146-e8ca-4d5e-b6b4-6a9397114511 · outbound

This paper cites LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.753365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 573beeee-b7f1-42ef-bfe4-001a7e73f209 · outbound

This paper cites https://huggingface.co/nlp-waseda/ roberta-base-japanese.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length https://huggingface.co/nlp-waseda/ roberta-base-japanese

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:56.994475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a595018f-eb24-4ee0-85ff-c4f098c67555 · outbound

This paper cites InThe Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length InThe Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:56.979519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b436fd5-fc94-4aec-999b-de8ff7f15ea0 · outbound

This paper cites https://huggingface.co/tohoku-nlp/ bert-base-japanese-v3.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length https://huggingface.co/tohoku-nlp/ bert-base-japanese-v3

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:56.952707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee355f65-afc6-4064-8ff2-d9e9f1b8aff7 · outbound

This paper cites Ruri: Japanese General Text Embeddings.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Ruri: Japanese General Text Embeddings

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.779853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 032db511-68e3-49e3-be29-a5cf03fbfa25 · outbound

This paper cites https://huggingface.co/ku-nlp/ deberta-v3-base-japanese.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length https://huggingface.co/ku-nlp/ deberta-v3-base-japanese

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:56.938617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a297916b-f6f6-4de8-acdd-0f39d7584d50 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.788257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d94effc0-a8e0-4e46-85f0-f384c2574f22 · outbound

This paper cites B Details of Sentence Retrieval Task using MIRACL We used the Japanese subset of the MIRACL dataset (Zhang et al., 2023).

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length B Details of Sentence Retrieval Task using MIRACL We used the Japanese subset of the MIRACL dataset (Zhang et al., 2023)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:56.923795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 520c2667-c838-46db-9366-9bc62d2a8609 · outbound

This paper cites an unresolved cited work.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:27:57.050194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20c2b88d-28ed-4c97-82d7-1ddc06200bd7 · outbound

This paper cites In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:57.009268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35a43852-0f8d-4e2b-afb2-7f3bae244cbc · outbound

This paper cites Tianyu Gao, Xingcheng Yao, and Danqi Chen.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Tianyu Gao, Xingcheng Yao, and Danqi Chen

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:57.023121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72b39452-7768-4638-8d31-aa9af7f51493 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.770935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0720b29c-834b-4f38-a7f3-c4262aa1c801 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Gemma 2: Improving Open Language Models at a Practical Size

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.744436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 607b0773-ec1b-4e98-969b-10ec70144092 · outbound

This paper cites NeoBERT: A Next-Generation BERT.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length NeoBERT: A Next-Generation BERT

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:56.720839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c528ff18-e387-4229-949d-626836370daf · outbound

This paper cites an unresolved cited work.

llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length Unresolved cited work

Reference 3992

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:27:56.966340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.