Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T07:59:57.505776Z
Paper Citation Record · LEDGER
Optimizing Korean-Centric LLMs via Token Pruning
As of 5 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2604.16235.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-30T19:48:50.845119Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact8
- verified fuzzy2
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch3
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 29b5e0ff-5b5d-40f9-a893-a9bec4880b0a · outbound
This paper cites Aya 23: Open Weight Releases to Further Multilingual Progress.
Optimizing Korean-Centric LLMs via Token Pruning Aya 23: Open Weight Releases to Further Multilingual Progress
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.050160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 68032727-76d8-4bbf-8664-88245f33ad57 · outbound
This paper cites Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters.
Optimizing Korean-Centric LLMs via Token Pruning Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T08:02:25.055551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ef78b3de-265e-4198-a446-d0a844840ea0 · outbound
This paper cites InFindings of the Association for Computational Linguistics: ACL 2025, pages 12257–12284.
Optimizing Korean-Centric LLMs via Token Pruning InFindings of the Association for Computational Linguistics: ACL 2025, pages 12257–12284
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T11:55:05.259574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f5ad7615-def5-4240-a127-7a9f6a04bda4 · outbound
This paper cites Trillion 7B Technical Report.
Optimizing Korean-Centric LLMs via Token Pruning Trillion 7B Technical Report
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.042527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8d576864-54bf-4fd4-9093-b25c75e7b75c · outbound
This paper cites CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean.
Optimizing Korean-Centric LLMs via Token Pruning CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.047687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 970c4c04-7a5c-4329-82fe-061bc44cdbe5 · outbound
This paper cites Thunder-LLM: Efficiently Adapting LLMs to Korean with Minimal Resources.
Optimizing Korean-Centric LLMs via Token Pruning Thunder-LLM: Efficiently Adapting LLMs to Korean with Minimal Resources
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.062607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bdc17f27-1344-4328-98e3-7097029ba323 · outbound
This paper cites Ministral 3.
Optimizing Korean-Centric LLMs via Token Pruning Ministral 3
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-14T19:12:24.826958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 872220c4-fe84-429e-9ff2-26030f309a7f · outbound
This paper cites Understanding and Mitigating Language Confusion in LLMs.
Optimizing Korean-Centric LLMs via Token Pruning Understanding and Mitigating Language Confusion in LLMs
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.065225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0c512bff-beed-4e02-aef9-5c31fccb26b4 · outbound
This paper cites GECKO: Generative Language Model for English, Code and Korean.
Optimizing Korean-Centric LLMs via Token Pruning GECKO: Generative Language Model for English, Code and Korean
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.040120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9e1203ba-1a21-4ba7-8cbc-75e99026c963 · outbound
This paper cites an unresolved cited work.
Optimizing Korean-Centric LLMs via Token Pruning Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-05-21T11:55:05.262234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 73358076-494e-48fd-bb63-a134fec17207 · outbound
This paper cites InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7993–8007.
Optimizing Korean-Centric LLMs via Token Pruning InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7993–8007
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T11:55:05.265549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b6bfe6d2-5424-4084-8345-b286c74c9a13 · outbound
This paper cites Gemma 3 Technical Report.
Optimizing Korean-Centric LLMs via Token Pruning Gemma 3 Technical Report
Reference 12
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-10T08:02:25.057772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fda74fc0-cdde-4fbb-9063-3db4d008e91e · outbound
This paper cites RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining.
Optimizing Korean-Centric LLMs via Token Pruning RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:02:25.060215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation acc22184-b15e-44af-be4e-da282b0c0a74 · outbound
This paper cites Qwen3 Technical Report.
Optimizing Korean-Centric LLMs via Token Pruning Qwen3 Technical Report
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T08:02:25.045137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation b9ced6e2-3e6b-444a-9b63-1df2b26c968b · inbound
Language Models are not Equally Robust to Non-Canonical Tokenization across Languages cites this paper.
Language Models are not Equally Robust to Non-Canonical Tokenization across Languages Optimizing Korean-Centric LLMs via Token Pruning
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T19:48:50.845119Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.