Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:06:32.231971Z
Paper Citation Record · LEDGER
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2505.19987.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:50:39.108597Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T15:50:39.423118Z
Reference resolution
15 of 15 outbound references displayed
- verified exact0
- verified fuzzy3
- unresolved11
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b653c07b-b0d1-4062-a072-90e8ff848be9 · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:34.013112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8ff9700f-5a3f-4003-9443-db5aa158f5c4 · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:33.861351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c1910e35-4bec-4a4d-a252-9a07c1ddf3c4 · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:33.747092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e7fc6afa-41cd-4989-b25f-f296c34c1b38 · outbound
This paper cites • Non-translation Error: Translation is unassessable and unrelated to the Source.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation • Non-translation Error: Translation is unassessable and unrelated to the Source
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:06:33.589624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ea7844bd-f713-4fd4-b9ff-8e24d057cd17 · outbound
This paper cites arXiv preprint arXiv:2503.02324.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation arXiv preprint arXiv:2503.02324
Reference 5
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-07T14:06:30.899432Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e44889b3-126e-4d6b-94df-ac53829aa8b3 · outbound
This paper cites errors": [ {.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation errors": [ {
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:06:33.306526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 012e882d-6094-4e76-a795-e951cf693d27 · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:33.446649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b3b1a4d8-22f4-4665-91d7-0cfbbd418e3c · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:33.088101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 77a3e8d8-3e6c-49b9-bdc7-fb6e12bc50cf · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:32.927734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e8056c1c-79c1-45b9-ac99-08f48777721d · outbound
This paper cites an unresolved cited work.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T14:06:32.740245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d5f07ff1-92e3-4b88-b0d6-785ceb6727a9 · outbound
This paper cites level":.
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T14:06:32.572269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 81073048-1d6f-46ce-b638-aa323f39b73d · outbound
This paper cites SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:06:30.740855Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 421491c1-8e4b-43e1-9689-995e0b240b53 · outbound
This paper cites R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:06:30.617779Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb34f0ed-3049-4fe2-b618-19491a0b5f27 · outbound
This paper cites Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:06:30.809680Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40f4fdee-0c10-41dc-935d-c83c02e736f1 · outbound
This paper cites New Trends for Modern Machine Translation with Large Reasoning Models.
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation New Trends for Modern Machine Translation with Large Reasoning Models
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:06:30.666322Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation d49b0b23-8194-464d-8c30-4fa49d728cf2 · inbound
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment cites this paper.
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
Reference 62
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:50:39.433690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.