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Paper Citation Record · LEDGER

New Trends for Modern Machine Translation with Large Reasoning Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.10351.

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

pith.paper-citation-record.v1
2503.10351 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:28:19.924280Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:06:29.736029Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e5ebc837-fca6-4ac9-85d2-29fbd76a5500 · inbound

ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning cites this paper.

ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning New Trends for Modern Machine Translation with Large Reasoning Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-15T20:28:19.924280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:28:19.924280Z digest=sha256:799d2a7203a0e29a6a297f933bfda9ffa9ef8bd42556ac5cd89352637f9cec2c

Observation 182fbcf7-8edd-428e-a1f6-a6c686a4f0d6 · inbound

TULUN: Transparent and Adaptable Low-resource Machine Translation cites this paper.

TULUN: Transparent and Adaptable Low-resource Machine Translation New Trends for Modern Machine Translation with Large Reasoning Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T14:31:55.173696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:55.173696Z digest=sha256:7443230214390d5615e83e6ab781b4f83bc7b4c773b811f1ceced09e93767e5d

Observation 40f4fdee-0c10-41dc-935d-c83c02e736f1 · inbound

How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation cites this paper.

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.

source=pdf_text observed=2026-08-07T14:06:30.666322Z digest=sha256:4261541e56c9ed9dfeba012f49aaec93aa25a82ef826d7cd1bb10b91487289f2

Observation 526ef77f-72ca-4ced-89b3-060661544c10 · inbound

TAT-R1: Terminology-Aware Translation with Reinforcement Learning and Word Alignment cites this paper.

TAT-R1: Terminology-Aware Translation with Reinforcement Learning and Word Alignment New Trends for Modern Machine Translation with Large Reasoning Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T13:43:10.232884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:43:10.232884Z digest=sha256:8c858ade930024d5c3e3bd7e4c61b5201b055504beef654a1fc7d02ed6de46c2

Observation 5074555d-9f25-4155-a67f-8b067976a12d · inbound

TransEvalnia: Reasoning-based Evaluation and Ranking of Translations cites this paper.

TransEvalnia: Reasoning-based Evaluation and Ranking of Translations New Trends for Modern Machine Translation with Large Reasoning Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T16:47:04.367359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:47:04.367359Z digest=sha256:ff80883ca7e8d7a6d157eb339af18f77ddf3ebf16b04cb56709fd5555e1e027b

Observation cee82451-7b5b-4419-affc-c0441d04d60d · inbound

ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation cites this paper.

ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation New Trends for Modern Machine Translation with Large Reasoning Models

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-10T02:48:27.400166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-10T02:45:59.473429Z digest=sha256:44e943cce9d3b95d3a8c3ea39d6e0f3b0dd5cfa4ce473e4c46f91e2ca1e82993

Observation 1eb53053-665b-455c-a6ba-204b4944512f · inbound

Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent cites this paper.

Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent New Trends for Modern Machine Translation with Large Reasoning Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-02T03:06:29.738651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T10:22:18.085996Z digest=sha256:264ac5671449bd01f849303178936775c6932c625aab6b46d2698b91f106ef27

Observation 9d819cce-bb7c-4515-8f2f-288fc2d2c7bf · inbound

LatentMT: Machine Translation with Latent Reasoning cites this paper.

LatentMT: Machine Translation with Latent Reasoning New Trends for Modern Machine Translation with Large Reasoning Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-01T14:53:43.750878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T14:53:43.750878Z digest=sha256:a1c87c954d128580efd4a0b71ff2cdf0cff034f99476b935dc8655a5580d3da4

Observation 9466be78-4f07-4f82-af5d-21f654500c52 · inbound

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation cites this paper.

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation New Trends for Modern Machine Translation with Large Reasoning Models

Reference 3

Resolution
malformed identifier
no resolver link, observed 2026-08-03T10:03:44.664106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:03:44.664106Z digest=sha256:e01ca831c8910aca60c17f64a5726a4080a837cec6b702e8fc8f94de60ac3d20