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Paper Citation Record · LEDGER

MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.12881.

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

pith.paper-citation-record.v1
2410.12881 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:43:23.065299Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T01:40:42.426614Z

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 b8f0d442-3736-4f77-ba2e-d2b9ad5a1dc7 · inbound

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling cites this paper.

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T11:43:23.065299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:43:23.065299Z digest=sha256:a2a5e7c9739541aa53ba6ad0ccc3825908ed2863e7793a1d374e7fc70af4a44a

Observation 13e3b9ac-5f4b-409c-9d2a-c78bebc28867 · inbound

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training cites this paper.

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T11:59:52.151511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:59:52.151511Z digest=sha256:9485ed1fec567a8727255314829ff957fa2a71d1691de6413587548c0e1e188d

Observation 84b22eca-2eb9-4c5e-8da1-ac167ade236e · inbound

NVIDIA Nemotron 3: Efficient and Open Intelligence cites this paper.

NVIDIA Nemotron 3: Efficient and Open Intelligence MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:40:42.430031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-18T01:40:42.190369Z digest=sha256:3de191fe11a06a6d623106a2277eb820adc1547841fcedf3bd8e4266b2709c6b

Observation c3327f60-0a45-4cd2-8bd0-45e62e9ed388 · inbound

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation cites this paper.

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:41:10.649948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T17:31:30.510866Z digest=sha256:f1c993ef16d8d857c89651d5d83ccd9d778bde688e80b6070179aee56d1a8fb6

Observation 64069db1-6fdf-4528-a30a-224b471dec8a · inbound

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation cites this paper.

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-14T19:49:46.135987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T19:49:46.135987Z digest=sha256:81ab2d0b62a7c34e06cd9801efd97f4a169db8cdd3dc109aa888857a1e948bf7