Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.14365.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:47.623400Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T04:32:32.891475Z
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 80e66f49-39ce-4d1d-82d9-3d72e932817a · inbound
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs cites this paper.
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T23:58:29.256685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5e894bf6-28e3-4bf2-985b-5a2217935ecb · inbound
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning cites this paper.
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Reference 266
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:32:32.894550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4aa692bd-fc69-43a2-88a0-5f4741231aa3 · inbound
A Survey on Large Language Models for Mathematical Reasoning cites this paper.
A Survey on Large Language Models for Mathematical Reasoning JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Reference 116
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:14:47.623400Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.