Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.16849.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:21:08.255765Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T21:42:10.897720Z
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 eb61f1dc-8ae3-46a6-8d7e-9793e55ffc54 · inbound
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 162
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:42:10.900414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation af82a7c5-9336-4f8c-a046-38fcc0f9ade4 · inbound
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models cites this paper.
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:21:08.255765Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.