Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.12452.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:19:20.300033Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T21:42:10.554768Z
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 db27c727-a1dd-4329-81d8-1af4295ffdfd · 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 Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
Reference 223
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:42:10.557816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a7d88685-6bb0-4bae-8516-548b4fe424d7 · inbound
CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs cites this paper.
CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:19:20.300033Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.