Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.11132.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:23:26.286580Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T21:28:58.981049Z
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 fd05ab1f-b1c7-452e-a023-64c57dacae3b · inbound
Iterative Deepening Sampling as Efficient Test-Time Scaling cites this paper.
Iterative Deepening Sampling as Efficient Test-Time Scaling RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:23:26.286580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17af7e53-82ef-4714-a73d-0502b397e090 · inbound
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T22:23:15.577864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 203b735c-7071-4d86-ad12-9f6b38c0d130 · inbound
Environment-Grounded Automated Prompt Optimization for LLM Game Agents cites this paper.
Environment-Grounded Automated Prompt Optimization for LLM Game Agents RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T21:28:58.982799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.