Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
As of 31 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.20194.
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 2026-07-31T06:34:12.847434+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T23:54:32.621093Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T00:02:49.975961Z
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 67408328-cee1-451a-9477-0df81bc462b1 · inbound
Self-Optimizing Multi-Agent Systems for Deep Research cites this paper.
Self-Optimizing Multi-Agent Systems for Deep Research GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T18:18:06.474176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation 517b542d-bdcc-4ec7-93c5-334138941159 · inbound
Optimal Transport for LLM Reward Modeling from Noisy Preference cites this paper.
Optimal Transport for LLM Reward Modeling from Noisy Preference GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
Reference 251
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:46:07.348994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation b8ea111c-6918-4653-8877-3d35b65f6623 · inbound
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO cites this paper.
Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T00:02:49.977641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.