Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2109.08331.
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 2026-08-07T00:25:19.383182Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T13:23:28.567236Z
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 cba6578a-fae6-4eb5-a491-611ddce32a77 · inbound
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems cites this paper.
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:56:25.931462Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfbd6c09-4f50-407e-b5e4-4571fa645ae7 · inbound
Constrained Auto-Bidding via Generative Response Modeling cites this paper.
Constrained Auto-Bidding via Generative Response Modeling Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:23:28.568641Z
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 8fc568dd-179b-4c51-a520-c756fc833e6b · inbound
Generative Optimization for Incentivized Advertising with Global Level Constraints cites this paper.
Generative Optimization for Incentivized Advertising with Global Level Constraints Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:25:19.383182Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.