Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1912.04136.
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 2026-08-08T11:58:17.285029Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T14:38:21.468285Z
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 75a7666c-b69d-4573-9741-5dac53f5fe9a · inbound
Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning cites this paper.
Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:58:17.285029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 94f784ff-c043-4172-8b59-4453d5003995 · inbound
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games cites this paper.
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T20:38:32.926910Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc25d49c-21eb-4831-96f8-e5b9a1915127 · inbound
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning cites this paper.
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:32:53.761065Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 537b7f25-8aff-4080-9cca-21b382d81ddc · inbound
Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients cites this paper.
Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:41:44.235997Z
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 ecf58914-12bd-4e63-8871-c8e56c99fa2c · inbound
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety cites this paper.
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Reference 110
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T14:38:21.470059Z
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.