Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.00950.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:28:39.539780Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T09:36:02.442358Z
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 737e4a8a-d6fb-4180-a105-30d9a9554b55 · inbound
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact cites this paper.
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:28:39.539780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64b26be6-c7b2-4530-92d9-6be1157fd037 · inbound
A Modularized Framework for Piecewise-Stationary Restless Bandits cites this paper.
A Modularized Framework for Piecewise-Stationary Restless Bandits Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:36:02.462858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.