Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.10859.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T19:37:03.151417Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T05:26:38.634684Z
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 fccbbd7e-3b76-4161-96cb-c3baa963aba2 · inbound
Fair Contracts in Principal-Agent Games with Heterogeneous Types cites this paper.
Fair Contracts in Principal-Agent Games with Heterogeneous Types Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:37:03.151417Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c754b632-0eca-429c-97fb-05dc0a475b75 · inbound
PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning cites this paper.
PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:26:38.638889Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.