Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.08268.
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 2026-08-04T21:03:09.470730Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:08:49.404265Z
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 dbc57cc1-42ce-46ee-98d7-ce4c397a7f34 · inbound
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning cites this paper.
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:03:09.470730Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c20106c-898f-4025-8885-6d95013be826 · inbound
Multi-agent rendezvous in fluid flows via reinforcement learning cites this paper.
Multi-agent rendezvous in fluid flows via reinforcement learning Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T07:57:45.460484Z
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 235b1ef7-b5c5-4236-991f-04a6a0fa87e4 · inbound
Mean Field Reinforcement Learning cites this paper.
Mean Field Reinforcement Learning Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Reference 110
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T19:08:49.405860Z
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.