Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.12190.
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 2026-08-05T14:49:53.209213Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T06:15:27.555035Z
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 ad34a5e6-b7a7-40d8-9045-eefbf8e37b53 · inbound
Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models cites this paper.
Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:15:27.558692Z
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.
Observation f55d1d7c-b757-41a1-a332-1b7e6b8bef4e · inbound
cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending cites this paper.
cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:49:53.209213Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.