Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.13750.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:20:17.394115Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T20:30:30.285392Z
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 9c2142cb-a13d-493c-a3f5-33d1cd687e0d · inbound
Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding cites this paper.
Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:20:17.394115Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 14e78a57-7704-4df5-badf-01df49855e08 · inbound
MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion cites this paper.
MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T20:30:30.294226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.