Paper Citation Record · LEDGER

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2106.03051.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2106.03051 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:10:26.503753Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

22
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cc09f084-dd2d-4bc1-a119-1ad98b7fa693 · inbound

Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment cites this paper.

Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:26.503753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ede161cb-5bdf-42c5-87a3-484a9b7c4d3c · inbound

AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling cites this paper.

AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T11:59:59.255694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76821897-d3ad-4be8-af49-92d78b6f1825 · inbound

GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization cites this paper.

GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:18:07.114136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0efc52f5-c0bb-440b-b37e-2521a14670e1 · inbound

Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling cites this paper.

Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.966504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.