Paper Citation Record · LEDGER

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2311.10090.

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

pith.paper-citation-record.v1
2311.10090 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:39:32.181171Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T18:45:00.587178Z

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 8d6e0c89-2840-4822-a6f3-7c0dbb8d85cb · inbound

Learning to Reason at the Frontier of Learnability cites this paper.

Learning to Reason at the Frontier of Learnability JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:02.673620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54fc791e-7b69-4d75-9263-9d6fdf4a8ce5 · inbound

Recurrent Structural Policy Gradient for Partially Observable Mean Field Games cites this paper.

Recurrent Structural Policy Gradient for Partially Observable Mean Field Games JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T21:30:17.439093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec434f3f-33f7-441b-a989-de607ede3ee9 · inbound

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments cites this paper.

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T18:45:23.125183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a35becdb-4c4b-4a1e-94d2-42474c00f678 · inbound

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty cites this paper.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:02.673620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 484bde7f-451f-4547-aabe-3ef1e0edc9c8 · inbound

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty cites this paper.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:02.673620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 89a1d97a-2b21-464b-90cb-0e25441f4a72 · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 264

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:32.181171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.