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Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2408.09675.

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

pith.paper-citation-record.v1
2408.09675 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:30:15.327313Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:39:46.363672Z

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 1cab6ff8-ec38-4faf-96cc-3e1e7ab16ca9 · inbound

Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G cites this paper.

Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 230

Resolution
unresolved
no resolver link, observed 2026-08-08T17:54:46.784599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:54:46.784599Z digest=sha256:766994a845e396ef834c55df3a861e3207b494d876e5b6d1dc6b74f9bfc36fd7

Observation aa69b155-a5f3-49d8-a417-8b976f7ad8b3 · inbound

Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning cites this paper.

Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:15.327313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:15.327313Z digest=sha256:dd843b843fcb62e29001fb81514263ae8c5997951d2d93e3b22d98bafa8483c9

Observation b1ef45a9-d453-4972-8192-06249d9c3230 · inbound

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage cites this paper.

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:12:15.552234Z

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.

source=pdf_text observed=2026-05-19T11:08:51.426575Z digest=sha256:2909bff8c9a5eb1fe7ebd2b03d778e08673680058c906b2fbdad4b5e9534c5b6

Observation e38d6636-f240-41fd-bf89-a74491bd2ba2 · inbound

Automated Vehicles Should be Connected with Natural Language cites this paper.

Automated Vehicles Should be Connected with Natural Language Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T21:48:52.003514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:48:52.003514Z digest=sha256:7228d5d6693431d78819fdc1c4f44cdb5a02b649ea5008880cd22441cef9c921

Observation 872b1cb1-9bd8-4deb-96a5-01d0d94dfcdb · 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 Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-21T08:19:52.582897Z

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.

source=pdf_text observed=2026-05-21T08:16:06.101467Z digest=sha256:c280dd6ce91c01bc894b253bf5b45d518a6b388ee3e2f3524daddf7dc2261aec

Observation f6c51587-10bc-4c23-a66e-83ca402a9ef1 · 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 Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-06-30T18:45:00.598336Z

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.

source=pdf_text observed=2026-06-30T18:41:15.177901Z digest=sha256:deae7bc8e05646d21bc980387fbccdd86f3cf1ca5bfbe8eed85f025373d1a631

Observation 9b06f407-7fa1-4258-95e6-7dfee51136be · inbound

SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication cites this paper.

SCALE-COMM: Shared, Contrastively-Aligned Latent Embeddings for MARL Communication Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:13:44.534134Z

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.

source=pdf_text observed=2026-06-29T17:12:21.761535Z digest=sha256:89b4582fb1d87a1f4076f97efd4f238c7e8ef47e287384ff415d105b60802341

Observation f3df9986-87ac-48f9-846f-d424a2b64443 · inbound

Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning cites this paper.

Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T21:27:24.465575Z

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.

source=arxiv_source observed=2026-06-27T19:46:31.662942Z digest=sha256:58f1c1ca8c699000b61b587995f9211c35f98fd3924871601aa028988717bb1c

Observation 92be3fd7-91cb-4d88-9bbb-25b06f8cc5ee · inbound

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control cites this paper.

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T11:39:46.365155Z

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.

source=arxiv_source observed=2026-06-26T07:54:12.950136Z digest=sha256:393eee4a9815821bfe9b51bb64b243ff8bcf0ab59705831a53a3438213b4ad3f

Observation 3d29be34-18d8-455b-b1f5-fa0021df469b · inbound

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination cites this paper.

MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-12T02:15:17.079975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T02:15:17.079975Z digest=sha256:2fb821ea9927ddc0f95096ae6ec45a0e604191d3c19e15e98afbb36a731b79fb