Paper Citation Record · LEDGER

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2506.12497.

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

pith.paper-citation-record.v1
2506.12497 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:54:47.414689Z

measured 19 of 19 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T23:08:08.096620Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:10:44.714270Z

Reference resolution

18 of 18 outbound references displayed

verified exact2
verified fuzzy9
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f42733e-c511-4ad0-b335-1fc0f1732b30 · outbound

This paper cites Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:46.227463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84e1b523-0713-4f3b-9761-1a340b31ae29 · outbound

This paper cites W AVE : Wasserstein adaptive value estimation for actor-critic reinforcement learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning W AVE : Wasserstein adaptive value estimation for actor-critic reinforcement learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.990993Z

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 6d058de6-474e-4b44-9f7a-4bb24a8eb3c4 · outbound

This paper cites A comprehensive survey of multiagent reinforcement learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning A comprehensive survey of multiagent reinforcement learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.730019Z

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 c726fe8f-7fe6-478c-b72d-62fe210d7b63 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Sinkhorn distances: Lightspeed computation of optimal transport

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:46.460203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c59024bb-4219-48ad-9934-fde4d456da8c · outbound

This paper cites Counterfactual multi-agent policy gradients.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Counterfactual multi-agent policy gradients

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.465082Z

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 f8d904c0-40f6-4a96-9182-aedfcc5ebaff · outbound

This paper cites Learning generative models with sinkhorn divergences.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Learning generative models with sinkhorn divergences

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.449756Z

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 372ee92a-97fe-4dd6-ac43-2e00abc7bb80 · outbound

This paper cites On centralized critics in multi-agent reinforcement learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning On centralized critics in multi-agent reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.303839Z

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 34068ae7-78f9-436f-af10-7ffc26f869c4 · outbound

This paper cites Offline reinforcement learning with wasserstein regularization via optimal transport maps.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Offline reinforcement learning with wasserstein regularization via optimal transport maps

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:48.140398Z

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 886e0ab9-092a-4922-affa-0fb8e0837136 · outbound

This paper cites Computational optimal transport: With applications to data science.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Computational optimal transport: With applications to data science

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:46.851586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d782aa7f-4058-4561-9759-a1608ae3790c · outbound

This paper cites Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:54:47.660350Z

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 fc2fb200-27fd-4ffe-94f1-7fb041096eb9 · outbound

This paper cites and Lu, Z.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning and Lu, Z

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:47.994308Z

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 0db4bfa3-e463-4b1a-8aea-75324d59aa9b · outbound

This paper cites Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:47.107571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5d0eea6-5c5e-4e18-a68f-8a40f1038674 · outbound

This paper cites an unresolved cited work.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:47.159121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4695893-ba4d-4cab-95bb-3bbc59ca3369 · outbound

This paper cites Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:54:47.545452Z

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 cacbca4f-d833-4410-9e41-b02012472d8a · outbound

This paper cites Learning to share in networked multi-agent reinforcement learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning Learning to share in networked multi-agent reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:47.884663Z

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 87965751-f283-4f5b-90f9-3e2e537673df · outbound

This paper cites A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:47.313334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 292daf96-c009-42a2-a788-c17c0a617d7f · outbound

This paper cites A semi-independent policies training method with shared representation for heterogeneous multi-agents reinforcement learning.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning A semi-independent policies training method with shared representation for heterogeneous multi-agents reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:47.777866Z

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 7e81fb14-5203-486e-8544-d2e733049abe · outbound

This paper cites write newline.

Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning write newline

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:47.414689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3a27ee60-33fd-4334-9fc9-ba49c7b988e1 · inbound

Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters cites this paper.

Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters Wasserstein-Barycenter Consensus for Cooperative Multi-Agent Reinforcement Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:10:44.715867Z

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.