Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z
Paper Citation Record · LEDGER
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
As of 16 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2605.30461.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
5 of 5 outbound references displayed
- verified exact3
- verified fuzzy0
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 02099a04-8294-4794-896f-cf9c285c11eb · outbound
This paper cites [CD11] Yang Cai and Constantinos Daskalakis.
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics [CD11] Yang Cai and Constantinos Daskalakis
Reference 1
- Resolution
- verified exact
- doi, observed 2026-06-29T08:43:14.641330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2a7d1de5-8c22-4381-af1b-dfa260f81a71 · outbound
This paper cites Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning.
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T08:43:14.641498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 57cc0005-270c-4fb8-83ed-bf7160a32680 · outbound
This paper cites Guannan Qu, Yiheng Lin, Adam Wierman, and Na Li.
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics Guannan Qu, Yiheng Lin, Adam Wierman, and Na Li
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:43:14.639434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 60850833-40cc-4895-a667-1db5ff0dd7ff · outbound
This paper cites Then, Lsym is diagonalizable, and its eigenvalues are real.
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics Then, Lsym is diagonalizable, and its eigenvalues are real
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:38:41.127040Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79bc9ae3-b333-42a2-96b4-4b1450718b9c · outbound
This paper cites The eigenvalues ofLrw also lie in[0,2].
Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics The eigenvalues ofLrw also lie in[0,2]
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-29T08:38:41.127040Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.