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Paper Citation Record · LEDGER

Safe Multi-Agent Reinforcement Learning via Shielding

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2101.11196.

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

pith.paper-citation-record.v1
2101.11196 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:26:24.410893Z

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

30
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 598d7bfc-750f-438d-990a-b2d5172c037f · inbound

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning cites this paper.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Multi-Agent Reinforcement Learning via Shielding

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-10T21:26:24.410893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:26:24.410893Z digest=sha256:87b099a7282b0e37cc92b80fb0bc110450c47246a2b7b231ef60d4da7359b92c

Observation d0606e34-004e-427b-9db7-b06074b89dfe · inbound

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics cites this paper.

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics Safe Multi-Agent Reinforcement Learning via Shielding

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T17:39:01.233317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:39:01.233317Z digest=sha256:84cb6575226250826438aa7532212bf9db9444c3de3600675adb09482eb6668e

Observation 0e8495f5-7bb4-4c17-aac5-b40a550bdd1e · inbound

Efficient Dynamic Shielding for Parametric Safety Specifications cites this paper.

Efficient Dynamic Shielding for Parametric Safety Specifications Safe Multi-Agent Reinforcement Learning via Shielding

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T13:23:50.095616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:23:50.095616Z digest=sha256:63614a2a22d0c0d6b92dc882504118086bdb9056902813c53dbcde22a23ab5d1

Observation fa3f0ceb-416a-47f6-bfdb-9959c09d2369 · inbound

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions cites this paper.

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions Safe Multi-Agent Reinforcement Learning via Shielding

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:41:53.844718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-18T22:37:32.388931Z digest=sha256:5d5b40e7e68b7799d643018f4dff30613bc2438b7e46ed0bd72dbcc702f56b93

Observation 7a43f2c0-b18f-4aeb-888c-b911155a2a78 · inbound

Generating Local Shields for Decentralised Partially Observable Markov Decision Processes cites this paper.

Generating Local Shields for Decentralised Partially Observable Markov Decision Processes Safe Multi-Agent Reinforcement Learning via Shielding

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-10T17:45:40.809689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T17:44:29.380336Z digest=sha256:e31fd3903494227d0b7e7783e119691aef68feb6f1081601d14ca11e09a64b86