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Paper Citation Record · LEDGER

Scaling Laws for a Multi-Agent Reinforcement Learning Model

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2210.00849.

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

pith.paper-citation-record.v1
2210.00849 v2

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:33:51.262129Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:56.118010Z

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 244df2c4-d199-4ed9-943a-c53ed33b561f · inbound

Meek Models Shall Inherit the Earth cites this paper.

Meek Models Shall Inherit the Earth Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T18:33:51.262129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:33:51.262129Z digest=sha256:3f38f6f97fdac50af084f35dfbb837ca24e3cf9a5b556e97753d3cba7eae1331

Observation 06891625-1394-4133-866c-7e4a20abe488 · inbound

Model Merging Scaling Laws in Large Language Models cites this paper.

Model Merging Scaling Laws in Large Language Models Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:32:37.788740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T13:32:33.009367Z digest=sha256:344382bfaef7b801f92485ad52797fa39eb330fd2d0ead56b45e44866ef5d028

Observation 13870364-e23e-4d30-b6d7-2a00b38a6508 · inbound

Scaling Laws of Global Weather Models cites this paper.

Scaling Laws of Global Weather Models Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-02T20:36:37.073859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T20:36:37.073859Z digest=sha256:08311592543363ab1b18236ab3325a84911789c9e788ee7ef02465ef28f17573

Observation 4a5ac36a-c1b4-4282-a76d-88a346a594a7 · inbound

Unified Neural Scaling Laws cites this paper.

Unified Neural Scaling Laws Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T00:14:04.514809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T22:56:43.393302Z digest=sha256:177ade0b3e6bcdd5a36981100b6f27555c49d5cac63ecc6f29a5b6c797349ed4

Observation 362734cd-2221-4713-a92d-60d12e094313 · inbound

Two AI Metrics Diverged: Will it Make All the Difference? cites this paper.

Two AI Metrics Diverged: Will it Make All the Difference? Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T12:36:56.119275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-02T12:29:24.439779Z digest=sha256:b910a07f276d53f9082065ab06c1d19954259e18b448e877ff060bf3e5922130

Observation c7932402-ba4f-4643-b232-4a0163736cac · inbound

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning cites this paper.

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning Scaling Laws for a Multi-Agent Reinforcement Learning Model

Reference 73

Resolution
unresolved
no resolver link, observed 2026-07-13T03:08:01.590659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T03:08:01.590659Z digest=sha256:74963724d39d7224a1ce9b8ce80ca79153e22e3b6be76242597552ff28ede874