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Paper Citation Record · LEDGER

Resource allocation in dynamic multiagent systems

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.08317.

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

pith.paper-citation-record.v1
2102.08317 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:22.443344Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:36:47.723584Z

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 e6c120ef-a1e8-4df1-b697-149e62861ecc · inbound

Creativity in LLM-based Multi-Agent Systems: A Survey cites this paper.

Creativity in LLM-based Multi-Agent Systems: A Survey Resource allocation in dynamic multiagent systems

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T13:43:22.443344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:43:22.443344Z digest=sha256:801d9dcec9c4a4fe48a764708c6bc9e9818666bd0d6cf9920e783aef4af314a2

Observation 188c24e9-9852-41fc-b8cd-3458a75810a2 · inbound

GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation cites this paper.

GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation Resource allocation in dynamic multiagent systems

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:36:47.725122Z

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=2026-06-28T05:58:59.176270Z digest=sha256:1db1149e8e6d73fcdbd24aa64a9e59be1cf0f512f09b72994b954c46fc7bc30b