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Paper Citation Record · LEDGER

Learning Fairness in Multi-Agent Systems

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.14472.

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

pith.paper-citation-record.v1
1910.14472 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:37:03.113262Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T20:20:11.845899Z

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 a125462a-c1d3-4342-8673-9c9385053366 · inbound

Fair Contracts in Principal-Agent Games with Heterogeneous Types cites this paper.

Fair Contracts in Principal-Agent Games with Heterogeneous Types Learning Fairness in Multi-Agent Systems

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T19:37:03.113262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:37:03.113262Z digest=sha256:0e41838b8650a43f78ffaff65b0a0439f14421e80c5ff3500d4e59a355e52b85

Observation bc0cdedd-b6db-4d79-98c9-f01a46066913 · inbound

AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning cites this paper.

AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning Learning Fairness in Multi-Agent Systems

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-17T20:20:11.847718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-17T20:18:09.847453Z digest=sha256:dcdd31d564df3565dc6f7a651e7e965031eda9d57806feb9e7dc02190bc9e8ff