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Paper Citation Record · LEDGER

An $\alpha$-potential game framework for $N$-player dynamic games

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.16962.

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

pith.paper-citation-record.v1
2403.16962 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:35:00.157717Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T00:51:56.208412Z

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 2d6b6a55-a5f5-4056-bae8-e47618335c2e · inbound

{\alpha}-RACER: Real-Time Algorithm for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function cites this paper.

{\alpha}-RACER: Real-Time Algorithm for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function An $\alpha$-potential game framework for $N$-player dynamic games

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T17:35:00.157717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:35:00.157717Z digest=sha256:c76fe814d380f819c74cbb3fc8f7a784250bac7919c1edea47e20a1eacfee7fe

Observation 059418f1-ebe5-484a-8092-ec764ce577d2 · inbound

Distributed games with jumps: An $\alpha$-potential game approach cites this paper.

Distributed games with jumps: An $\alpha$-potential game approach An $\alpha$-potential game framework for $N$-player dynamic games

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-19T00:51:56.211720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T00:49:12.474709Z digest=sha256:b767cb7234a2eab3254832db466f7874f4674112996decfff8b0c453feba6274

Observation c155a2a7-d4da-40c2-8f39-1a3a8969cd97 · inbound

Asymmetric Network Games: $\alpha$-Potential Function and Learning cites this paper.

Asymmetric Network Games: $\alpha$-Potential Function and Learning An $\alpha$-potential game framework for $N$-player dynamic games

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T22:43:47.127902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:43:47.127902Z digest=sha256:f6a98cdd3c387001054fbe39e72fd2094159110b44f31985d95c506b4f2a29c2

Observation 9b277d56-1acc-4254-80ed-edd8fbab6fd5 · inbound

NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning cites this paper.

NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning An $\alpha$-potential game framework for $N$-player dynamic games

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-15T15:30:07.630889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T15:27:17.600379Z digest=sha256:2f13414d035829d4641407ce253e3927ed5219ff778a77e2aea8a8610bdd5f4c