Paper Citation Record · LEDGER

Learning-based Multi-agent Race Strategies in Formula 1

As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2602.23056.

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

pith.paper-citation-record.v1
2602.23056 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:32:44.146416Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c6f21b03-8771-4bde-b9cf-8396b0d2b22e · outbound

This paper cites Virtual strategy engineer: Using artificial neural networks for making race strategy decisions in circuit motorsport,.

Learning-based Multi-agent Race Strategies in Formula 1 Virtual strategy engineer: Using artificial neural networks for making race strategy decisions in circuit motorsport,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.215047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 391b5798-98c1-4e01-83fb-9ac2fd075460 · outbound

This paper cites Minimum-race-time energy allocation strategies for the hybrid- electric Formula 1 power unit,.

Learning-based Multi-agent Race Strategies in Formula 1 Minimum-race-time energy allocation strategies for the hybrid- electric Formula 1 power unit,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.295456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 546b6610-2780-4e30-a885-07ca4111869e · outbound

This paper cites Maximum-distance race strategies for a fully electric endurance race car,.

Learning-based Multi-agent Race Strategies in Formula 1 Maximum-distance race strategies for a fully electric endurance race car,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.476627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af1c9b88-092c-4ac9-ba30-559ba9f9df74 · outbound

This paper cites Evolutionary F1 race strategy,.

Learning-based Multi-agent Race Strategies in Formula 1 Evolutionary F1 race strategy,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.628442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 544ee99e-7c3f-4ee4-ac39-f0912db3a9fc · outbound

This paper cites On the optimization of pit stop strategies via dynamic programming,.

Learning-based Multi-agent Race Strategies in Formula 1 On the optimization of pit stop strategies via dynamic programming,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.724517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c35703c-adaf-4db3-b0ec-f62adec9c5d5 · outbound

This paper cites Strategic co-design in Formula 1: Balancing physical configuration and race tactics,.

Learning-based Multi-agent Race Strategies in Formula 1 Strategic co-design in Formula 1: Balancing physical configuration and race tactics,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.817258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa59000-3f9e-46f3-8fd8-202a0240b01a · outbound

This paper cites Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsports.

Learning-based Multi-agent Race Strategies in Formula 1 Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsports

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.929440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47a25eb9-4d37-45fe-a915-d61fc869a9b4 · outbound

This paper cites Explainable reinforcement learning for Formula One race strategy,.

Learning-based Multi-agent Race Strategies in Formula 1 Explainable reinforcement learning for Formula One race strategy,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:42.984149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e9fbf2e-a280-4622-96c3-8f67aa8107a5 · outbound

This paper cites Formula-E race strategy development using distributed policy gradient reinforcement learning,.

Learning-based Multi-agent Race Strategies in Formula 1 Formula-E race strategy development using distributed policy gradient reinforcement learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.037192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abe93b4b-ad3b-472f-9211-3a51f1997327 · outbound

This paper cites Towards learning-based Formula 1 race strategies,.

Learning-based Multi-agent Race Strategies in Formula 1 Towards learning-based Formula 1 race strategies,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.105299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d427bfa9-6544-4f54-a2ae-169dc03965d7 · outbound

This paper cites Competitors- aware stochastic lap strategy optimisation for race hybrid vehicles,.

Learning-based Multi-agent Race Strategies in Formula 1 Competitors- aware stochastic lap strategy optimisation for race hybrid vehicles,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.261087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8783ebd8-98b6-496f-8d55-87aa4f15fdaa · outbound

This paper cites Model predictive control strategies for electric endurance race cars accounting for competitors’ interactions,.

Learning-based Multi-agent Race Strategies in Formula 1 Model predictive control strategies for electric endurance race cars accounting for competitors’ interactions,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.310142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 568e375a-c9f9-48c1-bb05-1aadf0f9523d · outbound

This paper cites Optimizing pit stop strategies in Formula 1 with dynamic programming and game theory,.

Learning-based Multi-agent Race Strategies in Formula 1 Optimizing pit stop strategies in Formula 1 with dynamic programming and game theory,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.360325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a35ad26-6a30-4f2e-8eff-993f50047a05 · outbound

This paper cites Formula-E multi-car race strategy development—a novel approach using reinforcement learning,.

Learning-based Multi-agent Race Strategies in Formula 1 Formula-E multi-car race strategy development—a novel approach using reinforcement learning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.499788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b2f26a6-c4fb-4c69-8610-474a39adc2cb · outbound

This paper cites Game Theory in Formula 1: From Physical to Strategic Interactions.

Learning-based Multi-agent Race Strategies in Formula 1 Game Theory in Formula 1: From Physical to Strategic Interactions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.645323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6b55e3a-af46-46f8-b2e5-50c8797200c6 · outbound

This paper cites Interaction-aware multi-agent reinforcement learning for mobile agents with individual goals,.

Learning-based Multi-agent Race Strategies in Formula 1 Interaction-aware multi-agent reinforcement learning for mobile agents with individual goals,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.726965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7bc4d07-cb52-47b3-802a-e479e6b9844d · outbound

This paper cites Highly accurate protein structure prediction with alphafold,.

Learning-based Multi-agent Race Strategies in Formula 1 Highly accurate protein structure prediction with alphafold,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.810076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06164088-8257-4fcd-94bc-c78d7f995e66 · outbound

This paper cites Mastering the game of go without human knowledge,.

Learning-based Multi-agent Race Strategies in Formula 1 Mastering the game of go without human knowledge,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:43.872090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 843757f9-6bd1-4fba-9eb1-0eea662094b5 · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,.

Learning-based Multi-agent Race Strategies in Formula 1 A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:44.034989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e882454-d4bd-4a15-831f-c0e38d3948b7 · outbound

This paper cites The rating of chessplayers, past and present,.

Learning-based Multi-agent Race Strategies in Formula 1 The rating of chessplayers, past and present,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T20:32:44.146416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.