Paper Citation Record · LEDGER

Explainable Reinforcement Learning Agents Using World Models

As of 23 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2505.08073.

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

pith.paper-citation-record.v1
2505.08073 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:08:43.060904Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation c634ca70-d014-411f-a831-e0c4d0d92586 · outbound

This paper cites Experiential explanations for reinforce- ment learning.

Explainable Reinforcement Learning Agents Using World Models Experiential explanations for reinforce- ment learning

Reference 1

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Source-reported events for the cited work

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Observation 09b97db1-4270-4e63-beea-4cada6455eb7 · outbound

This paper cites Traditional and raw task load index (tlx) correlations: Are paired comparisons necessary.

Explainable Reinforcement Learning Agents Using World Models Traditional and raw task load index (tlx) correlations: Are paired comparisons necessary

Reference 5

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Observation c84ca078-c149-4b9d-b336-0087680c39fb · outbound

This paper cites World models.

Explainable Reinforcement Learning Agents Using World Models World models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.392105Z

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Observation d6da496a-9be0-4a32-910d-c31f99c81769 · outbound

This paper cites Learning latent dynamics for planning from pixels,.

Explainable Reinforcement Learning Agents Using World Models Learning latent dynamics for planning from pixels,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.378077Z

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Observation c13b1c27-e517-4dae-ac84-586bb75d44ac · outbound

This paper cites Mastering diverse control tasks through world models.

Explainable Reinforcement Learning Agents Using World Models Mastering diverse control tasks through world models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.235237Z

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Observation ecdb15b2-15e1-4c81-895b-f0d6e53f07eb · outbound

This paper cites Benchmarking the spectrum of agent capabilities,.

Explainable Reinforcement Learning Agents Using World Models Benchmarking the spectrum of agent capabilities,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.218815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 138cac29-75a5-48b0-975f-6265ff0ac59a · outbound

This paper cites Olson, and Elisabeth Andr ´e.

Explainable Reinforcement Learning Agents Using World Models Olson, and Elisabeth Andr ´e

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.995089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4953046d-cc8d-48ec-ae44-9e1321ed5155 · outbound

This paper cites [Kaelbling et al., 1996] Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore.

Explainable Reinforcement Learning Agents Using World Models [Kaelbling et al., 1996] Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.874056Z

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Observation 2a97cd61-aa9b-4090-a823-f36449c80426 · outbound

This paper cites Why don’t you do something about it? outlining connec- tions between ai explanations and user actions,.

Explainable Reinforcement Learning Agents Using World Models Why don’t you do something about it? outlining connec- tions between ai explanations and user actions,

Reference 20

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Source-reported events for the cited work

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Observation 6141cb9a-4465-4216-9a36-96d830e3260f · outbound

This paper cites Explainable reinforcement learning: A survey and comparative review.

Explainable Reinforcement Learning Agents Using World Models Explainable reinforcement learning: A survey and comparative review

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.764909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 68b69e45-a37e-4349-806f-a0f982118327 · outbound

This paper cites Explanation in artificial intelli- gence: Insights from the social sciences.

Explainable Reinforcement Learning Agents Using World Models Explanation in artificial intelli- gence: Insights from the social sciences

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.689442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b398b613-5ee3-4f0b-ab7d-132af08d4ea7 · outbound

This paper cites Olson, Roli Khanna, Lawrence Neal, Fuxin Li, and Weng-Keen Wong.

Explainable Reinforcement Learning Agents Using World Models Olson, Roli Khanna, Lawrence Neal, Fuxin Li, and Weng-Keen Wong

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.587478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 40710ab0-4933-4e05-b106-2edbeef82f63 · outbound

This paper cites Inherently explainable reinforcement learning in natural language.

Explainable Reinforcement Learning Agents Using World Models Inherently explainable reinforcement learning in natural language

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.504443Z

Source-reported events for the cited work

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Observation c5efbf91-ad15-44c3-a55b-34f822687d8c · outbound

This paper cites Counterfactual ex- plainer for deep reinforcement learning models using pol- icy distillation.

Explainable Reinforcement Learning Agents Using World Models Counterfactual ex- plainer for deep reinforcement learning models using pol- icy distillation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.424716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a54340c7-63c7-4c4d-89c6-c2dc7c05570a · outbound

This paper cites Integrating policy summaries with reward decomposition for explaining reinforcement learn- ing agents.

Explainable Reinforcement Learning Agents Using World Models Integrating policy summaries with reward decomposition for explaining reinforcement learn- ing agents

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.365998Z

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Observation 841f3181-13c5-4515-8b51-0ddf8d1d9931 · outbound

This paper cites Bridging the gap: Providing post-hoc sym- bolic explanations for sequential decision-making prob- lems with inscrutable representations.

Explainable Reinforcement Learning Agents Using World Models Bridging the gap: Providing post-hoc sym- bolic explanations for sequential decision-making prob- lems with inscrutable representations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.282012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c07f0ad3-1c98-4082-b559-904866319031 · outbound

This paper cites Contrastive explana- tions for reinforcement learning in terms of expected con- sequences.

Explainable Reinforcement Learning Agents Using World Models Contrastive explana- tions for reinforcement learning in terms of expected con- sequences

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.206753Z

Source-reported events for the cited work

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Observation 9ba0c72d-6bb8-4082-8dbf-227614042c79 · outbound

This paper cites Assessing explainability in reinforcement learning.

Explainable Reinforcement Learning Agents Using World Models Assessing explainability in reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.189709Z

Source-reported events for the cited work

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Observation 064225cf-735a-467f-97bb-279fd0144754 · outbound

This paper cites Hoffman, Shane T.

Explainable Reinforcement Learning Agents Using World Models Hoffman, Shane T

Reference 1988

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.068414Z

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Observation 2a63aa48-6dcc-4c77-a249-c44c4695f79f · outbound

This paper cites The emerging landscape of explainable automated planning & decision making.

Explainable Reinforcement Learning Agents Using World Models The emerging landscape of explainable automated planning & decision making

Reference 1989

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.763930Z

Source-reported events for the cited work

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Observation e40a5ab3-437b-43cb-9792-3e5c464b6e00 · outbound

This paper cites Explainable reinforcement learning through a causal lens.

Explainable Reinforcement Learning Agents Using World Models Explainable reinforcement learning through a causal lens

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:43.858172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 926c3f12-3b05-48d5-92a7-69be3aab3f77 · outbound

This paper cites Explaining reinforcement learning agents through counterfactual action outcomes.

Explainable Reinforcement Learning Agents Using World Models Explaining reinforcement learning agents through counterfactual action outcomes

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.998630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0d2a2448-593c-4070-a0ee-4f73d44b145c · outbound

This paper cites Diversity is all you need: Learning skills without a reward function,.

Explainable Reinforcement Learning Agents Using World Models Diversity is all you need: Learning skills without a reward function,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.514200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b64c1344-b4a1-484f-a468-9afa71fce6ac · outbound

This paper cites State2explanation: Concept-based explanations to benefit agent learning and user understanding.Advances in Neural Information Processing Systems, 36:67156–67182,.

Explainable Reinforcement Learning Agents Using World Models State2explanation: Concept-based explanations to benefit agent learning and user understanding.Advances in Neural Information Processing Systems, 36:67156–67182,

Reference 2020

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation babc8fc7-8dab-45b9-84b6-fa924764016c · outbound

This paper cites Leveraging rationales to improve human task per- formance.

Explainable Reinforcement Learning Agents Using World Models Leveraging rationales to improve human task per- formance

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.685176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 827bf23b-d9c7-4398-9aff-827cc85839dc · outbound

This paper cites Development of nasa-tlx (task load index): Re- sults of empirical and theoretical research.

Explainable Reinforcement Learning Agents Using World Models Development of nasa-tlx (task load index): Re- sults of empirical and theoretical research

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.092568Z

Source-reported events for the cited work

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Observation a15d1a93-8d50-4c27-8388-a3372b625e4b · outbound

This paper cites Auto- mated rationale generation: a technique for explainable ai and its effects on human perceptions.

Explainable Reinforcement Learning Agents Using World Models Auto- mated rationale generation: a technique for explainable ai and its effects on human perceptions

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.587522Z

Source-reported events for the cited work

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Observation 725593ed-5448-4110-8d0a-35d3663ddc6a · outbound

This paper cites Efficient Adaptation of Reinforcement Learning Agents to Suddent Environmental Change.

Explainable Reinforcement Learning Agents Using World Models Efficient Adaptation of Reinforcement Learning Agents to Suddent Environmental Change

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:44.900731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4e51d79-3b3d-4e10-b22f-f4df66770970 · outbound

This paper cites Agent strategy summarization.

Explainable Reinforcement Learning Agents Using World Models Agent strategy summarization

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:08:45.120924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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