Paper Citation Record · LEDGER

Denoised MDPs: Learning World Models Better Than the World Itself

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2206.15477.

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

pith.paper-citation-record.v1
2206.15477 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:30:24.150230Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:27:09.651046Z

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 5ff31b1c-18ba-41e2-b8ec-0e65e87809cb · inbound

Policy-shaped prediction: avoiding distractions in model-based reinforcement learning cites this paper.

Policy-shaped prediction: avoiding distractions in model-based reinforcement learning Denoised MDPs: Learning World Models Better Than the World Itself

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:45.031618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 787afeb1-f994-4e6c-8c5c-19f034ad1dc4 · inbound

Latent Action Learning Requires Supervision in the Presence of Distractors cites this paper.

Latent Action Learning Requires Supervision in the Presence of Distractors Denoised MDPs: Learning World Models Better Than the World Itself

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T19:18:45.146809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c32fcc-22dc-4790-a6da-1a44c7ecc953 · inbound

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control cites this paper.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Denoised MDPs: Learning World Models Better Than the World Itself

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:30:24.150230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01cb7eed-f05d-4579-811c-e781315408a4 · inbound

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models cites this paper.

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models Denoised MDPs: Learning World Models Better Than the World Itself

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:50:48.329305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4c03896-0f4d-4519-b14f-b67515617821 · inbound

Mask World Model: Predicting What Matters for Robust Robot Policy Learning cites this paper.

Mask World Model: Predicting What Matters for Robust Robot Policy Learning Denoised MDPs: Learning World Models Better Than the World Itself

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:11:06.288094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4b6c52a4-662d-4310-abff-6573d5ae9e58 · inbound

Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning cites this paper.

Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning Denoised MDPs: Learning World Models Better Than the World Itself

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:27:09.652482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.