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Paper Citation Record · LEDGER

Procedural Generalization by Planning with Self-Supervised World Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.01587.

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

pith.paper-citation-record.v1
2111.01587 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:12:51.858776Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:19:31.226922Z

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 26d6ab07-b538-44e4-81bf-537e2f89ccb1 · inbound

Analogy making as amortised model construction cites this paper.

Analogy making as amortised model construction Procedural Generalization by Planning with Self-Supervised World Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T15:12:51.858776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:12:51.858776Z digest=sha256:e00757269ce5a2272a4ce776f89b7b94877d745e2065fce7863b49de42372c6f

Observation af60f6f7-a839-4697-8ef2-ee29fe851e40 · inbound

Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning cites this paper.

Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning Procedural Generalization by Planning with Self-Supervised World Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:19:31.229076Z

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=arxiv_source observed=2026-06-26T18:12:00.111067Z digest=sha256:01a2a18fd195bf095aebf1e5c240b78ceb73f3551631a65de51f6f1b9b3d1107