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Paper Citation Record · LEDGER

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.14646.

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

pith.paper-citation-record.v1
2004.14646 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:34:36.114222Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:21:31.528623Z

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 fe79a99b-7be5-4b09-a73b-94efda088bd3 · inbound

A Survey of State Representation Learning for Deep Reinforcement Learning cites this paper.

A Survey of State Representation Learning for Deep Reinforcement Learning Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T23:34:36.114222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:34:36.114222Z digest=sha256:cccb0a82e4e8b31fc2f5d5713fabf76793c8420dbac03f9ace2df20e0a5a0ab2

Observation 738cc90b-2a9c-4e36-a330-0d6ced055fca · inbound

Learning to Theorize the World from Observation cites this paper.

Learning to Theorize the World from Observation Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Reference 130

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:21:31.596565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-07T17:15:43.429602Z digest=sha256:3fbdba7f0087cdb86ea7077e81b9f05015407a69caf175689a6c18c5c16f12c3