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Paper Citation Record · LEDGER

EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.00498.

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

pith.paper-citation-record.v1
2210.00498 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:21:18.464264Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T17:27:35.916836Z

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 9dcfb937-6716-4056-bdf9-1e1cc0c76cf7 · inbound

TD-MPC2: Scalable, Robust World Models for Continuous Control cites this paper.

TD-MPC2: Scalable, Robust World Models for Continuous Control EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

Reference 150

Resolution
verified exact
arxiv_id, observed 2026-05-14T17:27:35.920063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-14T17:27:35.733800Z digest=sha256:7e9a484faf0692bf3566bd57e0f579fc1fe2956902709553e966941168f0960f

Observation 97a9345c-5642-4da6-94fc-5352cf84b381 · inbound

Exploratory Diffusion Model for Unsupervised Reinforcement Learning cites this paper.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-08T13:21:18.464264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:21:18.464264Z digest=sha256:6f97eb9c435cbefaa56863330b41b4afebaa2d4a3866635cc5fbb884bc63c961