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Paper Citation Record · LEDGER

PAC Reinforcement Learning for Predictive State Representations

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.05738.

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

pith.paper-citation-record.v1
2207.05738 v3

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:40:58.992054Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:46:05.424788Z

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 a4394478-de6c-47ae-8fce-b8b756c13a87 · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models PAC Reinforcement Learning for Predictive State Representations

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:58.992054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:58.992054Z digest=sha256:14d375b0ce21bf36d60b10450780aadb428ab2f6860acac3c621d9525eda623a

Observation 79800d39-d4b2-4a5d-be12-0e355058d4f9 · inbound

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs cites this paper.

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs PAC Reinforcement Learning for Predictive State Representations

Reference 73

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:46:05.428994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-09T15:12:54.575483Z digest=sha256:1f8e86d424cf7be7a088e99065dd9a9b2404ef037a2e990815145ae7427718e2