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Paper Citation Record · LEDGER

Temporal Abstraction in Reinforcement Learning with the Successor Representation

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2110.05740.

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

pith.paper-citation-record.v1
2110.05740 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:59:30.049297Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T06:55:29.510936Z

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 ca652e6f-5741-46ed-8d79-22256bfed4a8 · inbound

Dynamic Latent Routing cites this paper.

Dynamic Latent Routing Temporal Abstraction in Reinforcement Learning with the Successor Representation

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:49:38.178035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-15T01:49:14.934586Z digest=sha256:795df932c01174d13ddf85517a29a573e63c847624bc83afe227722e7f813912

Observation dac7fbbc-6f19-41db-9efb-6bce0d2bafb4 · inbound

Exploration and Online Transfer with Behavioral Foundation Models cites this paper.

Exploration and Online Transfer with Behavioral Foundation Models Temporal Abstraction in Reinforcement Learning with the Successor Representation

Reference 42

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T06:55:29.514716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-07-01T06:50:59.743859Z digest=sha256:ee18f90a537e8b3d75b3541b0cc7d29766410ed4079a7e8ded19f4dc69d58b6e

Observation cabdb706-c5ba-4a3b-8755-0ef54ca2c5f3 · inbound

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction cites this paper.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Temporal Abstraction in Reinforcement Learning with the Successor Representation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T04:59:30.049297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:59:30.049297Z digest=sha256:7a7e55b9747726e34acec165aadafb188342ec463f169538a7c0c291bdefb433