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Paper Citation Record · LEDGER

Auto-exploration for online reinforcement learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2512.06244.

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

pith.paper-citation-record.v1
2512.06244 v4

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-14T19:28:32.795407Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T19:29:23.733731Z

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 3c2f95b9-6767-426a-8ab0-2aecb20cb122 · inbound

Value Mirror Descent for Reinforcement Learning cites this paper.

Value Mirror Descent for Reinforcement Learning Auto-exploration for online reinforcement learning

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-06-25T02:18:11.514207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T19:07:41.863349Z digest=sha256:9c112eb066b57fba966671cd8b40bb54cc1aeb38e70f93f2bcfdf70963cc7fab

Observation b1293fed-13ee-4003-9f63-d3f5c7a3ce3f · inbound

Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions cites this paper.

Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions Auto-exploration for online reinforcement learning

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-25T02:18:11.514207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-14T19:28:32.795407Z digest=sha256:3439979780b97917e1a47ae6bc62b7b5cd804bcec6badff8e226c326826e86d4