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Paper Citation Record · LEDGER

Lipschitz Continuity in Model-based Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1804.07193.

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

pith.paper-citation-record.v1
1804.07193 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:21.402923Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-17T12:04:10.871465Z

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 b542f905-588d-4e63-a3f4-a60e5c47e78c · inbound

Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning Lipschitz Continuity in Model-based Reinforcement Learning

Reference 295

Resolution
metadata mismatch
local_arxiv, observed 2026-05-17T12:04:10.873417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-17T12:04:10.210508Z digest=sha256:e3dec8313bb9dcaab0c53fd7d49fbeec0967ad95e44782022b63092a60d01953

Observation 22af8a4b-41b4-401c-b211-b638843799b6 · inbound

Bellman operator convergence enhancements in reinforcement learning algorithms cites this paper.

Bellman operator convergence enhancements in reinforcement learning algorithms Lipschitz Continuity in Model-based Reinforcement Learning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T15:36:21.402923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:36:21.402923Z digest=sha256:8ff622459d527b89d3c508c8c766f0d6c63e5d46f218c6988cdb6b060720bd8f