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Paper Citation Record · LEDGER

On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.11432.

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

pith.paper-citation-record.v1
2405.11432 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:28:40.574549Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:32:59.025661Z

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 b0e01a65-b17d-42cd-9e93-4021b7950582 · inbound

Real Time Control of Tandem-Wing Experimental Platform Using Concerto Reinforcement Learning cites this paper.

Real Time Control of Tandem-Wing Experimental Platform Using Concerto Reinforcement Learning On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-08T19:28:40.574549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:28:40.574549Z digest=sha256:76d23aad9a24d40bf1ec3c82175029fbc7f4ad13e2fc9e95c15d561f04ce876a

Observation de42dd65-0663-4869-91e1-e59e5e24e0a3 · inbound

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks cites this paper.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-06T21:32:59.033488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T21:32:58.966103Z digest=sha256:4017d77b4708ec8c9ea3b0067cc17e5503ba5bf10d4f758fccdb859fc12d9ad5