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Paper Citation Record · LEDGER

Truly No-Regret Learning in Constrained MDPs

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.15776.

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

pith.paper-citation-record.v1
2402.15776 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:50.106454Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T22:03:30.896960Z

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 0b112a79-ae19-4585-ad14-1282813a7f50 · inbound

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form cites this paper.

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form Truly No-Regret Learning in Constrained MDPs

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-23T22:03:30.898987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-23T21:58:56.180393Z digest=sha256:ab9af714af99cc94b3176421259207a200a91b50b02dde1b96a4ab3adec46ebb

Observation 132bc39d-c3d1-47da-bf21-fd3bedf6e30a · inbound

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints cites this paper.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Truly No-Regret Learning in Constrained MDPs

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T13:35:50.106454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:35:50.106454Z digest=sha256:3ac09c1ed4bba8b0add3611823567ad46d89f46f6de8be8bd852c54b246572d3