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Paper Citation Record · LEDGER

Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.12946.

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

pith.paper-citation-record.v1
2403.12946 v2

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:45:55.519507Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:37:31.119029Z

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 bf25c8fe-24d5-4a71-b7e8-fee6f65a26e0 · inbound

Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis cites this paper.

Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-15T20:45:55.519507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:45:55.519507Z digest=sha256:a3332ee3295e10ba64b72b55d57e370e09095a843860e627e2b7274b9a84df26

Observation 2d44951e-5027-47ea-a6e8-03f2f6e0143b · inbound

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning cites this paper.

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

Reference 77

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:37:31.168085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T14:37:29.102856Z digest=sha256:7e1dc21c3f9b5f13a689fb8a981e4b4230dbbbc4608f09b2c66b2d8f83d28249