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Paper Citation Record · LEDGER

Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.01680.

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

pith.paper-citation-record.v1
2201.01680 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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:58:56.177965Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:06:55.406756Z

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 b05cb6a5-4105-4e02-a035-f418a6532da7 · inbound

Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds cites this paper.

Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T17:58:56.177965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T17:58:56.177965Z digest=sha256:6d11437deef677a1d070ebb1b2ff7f310a93a1259a02ee74247675be423bba71

Observation f8a4e4e1-5887-4540-bb31-ee742cace3a9 · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems

Reference 261

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:06:55.408237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-28T02:35:39.845487Z digest=sha256:a499c60fd3d6865fa905cd7014c300c3d3afd8b1d2c86bedf2618b4ae4732057