Paper Citation Record · LEDGER

Symmetric Linear Dynamical Systems are Learnable from Few Observations

As of 10 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 1 inbound Pith citation observation for arXiv:2512.05337.

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

pith.paper-citation-record.v1
2512.05337 v2

Coverage vector

measured 3 of 3 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:34:56.135450Z

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:58:15.614504Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:35:52.656846Z

Reference resolution

3 of 3 outbound references displayed

verified exact0
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dbb38aab-2848-40eb-b0a4-dcbcf7b62dd2 · outbound

This paper cites Learning linear dynamical systems under convex constraints.

Symmetric Linear Dynamical Systems are Learnable from Few Observations Learning linear dynamical systems under convex constraints

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T18:34:56.135450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb33a678-f373-43be-b3a8-fa764be1fa07 · outbound

This paper cites Revisiting ho–kalman-based system identification: Robustness and finite-sample analysis.IEEE Transactions on Automatic Control, 67(4):1914–1928,.

Symmetric Linear Dynamical Systems are Learnable from Few Observations Revisiting ho–kalman-based system identification: Robustness and finite-sample analysis.IEEE Transactions on Automatic Control, 67(4):1914–1928,

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-03T18:34:56.127924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d9f3e56-f161-470e-8a56-ae30777d96d6 · outbound

This paper cites Large Vector Auto Regressions.

Symmetric Linear Dynamical Systems are Learnable from Few Observations Large Vector Auto Regressions

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T18:34:56.131805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation aa47b8b5-b5a3-486e-ad67-52ab64f39a54 · inbound

Network Reconstruction in Consensus Algorithms with Hidden Agents cites this paper.

Network Reconstruction in Consensus Algorithms with Hidden Agents Symmetric Linear Dynamical Systems are Learnable from Few Observations

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-25T01:17:48.116514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.