Paper Citation Record · LEDGER

On Linear Identifiability of Learned Representations

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.00810.

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

pith.paper-citation-record.v1
2007.00810 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:18:11.742938Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

16
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ace1622d-cd63-4505-8011-51061d28b305 · inbound

Score-Based Generative Modeling through Stochastic Differential Equations cites this paper.

Score-Based Generative Modeling through Stochastic Differential Equations On Linear Identifiability of Learned Representations

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-24T14:09:33.086184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18ec24e3-6e4c-4537-9c4c-9f0d19ccef1e · inbound

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws cites this paper.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws On Linear Identifiability of Learned Representations

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.025945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ccfdf918-fc11-4fc0-98cc-c5a3aed59f13 · inbound

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research cites this paper.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research On Linear Identifiability of Learned Representations

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.742938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9f6024d-09bb-4c0d-8423-3861efb61d73 · inbound

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning cites this paper.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning On Linear Identifiability of Learned Representations

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T15:56:58.462920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f73cbbc4-673c-4a00-a343-99731e664643 · inbound

Unsupervised Causal Abstractions Discovery cites this paper.

Unsupervised Causal Abstractions Discovery On Linear Identifiability of Learned Representations

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-06-26T20:49:57.370084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.