Paper Citation Record · LEDGER

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 5 inbound Pith citation observations for arXiv:2504.13101.

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

pith.paper-citation-record.v1
2504.13101 v3

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:18:11.753182Z

measured 17 of 17 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T15:01:36.288362Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T00:25:09.934600Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81983e80-8f8b-4582-b879-79bc092e7707 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.705308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3167e37-b295-40ea-8f6a-600435556925 · outbound

This paper cites Function Classes for Identifiable Nonlinear Independent Component Analysis.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Function Classes for Identifiable Nonlinear Independent Component Analysis

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-16T12:18:11.699852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0677bf4e-08b3-4521-9aab-0606d3f5d09d · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.738053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 446152b8-133e-4666-b306-1da4b7dd1323 · outbound

This paper cites The Platonic Representation Hypothesis.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research The Platonic Representation Hypothesis

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.711090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36e9a8db-f37f-4be9-a419-75dcb3d0a64f · outbound

This paper cites Platonic ideals.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Platonic ideals

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.753182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f4a4d10-a459-42d1-a373-0d7592b38c56 · outbound

This paper cites Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.721651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5c3c285-c334-49fb-ad5d-db8454482f0a · outbound

This paper cites The bitter lesson.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research The bitter lesson

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.747887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c02b5604-053d-40cf-87e5-bc639268ce2c · outbound

This paper cites Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.716401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee5d0ab5-0f57-4807-8e36-86bf43f192fc · outbound

This paper cites Weakly-Supervised Disentanglement Without Compromises.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Weakly-Supervised Disentanglement Without Compromises

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.733141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c84fa07-45b0-41dc-8627-e2f6f910aee8 · outbound

This paper cites Provably Learning Object-Centric Representations.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Provably Learning Object-Centric Representations

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.693572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e272223-f641-4c5e-9557-585fbcec0571 · outbound

This paper cites Self-Supervised Learning via Maximum Entropy Coding.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Self-Supervised Learning via Maximum Entropy Coding

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.728266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites On Linear Identifiability of Learned Representations.

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.

Pith citing papers

Observation 16006a86-cd57-430c-9c43-04cdf73753a1 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:09.298432Z

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.

Observation 910309bc-9e13-430f-bcfd-ed7da289d8c7 · inbound

Understanding Self-Supervised Learning via Latent Distribution Matching cites this paper.

Understanding Self-Supervised Learning via Latent Distribution Matching Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:51:32.016142Z

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.

Observation add60ea5-621d-4987-ae5c-7bdf50be33c2 · inbound

Understanding Self-Supervised Learning via Latent Distribution Matching cites this paper.

Understanding Self-Supervised Learning via Latent Distribution Matching Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:13:53.207046Z

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.

Observation 67008492-c268-481c-a785-89095e1e55d7 · inbound

Understanding Self-Supervised Learning via Latent Distribution Matching cites this paper.

Understanding Self-Supervised Learning via Latent Distribution Matching Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:25:09.936388Z

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.

Observation a818421c-b353-47b1-8788-1eb65efb1f18 · inbound

Understanding Self-Supervised Learning via Latent Distribution Matching cites this paper.

Understanding Self-Supervised Learning via Latent Distribution Matching Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T15:01:36.288362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.