Paper Citation Record · LEDGER

Trained Transformers Learn Linear Models In-Context

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2306.09927.

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

pith.paper-citation-record.v1
2306.09927 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:13:25.873634Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:29:50.992154Z

Reference resolution

0 of 0 outbound references displayed

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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 c1446f92-28fc-456e-9393-cb813de2747d · inbound

Transformers versus the EM Algorithm in Multi-class Clustering cites this paper.

Transformers versus the EM Algorithm in Multi-class Clustering Trained Transformers Learn Linear Models In-Context

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T17:13:25.873634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb06c04-b450-4bef-9b05-d4e01e6abfb9 · inbound

Solving Empirical Bayes via Transformers cites this paper.

Solving Empirical Bayes via Transformers Trained Transformers Learn Linear Models In-Context

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T20:21:58.426007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 726141bb-2a40-426a-9ef6-1ff457cecb61 · inbound

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse cites this paper.

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse Trained Transformers Learn Linear Models In-Context

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:11:02.235661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2fc7f14-4432-4a2a-a85f-c240aa9b09e9 · inbound

Learning Compositional Functions with Transformers from Easy-to-Hard Data cites this paper.

Learning Compositional Functions with Transformers from Easy-to-Hard Data Trained Transformers Learn Linear Models In-Context

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:40.385953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3d42c49-58b1-4a16-ad95-623512cf4413 · inbound

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression cites this paper.

Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression Trained Transformers Learn Linear Models In-Context

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T22:10:17.617037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8156a7f-8b4c-41ef-b776-a516f5be5b85 · inbound

How Can Mamba Learn In Context with Outliers and Generalize Provably? cites this paper.

How Can Mamba Learn In Context with Outliers and Generalize Provably? Trained Transformers Learn Linear Models In-Context

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T13:29:32.330348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 863b275c-17af-4ea1-88a6-a8937a55992e · inbound

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently cites this paper.

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently Trained Transformers Learn Linear Models In-Context

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T20:57:13.415074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21622bc8-051d-4b67-80dd-df411e07cb58 · inbound

Token Sample Complexity of Attention cites this paper.

Token Sample Complexity of Attention Trained Transformers Learn Linear Models In-Context

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T17:10:09.518256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2201271-7f22-4384-8742-54b34a4cdf71 · inbound

Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity cites this paper.

Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity Trained Transformers Learn Linear Models In-Context

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T22:19:23.982015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 619b25d3-97f2-484c-88f5-2bbf04756de9 · inbound

Visual prompting reimagined: The power of the Activation Prompts cites this paper.

Visual prompting reimagined: The power of the Activation Prompts Trained Transformers Learn Linear Models In-Context

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:45:53.683652Z

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.

Observation dd82a22e-8adb-450e-a7ab-3b2b72338472 · inbound

Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs cites this paper.

Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs Trained Transformers Learn Linear Models In-Context

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:02.651960Z

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.

Observation 62c1cd92-f390-4f23-b42b-c9910c813472 · inbound

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks cites this paper.

Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks Trained Transformers Learn Linear Models In-Context

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:45:28.160280Z

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.

Observation adf96f5d-df37-4882-ae34-985bca3dd4b1 · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Trained Transformers Learn Linear Models In-Context

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:56:32.047335Z

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.

Observation 04269abe-6acd-4a0e-9c06-0be4c81a5a68 · inbound

Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.

Structure Before Collapse: Transient semantic geometry in next-token prediction Trained Transformers Learn Linear Models In-Context

Reference 124

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T13:29:50.993590Z

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.

Observation cdf6aacb-619a-46a5-ba46-6fec254b322e · inbound

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models cites this paper.

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models Trained Transformers Learn Linear Models In-Context

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T23:43:11.185169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbe38d16-fb9e-447c-83a6-8ebada201829 · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex Trained Transformers Learn Linear Models In-Context

Reference 230

Resolution: unresolved; no resolver link, observed 2026-07-31T23:52:10.128256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.