Paper Citation Record · LEDGER

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2311.00871.

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

pith.paper-citation-record.v1
2311.00871 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:49:53.729633Z

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

8
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 62377aec-c689-4e69-9860-f75936b24e17 · inbound

A Survey on In-context Learning cites this paper.

A Survey on In-context Learning Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T12:58:27.531557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a6396400-5d61-4d44-b894-69a32534e92b · inbound

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models cites this paper.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:49:53.729633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f45318f3-cef2-4aad-b3b3-bd89673c894c · inbound

Reverse Convolution and Its Applications to Image Restoration cites this paper.

Reverse Convolution and Its Applications to Image Restoration Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:40.619277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8af09c67-f019-4a48-81f4-43c641bc62c8 · inbound

Selective Induction Heads: How Transformers Select Causal Structures In Context cites this paper.

Selective Induction Heads: How Transformers Select Causal Structures In Context Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 29

Resolution: malformed identifier; no resolver link, observed 2026-08-04T21:11:45.526293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d911264-1561-445f-a98e-36f6d29df409 · inbound

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention cites this paper.

Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:23.215388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dce2508-c6ea-4192-bb02-c02967b538a2 · inbound

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off cites this paper.

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:26.228592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71389871-8ebc-4a80-9203-1586c1068376 · inbound

Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers cites this paper.

Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T07:15:11.064373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61b97eb3-1fc5-4c03-994e-3198a975978e · inbound

Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification cites this paper.

Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:11:04.020986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c7c9610-8721-45e3-9d8c-3007f8521b22 · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 273

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:17:54.489596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5a2e7b37-9a8a-4db3-a3ae-77e582594de5 · inbound

Consistency Training while Mitigating Obfuscation via Rate Matching cites this paper.

Consistency Training while Mitigating Obfuscation via Rate Matching Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-06-28T14:32:18.135024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6201558a-4a0c-4fa4-a280-86eac8ca2c96 · inbound

The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces cites this paper.

The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:07:21.141557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4771fc72-d96e-4a8e-a2ee-5002b911582f · inbound

Can In-Context Learning Support Intrinsic Curiosity? cites this paper.

Can In-Context Learning Support Intrinsic Curiosity? Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 81

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:19:13.125900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 167051eb-a671-4f62-a48c-d609bf3d034e · inbound

Induction Heads Interpolate N-Grams cites this paper.

Induction Heads Interpolate N-Grams Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T06:58:39.213932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe33d829-6f48-4ffb-beb9-adf931e618d8 · inbound

How Context Attribution Handles What the Model Already Knows cites this paper.

How Context Attribution Handles What the Model Already Knows Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 168

Resolution: unresolved; no resolver link, observed 2026-07-30T12:03:28.592234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.