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Paper Citation Record · LEDGER

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2205.05055.

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

pith.paper-citation-record.v1
2205.05055 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:58:33.247733Z

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

52
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 846f9e1e-8b42-444b-9941-d020166a3b2f · inbound

Emergent Abilities of Large Language Models cites this paper.

Emergent Abilities of Large Language Models Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T07:38:37.918130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-11T07:38:37.734402Z digest=sha256:f7a6c679da50d03bf5d7381817595cd6963aa6459f5af024b7382a9f7919c548

Observation 98fa9fe2-fd9d-4626-880f-7f2610af7600 · inbound

What learning algorithm is in-context learning? Investigations with linear models cites this paper.

What learning algorithm is in-context learning? Investigations with linear models Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T13:40:33.959718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-17T13:40:33.889712Z digest=sha256:ebb79ee308d1589c85c6012cfa5d0cddaabfac95f03db246b9f737addd472e49

Observation e1cd5043-7575-43aa-aa92-0dceebb0e4ba · inbound

A Survey on Large Language Models with some Insights on their Capabilities and Limitations cites this paper.

A Survey on Large Language Models with some Insights on their Capabilities and Limitations Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 151

Resolution
unresolved
no resolver link, observed 2026-08-10T22:17:55.965179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:17:55.965179Z digest=sha256:725733b1c782640a1077ea30c4857ef2a88b0e49ff9dbb18ca712a6b06deda2b

Observation 5ee8578b-487e-45d9-885b-244d93f5862f · inbound

ICL CIPHERS: Quantifying "Learning" in In-Context Learning via Substitution Ciphers cites this paper.

ICL CIPHERS: Quantifying "Learning" in In-Context Learning via Substitution Ciphers Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-16T05:58:33.247733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T05:58:33.247733Z digest=sha256:7fb8a03e5d8abf7491109a8561ae20882773c70bea506ef460dd4c37185e436e

Observation 705639cc-4779-47f9-8337-93aed28b3e82 · inbound

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence cites this paper.

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T15:00:53.951093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:00:53.951093Z digest=sha256:7ff0f3ac766e7ae2d1631fd46d7e626c23713ac486384f0aa7137e37d2464e26

Observation f124717c-d578-4f3a-aeba-a94ac95f1686 · inbound

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models cites this paper.

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-06T21:38:15.949632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:38:15.949632Z digest=sha256:92d127392dca14c2037779eff03debb29d9db5a9a31ebc19ac030fcea4a99d1f

Observation 20be7d55-186f-4c11-abad-2197511b123d · inbound

Can Interpretation Predict Behavior on Unseen Data? cites this paper.

Can Interpretation Predict Behavior on Unseen Data? Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T19:10:28.154041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:10:28.154041Z digest=sha256:09684d5e810eefde8188112ba632bf40c6f04bcbebf4958032105658f3acc2b6

Observation 7db2d627-b3d5-464d-9bfa-9586837424c6 · inbound

Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning cites this paper.

Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-06T11:16:36.556619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:16:36.556619Z digest=sha256:1561b266eead030d8e7d1ec6a4563315e5be45df4d9ad26fc763d62729bc09bd

Observation cc887d98-fdb6-4f8f-b97e-be3a2a2778b5 · inbound

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity cites this paper.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T14:44:28.065789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:44:28.065789Z digest=sha256:f889531445bc973fcdc62b371c55aa691b398e7f813edb49a44d727e06f46d47

Observation 174cb64a-d71a-4b89-a947-4d1f0141c208 · inbound

PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval cites this paper.

PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-17T21:35:17.621368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-17T21:33:22.644753Z digest=sha256:d9a4ce790057f643cb26838c444c2908fd3b2622b7c926ecafd4053db3d9b234

Observation 24a9053d-893f-4e97-b11b-01144b9bc739 · inbound

The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology cites this paper.

The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T16:07:08.153791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-07-02T15:57:48.589980Z digest=sha256:8838eef65393640cf44055901cedaeef74aab388d26579d1a67479f7c9e97cfe