Paper Citation Record · LEDGER

Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2206.08082.

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

pith.paper-citation-record.v1
2206.08082 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:44:32.474657Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:56:38.835700Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 47943f11-022f-4da2-a191-9f1339aadd19 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:22:08.925526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80f18f7e-4beb-4140-85ab-dfeafcfe8d73 · inbound

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation cites this paper.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:44:32.474657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e012f182-8bed-4a74-8c98-df4fa7a9e924 · inbound

CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer cites this paper.

CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:37:14.982735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 538d8dac-eded-4430-8c4f-ee401fe172c1 · inbound

Exploring In-context Example Generation for Machine Translation cites this paper.

Exploring In-context Example Generation for Machine Translation Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:08:52.235364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96e119f7-bc5b-43dd-91d9-023d68e8f65f · inbound

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks cites this paper.

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:40.369919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfe3cbf8-8579-431f-9866-5ae455f89c53 · inbound

Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions cites this paper.

Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:44.586120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8458b72f-adb3-4b56-b38c-f2a8bcee1336 · inbound

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis cites this paper.

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:13.317108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb3c53a-5ebc-4fb5-8337-d076160092a5 · inbound

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer cites this paper.

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T10:43:46.336061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ac3a5f9-3ea5-4896-8ec3-0ad001d41a76 · inbound

Learning to Select Visual In-Context Demonstrations cites this paper.

Learning to Select Visual In-Context Demonstrations Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T05:46:06.091706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f47fab1-4938-4f01-8742-0e1765a30237 · inbound

Experience Transfer for Multimodal LLM Agents in Minecraft Game cites this paper.

Experience Transfer for Multimodal LLM Agents in Minecraft Game Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:25:51.447888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf0265c8-38a6-499b-8adb-98c23b483dd2 · inbound

The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure cites this paper.

The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:03:12.480555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ee98413-0274-4b28-9efa-7e6cf09a7764 · inbound

Self-Improving In-Context Learning cites this paper.

Self-Improving In-Context Learning Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:56:38.839649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.