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Paper Citation Record · LEDGER

Meta-learning via Language Model In-context Tuning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2110.07814.

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

pith.paper-citation-record.v1
2110.07814 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:41:49.920096Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:06:43.721106Z

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 b57ec3a4-e3b7-4cfe-8629-287ffaf164de · inbound

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cites this paper.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Meta-learning via Language Model In-context Tuning

Reference 196

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:51:46.832769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T09:51:46.701149Z digest=sha256:73b79c7be8488f91145933067d31a0e3958e80080d7c9116707c7a713654de96

Observation 7c06fa7a-0692-4142-9335-01d3f16b6f1b · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Meta-learning via Language Model In-context Tuning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T22:41:49.920096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:41:49.920096Z digest=sha256:2bf74358f6196a73b0eea9057bafaa09dcf33d5ab3844796fd4eb0957a676c5b

Observation 3c914249-4c86-4330-9d8d-39768d667a13 · 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 Meta-learning via Language Model In-context Tuning

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-06T21:38:16.542685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:38:16.542685Z digest=sha256:924ec547b55bd686ae23760299d96115bc59cd0298134a71685176b0fbf48212

Observation 684c41c3-b8ba-44e1-8b17-fc71f6fb16c8 · inbound

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation cites this paper.

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation Meta-learning via Language Model In-context Tuning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T00:55:55.744331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:55:55.744331Z digest=sha256:e62440150aac2995286cd650fcfd1acb17c94337986d7fba4a27d428117ea5e5

Observation c4b6315d-6c41-45c4-9284-c4285f8cc244 · inbound

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning cites this paper.

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning Meta-learning via Language Model In-context Tuning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T11:17:08.409472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T11:17:08.409472Z digest=sha256:2be2871cd71ae0b00c61aab148ce3a52eb716e4c421694e52266159e86afd5d7

Observation f3cdf791-d74d-4b7c-ac0a-fd9cf4275782 · inbound

Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation cites this paper.

Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation Meta-learning via Language Model In-context Tuning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:41:00.280114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T15:54:28.388649Z digest=sha256:8f477ea4f0b29fe66bdee68af4d7918e7b36c4aed30a296fa70a36068839f6c9

Observation 74ca0fe7-692a-4a8d-9cfa-3605c4390495 · inbound

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models cites this paper.

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models Meta-learning via Language Model In-context Tuning

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:10:21.804426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T12:08:25.251230Z digest=sha256:281c7a7295f11d66721a2cac2bb2a2c06650a1708f117a4a91b1bf51db40dc04

Observation 44e299e0-475e-48a7-9836-a6720faa59d5 · inbound

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models cites this paper.

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models Meta-learning via Language Model In-context Tuning

Reference 133

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T07:06:43.722739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T07:14:26.441339Z digest=sha256:fad7b29334094f04bdbd08a54ac2de7474490b7978de04fabb6bb621c989f933