Paper Citation Record · LEDGER

Rethinking Invariance in In-context Learning

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.04994.

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

pith.paper-citation-record.v1
2505.04994 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:28:00.513958Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

verified exact2
verified fuzzy16
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7a44d8e-e972-43dd-bd70-fb8a7fb0607a · outbound

This paper cites In-context Examples Selection for Machine Translation.

Rethinking Invariance in In-context Learning In-context Examples Selection for Machine Translation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.341042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 795d0f5d-f000-4300-b161-bbe5ea5a5a1c · outbound

This paper cites What learning algorithm is in-context learning? investigations with linear models.

Rethinking Invariance in In-context Learning What learning algorithm is in-context learning? investigations with linear models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.994807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff0ad8af-438e-4cb1-a746-c69641bdf4b0 · outbound

This paper cites Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection.

Rethinking Invariance in In-context Learning Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.353445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ffba7b-778d-4702-8537-2ea2f0a366db · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Rethinking Invariance in In-context Learning Pythia: A suite for analyzing large language models across training and scaling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.357258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b74b68-2541-43a7-bf42-d6cb6f59476e · outbound

This paper cites On the sample complexity of learning under geometric stability.

Rethinking Invariance in In-context Learning On the sample complexity of learning under geometric stability

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.976476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3322e31-adcd-4405-ab62-67643089e59c · outbound

This paper cites GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow , March 2021.

Rethinking Invariance in In-context Learning GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow , March 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.364801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca86f74-a25b-41c6-be85-f074b0e2b154 · outbound

This paper cites Language models are few-shot learners.

Rethinking Invariance in In-context Learning Language models are few-shot learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.368323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a7994c2-fb5c-4746-b600-8c0d79b1d235 · outbound

This paper cites Scaling in-context demonstrations with structured attention.

Rethinking Invariance in In-context Learning Scaling in-context demonstrations with structured attention

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.958617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd53c5b6-e005-4fe5-a995-1b0f0f55ed21 · outbound

This paper cites On the Relation between Sensitivity and Accuracy in In-context Learning.

Rethinking Invariance in In-context Learning On the Relation between Sensitivity and Accuracy in In-context Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.375106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efc3ca4d-3d10-4702-b24f-434a1c424020 · outbound

This paper cites Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes.

Rethinking Invariance in In-context Learning Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:28:00.660667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41fb39e4-7c55-4f81-96e8-6d6a2eb59020 · outbound

This paper cites Introduction to algorithms.

Rethinking Invariance in In-context Learning Introduction to algorithms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.384072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a25691f-d57a-4ab3-b22b-2784ad7fa403 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Rethinking Invariance in In-context Learning Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.387633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcb69e0f-5d45-496d-bf27-911738d9f7c5 · outbound

This paper cites CausalLM is not optimal for in-context learning.

Rethinking Invariance in In-context Learning CausalLM is not optimal for in-context learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:28:00.633934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b5751317-ef0f-4145-b9de-79db185dd133 · outbound

This paper cites Transformers learn higher-order optimization methods for in-context learning: A study with linear models.

Rethinking Invariance in In-context Learning Transformers learn higher-order optimization methods for in-context learning: A study with linear models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.939838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d074603-ab4f-4ee2-9269-500d508dbb9b · outbound

This paper cites What can transformers learn in-context? a case study of simple function classes.

Rethinking Invariance in In-context Learning What can transformers learn in-context? a case study of simple function classes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.928662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1dad8738-52bd-4b52-abb3-db512586b952 · outbound

This paper cites OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization.

Rethinking Invariance in In-context Learning OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.403259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 821e9a9c-c4cf-492f-aedd-ad2a5d090281 · outbound

This paper cites The impact of positional encoding on length generalization in transformers.

Rethinking Invariance in In-context Learning The impact of positional encoding on length generalization in transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.917459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec6a6a28-fc78-476e-a08d-d708b3274d94 · outbound

This paper cites In-context learning learns label relationships but is not conventional learning.

Rethinking Invariance in In-context Learning In-context learning learns label relationships but is not conventional learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.906423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 590420cf-98a5-4982-9217-c1e7f2416b3b · outbound

This paper cites an unresolved cited work.

Rethinking Invariance in In-context Learning Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:28:00.895737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8365bc9-d41f-4211-b8f1-fbd52fe85acd · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.

Rethinking Invariance in In-context Learning Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.418144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70bf9c7e-3f2a-47b5-b9ea-346a55d84482 · outbound

This paper cites Noisy channel language model prompting for few-shot text classification.

Rethinking Invariance in In-context Learning Noisy channel language model prompting for few-shot text classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.878656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2a3951c-4e1e-4b0e-84c3-08ef43f69276 · outbound

This paper cites Metaicl: Learning to learn in context.

Rethinking Invariance in In-context Learning Metaicl: Learning to learn in context

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.867383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a35c9ee-9431-4b19-a02b-f81db30fb5fa · outbound

This paper cites Improving language understanding by generative pre-training.

Rethinking Invariance in In-context Learning Improving language understanding by generative pre-training

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.428736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d400d65f-3b7b-4d89-b45c-1f3f681b34bb · outbound

This paper cites Language models are unsupervised multitask learners.

Rethinking Invariance in In-context Learning Language models are unsupervised multitask learners

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.850355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ae1b5f8-d903-41da-ae12-4b1fad490240 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Rethinking Invariance in In-context Learning Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.436583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad73a517-8080-4b40-bba5-ba3479ae6683 · outbound

This paper cites Parallel Context Windows for Large Language Models.

Rethinking Invariance in In-context Learning Parallel Context Windows for Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.440018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 587ef7f1-ea5f-4514-8ff5-3ffc1576d725 · outbound

This paper cites Do pretrained Transformers Learn In-Context by Gradient Descent?.

Rethinking Invariance in In-context Learning Do pretrained Transformers Learn In-Context by Gradient Descent?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.444643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe9a095c-b170-4dd7-ad8b-653cf6d4b071 · outbound

This paper cites Giryes, G.

Rethinking Invariance in In-context Learning Giryes, G

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.832466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a97d5010-dd75-4c34-8c8c-1b71dcaccaff · outbound

This paper cites The exact sample complexity gain from invariances for kernel regression.

Rethinking Invariance in In-context Learning The exact sample complexity gain from invariances for kernel regression

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.819890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ebdcd352-5107-4741-bdff-3e03d8c54408 · outbound

This paper cites Attention is all you need.

Rethinking Invariance in In-context Learning Attention is all you need

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.455347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d9f32d-ff63-4897-b23a-d0822a552be6 · outbound

This paper cites Transformers learn in-context by gradient descent.

Rethinking Invariance in In-context Learning Transformers learn in-context by gradient descent

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.801160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8323610-fdca-4c13-b549-20c9651538da · outbound

This paper cites Uncovering mesa-optimization algorithms in Transformers.

Rethinking Invariance in In-context Learning Uncovering mesa-optimization algorithms in Transformers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.462114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a0c6fff-d434-4430-abd4-e6d18446cf4e · outbound

This paper cites Can in-context learning really generalize to out-of-distribution tasks? In ICLR, 2025.

Rethinking Invariance in In-context Learning Can in-context learning really generalize to out-of-distribution tasks? In ICLR, 2025

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.789683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3256133-2fc4-4b91-a4d0-5d36a48b52cf · outbound

This paper cites A theoretical understanding of self-correction through in-context alignment.

Rethinking Invariance in In-context Learning A theoretical understanding of self-correction through in-context alignment

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.469141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b7bfbfe-61b5-47b8-8f96-af5c37c0cab1 · outbound

This paper cites Finetuned language models are zero-shot learners.

Rethinking Invariance in In-context Learning Finetuned language models are zero-shot learners

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.771436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3825555-f7b2-4fcd-83be-16bccd3c74ca · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Rethinking Invariance in In-context Learning Chain-of-thought prompting elicits reasoning in large language models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.476021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7771b929-cbda-47aa-b699-530ad5b3d32d · outbound

This paper cites Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering.

Rethinking Invariance in In-context Learning Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.479396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36c53537-ed1f-4309-8577-facd1534868b · outbound

This paper cites Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models.

Rethinking Invariance in In-context Learning Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.483122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c26a0be6-e398-4909-a1b8-54855541a809 · outbound

This paper cites An explanation of in-context learning as implicit bayesian inference.

Rethinking Invariance in In-context Learning An explanation of in-context learning as implicit bayesian inference

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:28:00.753763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ca55e77-6a04-4f05-89b0-634813d4ad4c · outbound

This paper cites Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning.

Rethinking Invariance in In-context Learning Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.489971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ee7e0a-864f-437a-b312-9a217bb4cc2f · outbound

This paper cites Calibrate before use: Improving few-shot performance of language models.

Rethinking Invariance in In-context Learning Calibrate before use: Improving few-shot performance of language models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.493571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e1047a-1a13-4bff-8b25-072ad005c440 · outbound

This paper cites an unresolved cited work.

Rethinking Invariance in In-context Learning Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:28:00.735565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 37f99092-5824-432c-b212-125352c36d2b · outbound

This paper cites write newline.

Rethinking Invariance in In-context Learning write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.500782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fee94fb7-a8d7-42ee-876b-e9aa1c280ef0 · outbound

This paper cites @esa (Ref.

Rethinking Invariance in In-context Learning @esa (Ref

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.505383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7283c5-8068-4b85-a14e-52c02b2eb70a · outbound

This paper cites an unresolved cited work.

Rethinking Invariance in In-context Learning Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.509844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c08f4bea-5577-489d-8ec4-47b159f5f0b5 · outbound

This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

Rethinking Invariance in In-context Learning Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T23:28:00.513958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.