Paper Citation Record · LEDGER

Optimization-Inspired Few-Shot Adaptation for Large Language Models

As of 19 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 2 inbound Pith citation observations for arXiv:2505.19107.

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

pith.paper-citation-record.v1
2505.19107 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:24:33.015656Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:26:41.396467Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:21:26.600875Z

Reference resolution

67 of 67 outbound references displayed

verified exact0
verified fuzzy45
unresolved19
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b3559ae4-e06a-4137-bdf3-c905f9054233 · outbound

This paper cites A mechanism for sample-efficient in-context learning for sparse retrieval tasks.

Optimization-Inspired Few-Shot Adaptation for Large Language Models A mechanism for sample-efficient in-context learning for sparse retrieval tasks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.670052Z

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Observation be2125a8-fded-47dc-80b3-300853d3e6dc · outbound

This paper cites Second-order stochastic optimization for machine learning in linear time.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Second-order stochastic optimization for machine learning in linear time

Reference 2

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Observation 2778b413-1326-4a07-9a8b-c17655a313e0 · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 3

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Observation a92eb846-6a8c-4540-b8ef-30f8a3d12db0 · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models What learning algorithm is in-context learning? investigations with linear models

Reference 4

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Observation 9e6eab1d-22fa-4373-a9b6-19daa395dde3 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Flamingo: a visual language model for few-shot learning

Reference 5

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Observation c44bf5d5-7e7e-4a5f-9b8d-3ef6da1955cc · outbound

This paper cites Infinite mixture prototypes for few-shot learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Infinite mixture prototypes for few-shot learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.610870Z

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Observation 9df63e4e-dcaa-4e22-b686-5f059bff7785 · outbound

This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Transformers as statisticians: Provable in-context learning with in-context algorithm selection

Reference 7

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Observation ffb8f333-b820-4383-9946-0686909be4c9 · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Unresolved cited work

Reference 8

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Observation c5bb5ffd-c4a7-402e-9a0b-8532699bf7af · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Optimization-Inspired Few-Shot Adaptation for Large Language Models RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 9

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Observation d7f14e89-b0f5-4f72-880f-ae25ac339cc5 · outbound

This paper cites Language models are few-shot learners.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Language models are few-shot learners

Reference 10

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Observation 1be7ff25-ccdf-4177-b660-d598468b753d · outbound

This paper cites Semeval- 2019 task 3: Emocontext contextual emotion detection in text.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Semeval- 2019 task 3: Emocontext contextual emotion detection in text

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.566847Z

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Observation c991737d-a3d7-4c07-8727-662515b5901a · outbound

This paper cites Evaluating large language models trained on code.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Evaluating large language models trained on code

Reference 12

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Observation fd930ef6-9930-436e-9165-206f35a1e2fb · outbound

This paper cites Training verifiers to solve math word problems.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Training verifiers to solve math word problems

Reference 13

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Observation 3b5f9650-bca0-4ed6-b0b5-4582b762fb54 · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 14

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Observation fe8aebae-eb7c-4b2e-ade4-f0e2c8900241 · outbound

This paper cites Hate Speech Dataset from a White Supremacy Forum.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Hate Speech Dataset from a White Supremacy Forum

Reference 15

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Observation f2c11d43-6914-4740-b177-cf1b2a320d15 · outbound

This paper cites Sharp minima can generalize for deep nets.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Sharp minima can generalize for deep nets

Reference 16

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Observation bf6eca25-568f-4e73-870a-3ebadee35583 · outbound

This paper cites Incorporat- ing second-order functional knowledge for better option pricing.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Incorporat- ing second-order functional knowledge for better option pricing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.515769Z

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Observation bcd2a03d-5fae-4ca1-8a45-bc4fe1d40554 · outbound

This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 18

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Observation 63a8df59-4ce2-411f-b205-7db3ec5ddb99 · outbound

This paper cites Model-agnostic meta-learning for fast adapta- tion of deep networks.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Model-agnostic meta-learning for fast adapta- tion of deep networks

Reference 19

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Observation 5fd7dcff-6d41-4e3f-8dad-30e156aec4ef · outbound

This paper cites Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, and Raia Hadsell.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, and Raia Hadsell

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.489731Z

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Observation 0cf7c002-fe86-4cbf-ad5a-d6c46620037c · outbound

This paper cites Sharpness-aware mini- mization for efficiently improving generalization.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Sharpness-aware mini- mization for efficiently improving generalization

Reference 21

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Observation cdbea759-9a17-480e-adf0-c471a07c1ee6 · outbound

This paper cites Transformers are Universal In-context Learners.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Transformers are Universal In-context Learners

Reference 22

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Observation 0cac3aeb-752d-41f0-995a-884dce0d5f2b · outbound

This paper cites The impact of initialization on lora finetuning dynamics.

Optimization-Inspired Few-Shot Adaptation for Large Language Models The impact of initialization on lora finetuning dynamics

Reference 23

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Source-reported events for the cited work

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Observation dd8d1cc6-9d43-4609-8a53-25e9fdcf92de · outbound

This paper cites In-context learning creates task vectors.

Optimization-Inspired Few-Shot Adaptation for Large Language Models In-context learning creates task vectors

Reference 24

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Observation 48aeb200-adee-4d25-b54a-cb232853cd94 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Lora: Low-rank adaptation of large language models

Reference 25

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Observation ff4dfc64-afe9-46d9-93a9-0fa4a117bc95 · outbound

This paper cites Transformers are minimax optimal nonparametric in-context learners.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Transformers are minimax optimal nonparametric in-context learners

Reference 26

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Observation 898888f2-67d7-4323-8e31-beadde182e38 · outbound

This paper cites Asam: Adaptive sharpness-aware minimization for scale-invariant learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Asam: Adaptive sharpness-aware minimization for scale-invariant learning

Reference 27

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Observation 32e71d81-f450-4a61-a288-c910223eff1b · outbound

This paper cites Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.405629Z

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Observation 3c5830a0-d947-4e2a-95b8-851e10420d52 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 29

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Observation 8080f72c-c72b-44d9-b91b-0a835f55ddbe · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models In-context learning state vector with inner and momentum optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:37.395186Z

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Observation 5d70d02e-8dea-4d80-b81a-5fdc020fe475 · outbound

This paper cites Learning question classification with support vector machines, 2002.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Learning question classification with support vector machines, 2002

Reference 31

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Observation 8139911d-4b87-42a7-9fc3-bbe236485f2b · outbound

This paper cites Meta-SGD: Learning to Learn Quickly for Few-Shot Learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 32

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Unresolved cited work

Reference 33

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Observation 4d314b92-62f4-4461-925c-51ee0e87441e · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 35

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Observation 2c9c90b5-3843-4afe-bbb1-715f056d3a18 · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

Optimization-Inspired Few-Shot Adaptation for Large Language Models DoRA: Weight-decomposed low-rank adaptation

Reference 36

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Observation df521e17-68c3-49c5-8af8-72e04c221c02 · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Codet: Code generation with generated tests

Reference 37

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Observation 69bc6131-fffc-4fd7-a1da-ab718081f0b0 · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Self-refine: Iterative refinement with self-feedback

Reference 38

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Observation df5f7737-ea00-4511-b333-aa8be7d87ae7 · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Pissa: Principal singular values and singular vectors adaptation of large language models

Reference 39

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Observation 3d213692-540a-4ccb-a12f-d2f80fbf9352 · outbound

This paper cites A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks.

Optimization-Inspired Few-Shot Adaptation for Large Language Models A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks

Reference 40

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Observation 58dd585a-6d4d-4c54-aef6-a68669ea6d99 · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models On First-Order Meta-Learning Algorithms

Reference 41

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Optimization-Inspired Few-Shot Adaptation for Large Language Models A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:36.171198Z

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.956375Z

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Meta-curvature

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.747673Z

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Observation 20dea83b-dc9f-44d1-a076-fae024e2c4d7 · outbound

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Optimization-Inspired Few-Shot Adaptation for Large Language Models Language models are unsupervised multitask learners

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.533381Z

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Observation 7f23ea0a-7d4a-4619-91e0-86a0a66510ec · outbound

This paper cites Language models are unsupervised multitask learners.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Language models are unsupervised multitask learners

Reference 46

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This paper cites Meta-learning with implicit gradients.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Meta-learning with implicit gradients

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.225813Z

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Observation 7f426ed3-8e94-47f7-bdae-7b24c7000e79 · outbound

This paper cites Studying the link between radio galaxies and AGN fuelling with relativistic hydrodynamic simulations of flickering jets.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Studying the link between radio galaxies and AGN fuelling with relativistic hydrodynamic simulations of flickering jets

Reference 48

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Observation 91f49d6f-26ff-4bbe-b62b-e91e0e6857e9 · outbound

This paper cites Large Language Models Encode Clinical Knowledge.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Large Language Models Encode Clinical Knowledge

Reference 49

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Observation 79d1aaf4-f42d-47d8-a172-654804e65f44 · outbound

This paper cites Prototypical networks for few-shot learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Prototypical networks for few-shot learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:35.139660Z

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Observation efe61a5f-be97-4f44-89ce-7b42c25fc644 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Recursive deep models for semantic compositionality over a sentiment treebank

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.999850Z

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Observation 63811d55-222b-4bad-b7ff-e5ab1c90fcc0 · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Learning to compare: Relation network for few-shot learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.929774Z

Source-reported events for the cited work

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Observation 59c6b209-efd1-452c-b3a0-6bf06c431ff8 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 53

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This paper cites Dismai-Bench: Benchmarking and designing generative models using disordered materials and interfaces.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Dismai-Bench: Benchmarking and designing generative models using disordered materials and interfaces

Reference 54

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Observation f1fbde41-6c3a-48a7-b0a9-e602427187a3 · outbound

This paper cites Matching networks for one shot learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Matching networks for one shot learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.769100Z

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Observation ad8b4c03-c6ac-4c7b-8824-b90fad6122fb · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models Transformers learn in-context by gradient descent

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.665309Z

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Observation 26fb3251-a41c-451b-be36-9e828975cda4 · outbound

This paper cites Label words are anchors: An information flow perspective for understanding in-context learning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Label words are anchors: An information flow perspective for understanding in-context learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.568691Z

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Observation 8c2fe295-f69c-449f-aa74-a77fa6fafbb8 · outbound

This paper cites Do prompt-based models really understand the meaning of their prompts? In NAACL, 2022.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Do prompt-based models really understand the meaning of their prompts? In NAACL, 2022

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.430207Z

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Observation 573f2202-3972-410c-a744-7f7e05de7997 · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models Chain of thought prompting elicits reasoning in large language models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.316363Z

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Observation d3761583-981b-45a7-b817-5b0202be0541 · outbound

This paper cites How many pretraining tasks are needed for in-context learning of linear regression? In ICLR, 2024.

Optimization-Inspired Few-Shot Adaptation for Large Language Models How many pretraining tasks are needed for in-context learning of linear regression? In ICLR, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.166242Z

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Observation 5264202b-5fb9-4930-814b-4583529d89ec · outbound

This paper cites Corda: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine-tuning.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Corda: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine-tuning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:34.065824Z

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Observation e9633d1b-1b47-4833-94bc-598158bd88d6 · outbound

This paper cites Free lunch for few-shot learning: Distribution calibration.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Free lunch for few-shot learning: Distribution calibration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:33.961554Z

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Observation 2a81cc69-cefe-488b-95e1-0d21eae7e3dd · outbound

This paper cites Improving generalization by controlling label-noise information in neural network weights.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Improving generalization by controlling label-noise information in neural network weights

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:33.807182Z

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Observation 64e0ab43-c59c-4348-a6ec-cb7aaf8dcca2 · outbound

This paper cites In-context learning of a linear transformer block: benefits of the mlp component and one-step gd initialization.

Optimization-Inspired Few-Shot Adaptation for Large Language Models In-context learning of a linear transformer block: benefits of the mlp component and one-step gd initialization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:24:33.706473Z

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Observation 73b9388a-dd11-47f0-9862-2d087be3685d · outbound

This paper cites Character-level convolutional networks for text classification.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Character-level convolutional networks for text classification

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:32.783537Z

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Observation 7ac638d4-512b-4247-8c8f-1f910a9ef528 · outbound

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

Optimization-Inspired Few-Shot Adaptation for Large Language Models Calibrate before use: Improving few-shot performance of language models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:32.901928Z

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Observation dd90d013-447f-4c98-9bba-edc3ea3cfde4 · outbound

This paper cites Dvornek, Sekhar Tatikonda, James S.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Dvornek, Sekhar Tatikonda, James S

Reference 67

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:24:33.557983Z

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Observation 5f4a2958-dc63-45be-9a3a-b7f4a79f7b5c · outbound

This paper cites an unresolved cited work.

Optimization-Inspired Few-Shot Adaptation for Large Language Models Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:24:35.326589Z

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Pith citing papers

Observation 7c2603bb-85c0-478b-959b-04c790beb017 · inbound

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Optimization-Inspired Few-Shot Adaptation for Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:51:06.273452Z

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Observation 89b917f5-8670-4863-a6e1-85cf89f2f603 · inbound

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Optimization-Inspired Few-Shot Adaptation for Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:26.603755Z

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