Paper Citation Record · LEDGER

Daunce: Data Attribution through Uncertainty Estimation

As of 17 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.23223.

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

pith.paper-citation-record.v1
2505.23223 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:56:17.433024Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

47 of 47 outbound references displayed

verified exact2
verified fuzzy16
unresolved29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7dcfebef-f3d0-4068-929c-c764e7efe60b · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Daunce: Data Attribution through Uncertainty Estimation Studying Large Language Model Generalization with Influence Functions

Reference 1

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Source-reported events for the cited work

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Observation 2d2d5552-028d-42b9-bd08-c93524517092 · outbound

This paper cites Understanding black-box predictions via influence functions.

Daunce: Data Attribution through Uncertainty Estimation Understanding black-box predictions via influence functions

Reference 2

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Source-reported events for the cited work

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Observation 2b5ce45c-e0d8-4a69-a457-761e0109c0a8 · outbound

This paper cites Resolving training biases via influence-based data relabeling.

Daunce: Data Attribution through Uncertainty Estimation Resolving training biases via influence-based data relabeling

Reference 3

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Observation f4c8b9c1-210b-435d-91a5-0cce53ad57ae · outbound

This paper cites FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging.

Daunce: Data Attribution through Uncertainty Estimation FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:56:18.269369Z

Source-reported events for the cited work

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Observation 55abdeae-16b6-42b7-8d79-79383b9b873c · outbound

This paper cites G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation.

Daunce: Data Attribution through Uncertainty Estimation G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation

Reference 5

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Observation c9cb0f3c-ed17-41e1-a95f-d38187aef1da · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Daunce: Data Attribution through Uncertainty Estimation LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 6

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Source-reported events for the cited work

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Observation ef595917-9f38-462e-a769-c35d976a6905 · outbound

This paper cites Influence selection for active learning.

Daunce: Data Attribution through Uncertainty Estimation Influence selection for active learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:21.302597Z

Source-reported events for the cited work

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Observation e7b1d77b-f26e-4d4a-96cf-3b70d92e2151 · outbound

This paper cites What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

Daunce: Data Attribution through Uncertainty Estimation What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Reference 8

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Source-reported events for the cited work

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Observation b2bc8501-9aff-475e-bc36-6c6ed58eb579 · outbound

This paper cites On the accuracy of influence functions for measuring group effects.Advances in neural information processing systems, 32, 2019.

Daunce: Data Attribution through Uncertainty Estimation On the accuracy of influence functions for measuring group effects.Advances in neural information processing systems, 32, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:21.018244Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9ed25442-d03e-42f0-8813-5f3587828d28 · outbound

This paper cites Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method.Journal of Nuclear Science and Technology, 52 (7-8):993–999, 2015.

Daunce: Data Attribution through Uncertainty Estimation Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method.Journal of Nuclear Science and Technology, 52 (7-8):993–999, 2015

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:20.844496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88f46038-3f3c-4486-ac25-57eb4700cc88 · outbound

This paper cites Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning.

Daunce: Data Attribution through Uncertainty Estimation Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

Reference 11

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Observation 00b53dba-d8fa-4c29-ad73-3b0b5c8d50d1 · outbound

This paper cites Corruption-robust offline reinforcement learning with general function approximation.Advances in Neural Information Processing Systems, 36:36208–36221, 2023.

Daunce: Data Attribution through Uncertainty Estimation Corruption-robust offline reinforcement learning with general function approximation.Advances in Neural Information Processing Systems, 36:36208–36221, 2023

Reference 12

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Source-reported events for the cited work

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Observation 9ef71601-b08f-4b94-b5e9-5884ce765449 · outbound

This paper cites Training data influence analysis and estimation: A survey.Machine Learning, 113(5):2351–2403, 2024.

Daunce: Data Attribution through Uncertainty Estimation Training data influence analysis and estimation: A survey.Machine Learning, 113(5):2351–2403, 2024

Reference 13

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Source-reported events for the cited work

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Observation 80fc41d0-17ec-41ec-9748-d54835f4572c · outbound

This paper cites Training Data Attribution via Approximate Unrolled Differentiation.

Daunce: Data Attribution through Uncertainty Estimation Training Data Attribution via Approximate Unrolled Differentiation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:14.438057Z

Source-reported events for the cited work

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Observation 06d4e721-895a-4085-bd7b-42c125097206 · outbound

This paper cites TRAK: Attributing Model Behavior at Scale.

Daunce: Data Attribution through Uncertainty Estimation TRAK: Attributing Model Behavior at Scale

Reference 15

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Observation cb50e510-e933-4a9b-910e-8cfc23245976 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Systems, 33:2881–2891, 2020.

Daunce: Data Attribution through Uncertainty Estimation What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Systems, 33:2881–2891, 2020

Reference 16

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Source-reported events for the cited work

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Observation 3ff4be62-492d-42ae-9f86-a60c48bb2f63 · outbound

This paper cites Datamodels: Predicting Predictions from Training Data.

Daunce: Data Attribution through Uncertainty Estimation Datamodels: Predicting Predictions from Training Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:14.605035Z

Source-reported events for the cited work

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Observation 793e4a35-dac3-4978-b353-84c80f5eca59 · outbound

This paper cites Data shapley: Equitable valuation of data for machine learning.

Daunce: Data Attribution through Uncertainty Estimation Data shapley: Equitable valuation of data for machine learning

Reference 18

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Observation 3741686f-3f82-4de1-b247-82ac12ce7fba · outbound

This paper cites Data banzhaf: A robust data valuation framework for machine learning.

Daunce: Data Attribution through Uncertainty Estimation Data banzhaf: A robust data valuation framework for machine learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:14.717981Z

Source-reported events for the cited work

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Observation 09db8626-412a-4098-8678-483f598e822b · outbound

This paper cites Fast rates in pool-based batch active learning.Journal of Machine Learning Research, 25(262):1–42, 2024.

Daunce: Data Attribution through Uncertainty Estimation Fast rates in pool-based batch active learning.Journal of Machine Learning Research, 25(262):1–42, 2024

Reference 20

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Source-reported events for the cited work

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Observation 8c82670d-ae58-49f1-9e0c-9ce48e1ab431 · outbound

This paper cites Corruption-robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes.

Daunce: Data Attribution through Uncertainty Estimation Corruption-robust algorithms with uncertainty weighting for nonlinear contextual bandits and markov decision processes

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:20.351591Z

Source-reported events for the cited work

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Observation b3098791-d239-4879-b970-fbb0ec2bea9e · outbound

This paper cites A new active labeling method for deep learning.

Daunce: Data Attribution through Uncertainty Estimation A new active labeling method for deep learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:20.180075Z

Source-reported events for the cited work

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Observation 95927277-b537-4b9c-b6fc-f45e081991d0 · outbound

This paper cites Leveraging Importance Weights in Subset Selection.

Daunce: Data Attribution through Uncertainty Estimation Leveraging Importance Weights in Subset Selection

Reference 23

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Source-reported events for the cited work

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Observation 0d93982c-4292-404e-bcdf-3d1141b82996 · outbound

This paper cites Reducing labeling effort for structured prediction tasks.

Daunce: Data Attribution through Uncertainty Estimation Reducing labeling effort for structured prediction tasks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.980499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75b9063e-612e-4136-bfb0-737a53a0c2a4 · outbound

This paper cites Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Daunce: Data Attribution through Uncertainty Estimation Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Reference 25

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Observation e74cc6e4-52dc-4c3e-a0c0-15929519946c · outbound

This paper cites Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption.

Daunce: Data Attribution through Uncertainty Estimation Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:15.372995Z

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Observation 90d859ed-3fbc-4445-aedb-e2f4efbab2fc · outbound

This paper cites Bootstrap standard error estimates for linear regression.Journal of the American Statistical Association, 100(471):970–979, 2005.

Daunce: Data Attribution through Uncertainty Estimation Bootstrap standard error estimates for linear regression.Journal of the American Statistical Association, 100(471):970–979, 2005

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.766909Z

Source-reported events for the cited work

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Observation 336449cb-b5f3-4684-9d4d-f7933f72f761 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Daunce: Data Attribution through Uncertainty Estimation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 28

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Observation 265fc371-e1cb-44d2-bb4c-c8f6102d8999 · outbound

This paper cites Limitations of the empirical fisher approximation for natural gradient descent.Advances in neural information processing systems, 32, 2019.

Daunce: Data Attribution through Uncertainty Estimation Limitations of the empirical fisher approximation for natural gradient descent.Advances in neural information processing systems, 32, 2019

Reference 29

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Source-reported events for the cited work

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Observation cc534fe3-eb05-48e6-8e2d-b578aab4da79 · outbound

This paper cites Learning multiple layers of features from tiny images.

Daunce: Data Attribution through Uncertainty Estimation Learning multiple layers of features from tiny images

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:15.703340Z

Source-reported events for the cited work

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Observation 33c1e89f-edb1-4973-8226-6a3f27a02469 · outbound

This paper cites Deep residual learning for image recognition.

Daunce: Data Attribution through Uncertainty Estimation Deep residual learning for image recognition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.658780Z

Source-reported events for the cited work

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Observation d60efe43-07bd-4d72-8b60-5d9d5606cd3c · outbound

This paper cites Relatif: Identifying explanatory training samples via relative influence.

Daunce: Data Attribution through Uncertainty Estimation Relatif: Identifying explanatory training samples via relative influence

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.439735Z

Source-reported events for the cited work

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Observation 5301835d-0a04-462a-8954-95dcd7a56451 · outbound

This paper cites Scalable influence and fact tracing for large language model pretraining.

Daunce: Data Attribution through Uncertainty Estimation Scalable influence and fact tracing for large language model pretraining

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.303911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ccb1c0e2-720e-4342-8047-f0cb3e0ea71d · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 2022.

Daunce: Data Attribution through Uncertainty Estimation Lora: Low-rank adaptation of large language models.ICLR, 2022

Reference 34

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Observation 16449e39-d37f-4d1d-b216-43dfd790975b · outbound

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Daunce: Data Attribution through Uncertainty Estimation Qwen2.5 Technical Report

Reference 35

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Observation e55f62b6-27a0-4102-bfaa-9e20eb4d721e · outbound

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Daunce: Data Attribution through Uncertainty Estimation Numinamath

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:19.095811Z

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Observation 3e41cd7c-ddc2-48a1-8fb1-f5d841277a4d · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Daunce: Data Attribution through Uncertainty Estimation Measuring Mathematical Problem Solving With the MATH Dataset

Reference 37

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Observation dbdb202b-46a5-4abf-8ad2-74ee195aca5b · outbound

This paper cites The Llama 3 Herd of Models.

Daunce: Data Attribution through Uncertainty Estimation The Llama 3 Herd of Models

Reference 38

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Observation 19aabddc-d6d5-408b-923b-1fb53dc471f4 · outbound

This paper cites Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models.

Daunce: Data Attribution through Uncertainty Estimation Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models

Reference 39

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Observation 94874db0-ac34-43e0-894a-a9a6fb742079 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Daunce: Data Attribution through Uncertainty Estimation Instruction-Following Evaluation for Large Language Models

Reference 40

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Daunce: Data Attribution through Uncertainty Estimation Black-box prompt learning for pre-trained language models.Trans

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:18.994987Z

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Observation 51ca5ca6-b6bc-484c-bbd9-f0e834c87dee · outbound

This paper cites Black-box tuning for language- model-as-a-service.

Daunce: Data Attribution through Uncertainty Estimation Black-box tuning for language- model-as-a-service

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:18.747065Z

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Observation d09f2876-c023-4961-9694-52dca03e978b · outbound

This paper cites CombLM: Adapting black-box language models through small fine-tuned models.

Daunce: Data Attribution through Uncertainty Estimation CombLM: Adapting black-box language models through small fine-tuned models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:56:18.539752Z

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Observation 36158544-47bc-452a-8784-fbfd3bea8b1b · outbound

This paper cites Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930, 2020.

Daunce: Data Attribution through Uncertainty Estimation Estimating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930, 2020

Reference 44

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Observation b89bbc68-dbf9-464b-8b0b-4cc345126973 · outbound

This paper cites Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models.

Daunce: Data Attribution through Uncertainty Estimation Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models

Reference 45

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Observation 58b5a81b-e7b2-4d27-920b-f3f9343622ce · outbound

This paper cites BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models.

Daunce: Data Attribution through Uncertainty Estimation BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:17.320422Z

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Observation 5dde12c3-1d49-4daf-9114-0a3deaa672c3 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Daunce: Data Attribution through Uncertainty Estimation Measuring Massive Multitask Language Understanding

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:17.433024Z

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Pith citing papers

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