Estimating the Effects of Sample Training Orders for Large Language Models without Retraining | Paper Citation Record

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This paper cites Critical learning periods in deep networks.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Critical learning periods in deep networks

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This paper cites If influence functions are the answer, then what is the question?Advances in Neural Information Processing Systems, 35:17953–17967, 2022.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining If influence functions are the answer, then what is the question?Advances in Neural Information Processing Systems, 35:17953–17967, 2022

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Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Influence functions in deep learning are fragile

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This paper cites Curriculum learning.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning

Reference 4

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

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Pythia: A suite for analyzing large language models across training and scaling

Reference 5

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Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Unresolved cited work

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This paper cites Generalization potential of large language models.Neural Computing and Applications, 37(4):1973–1997, 2025.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Generalization potential of large language models.Neural Computing and Applications, 37(4):1973–1997, 2025

Reference 7

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This paper cites Curriculum learning for language modeling.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning for language modeling

Reference 8

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This paper cites Fast and accurate network embeddings via very sparse random projection.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Fast and accurate network embeddings via very sparse random projection

Reference 9

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This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 10

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This paper cites Zo- adamm: Zeroth-order adaptive momentum method for black-box optimization.Advances in neural information processing systems, 32, 2019.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Zo- adamm: Zeroth-order adaptive momentum method for black-box optimization.Advances in neural information processing systems, 32, 2019

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This paper cites Optimal rates for zero-order convex optimization: The power of two function evaluations.IEEE Transactions on Information Theory, 61(5):2788–2806, 2015.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Optimal rates for zero-order convex optimization: The power of two function evaluations.IEEE Transactions on Information Theory, 61(5):2788–2806, 2015

Reference 12

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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.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Systems, 33:2881–2891, 2020

Reference 13

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This paper cites Online convex optimization in the bandit setting: gradient descent without a gradient.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 14

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This paper cites The Early Phase of Neural Network Training.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining The Early Phase of Neural Network Training

Reference 15

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This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.SIAM journal on optimization, 23(4):2341–2368, 2013.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Stochastic first-and zeroth-order methods for nonconvex stochastic programming.SIAM journal on optimization, 23(4):2341–2368, 2013

Reference 16

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This paper cites Deep curriculum learning optimization.SN Computer Science, 1(5):245, 2020.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Deep curriculum learning optimization.SN Computer Science, 1(5):245, 2020

Reference 17

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This paper cites Gradientless Descent: High-Dimensional Zeroth-Order Optimization.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Reference 18

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This paper cites Training dynamics for text summarization models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Training dynamics for text summarization models

Reference 19

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This paper cites Au- tomated curriculum learning for neural networks.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Au- tomated curriculum learning for neural networks

Reference 20

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This paper cites Curriculum learning for facial expression recognition.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning for facial expression recognition

Reference 21

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This paper cites Fastif: Scalable influence functions for efficient model interpretation and debugging.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Fastif: Scalable influence functions for efficient model interpretation and debugging

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This paper cites On the power of curriculum learning in training deep networks.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining On the power of curriculum learning in training deep networks

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This paper cites Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?

Reference 24

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This paper cites A review on genetic algorithm: past, present, and future.Multimedia Tools and Applications, 80:8091 – 8126, 2020.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining A review on genetic algorithm: past, present, and future.Multimedia Tools and Applications, 80:8091 – 8126, 2020

Reference 25

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This paper cites Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning

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

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Understanding black-box predictions via influence functions

Reference 27

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

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining On the accuracy of influence functions for measuring group effects.Advances in neural information processing systems, 32, 2019

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This paper cites Crossover operators in genetic algorithms: A review.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Crossover operators in genetic algorithms: A review

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This paper cites Causal estimation of memorisation profiles.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Causal estimation of memorisation profiles

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This paper cites Environment curriculum generation via large language models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Environment curriculum generation via large language models

Reference 31

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This paper cites Token-wise influential training data retrieval for large language models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Token-wise influential training data retrieval for large language models

Reference 32

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This paper cites A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications.IEEE Signal Processing Magazine, 37(5):43–54, 2020.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications.IEEE Signal Processing Magazine, 37(5):43–54, 2020

Reference 33

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This paper cites Probing across time: What does roberta know and when? InFindings of the Association for Computational Linguistics: EMNLP 2021, pages 820–842, 2021.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Probing across time: What does roberta know and when? InFindings of the Association for Computational Linguistics: EMNLP 2021, pages 820–842, 2021

Reference 34

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This paper cites Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023

Reference 35

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This paper cites Teacher–student curriculum learning.IEEE transactions on neural networks and learning systems, 31(9):3732–3740, 2019.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Teacher–student curriculum learning.IEEE transactions on neural networks and learning systems, 31(9):3732–3740, 2019

Reference 36

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This paper cites Scalable language modeling: Wikitext-103 on a single gpu in 12 hours.Proceedings of the SYSML, 18, 2018.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Scalable language modeling: Wikitext-103 on a single gpu in 12 hours.Proceedings of the SYSML, 18, 2018

Reference 37

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This paper cites An Analysis of Neural Language Modeling at Multiple Scales.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining An Analysis of Neural Language Modeling at Multiple Scales

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This paper cites Curriculum Learning for Small Code Language Models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum Learning for Small Code Language Models

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This paper cites Random gradient-free minimization of convex functions.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Random gradient-free minimization of convex functions

Reference 40

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This paper cites DataMan: Data Manager for Pre-training Large Language Models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining DataMan: Data Manager for Pre-training Large Language Models

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This paper cites Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability.Advances in neural information processing systems, 30, 2017.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability.Advances in neural information processing systems, 30, 2017

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Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Sutherland

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This paper cites Training dynamics of neural language models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Training dynamics of neural language models

Reference 44

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This paper cites Understanding Learning Dynamics Of Language Models with SVCCA.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Understanding Learning Dynamics Of Language Models with SVCCA

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This paper cites On the dynamics of gender learning in speech translation.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining On the dynamics of gender learning in speech translation

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This paper cites Emergent structures and training dynamics in large language models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Emergent structures and training dynamics in large language models

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This paper cites Memorization without overfitting: Analyzing the training dynamics of large language models.Advances in Neural Information Processing Systems, 35:38274–38290, 2022.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Memorization without overfitting: Analyzing the training dynamics of large language models.Advances in Neural Information Processing Systems, 35:38274–38290, 2022

Reference 48

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining LLaMA: Open and Efficient Foundation Language Models

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This paper cites How hard can it be? estimating the difficulty of visual search in an image.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining How hard can it be? estimating the difficulty of visual search in an image

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This paper cites The johnson-lindenstrauss transform: an empirical study.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining The johnson-lindenstrauss transform: an empirical study

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This paper cites Curriculum learning for multimedia in the era of large language models.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning for multimedia in the era of large language models

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This paper cites Stochastic zeroth-order optimization in high dimensions.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Stochastic zeroth-order optimization in high dimensions

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This paper cites Curriculum learning by transfer learning: Theory and experiments with deep networks.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning by transfer learning: Theory and experiments with deep networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:22.056780Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1da6ce44-fc90-471b-ad73-730a07a88737 · outbound

This paper cites Curriculum learning for natural language understanding.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum learning for natural language understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:21.827040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6c20a177-9433-4e34-b74f-ac2a732b7db4 · outbound

This paper cites To repeat or not to repeat: Insights from scaling llm under token-crisis, 2023.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining To repeat or not to repeat: Insights from scaling llm under token-crisis, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:21.623817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7bb3c70-3c84-4182-9ab7-df105a1a1ed3 · outbound

This paper cites Dpzero: dimension- independent and differentially private zeroth-order optimization.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Dpzero: dimension- independent and differentially private zeroth-order optimization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:21.395411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 153fc756-b1ab-4a6f-a64e-04972164b57d · outbound

This paper cites An Empirical Exploration of Curriculum Learning for Neural Machine Translation.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining An Empirical Exploration of Curriculum Learning for Neural Machine Translation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:16.883475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26c1772e-475e-4b85-974d-9a4acc841ab0 · outbound

This paper cites FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:23:19.417173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03ff89cc-0680-49ab-9f54-8f90daec134b · outbound

This paper cites Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:23:19.216553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 026661ac-7dfe-46ca-8d12-6987f3b3b368 · outbound

This paper cites Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:17.231694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a189f7-9f1e-4d10-8f2a-a28696ee9c70 · outbound

This paper cites Billion-scale network embedding with iterative random projection.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Billion-scale network embedding with iterative random projection

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:21.194311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ad379b3-20a6-4b59-99db-860cf294c81b · outbound

This paper cites Curriculum Learning for Deep Generative Models with Clustering.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Curriculum Learning for Deep Generative Models with Clustering

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:23:18.970864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3afc1406-5e8b-463d-bd2c-23e0c2d3bfff · outbound

This paper cites An empirical study of memorization in nlp.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining An empirical study of memorization in nlp

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:21.047632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bf0e3eed-4ea1-415d-a4b1-a3f2c892a9a6 · outbound

This paper cites , T}}, with|ST |=T!.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining , T}}, with|ST |=T!

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:20.867902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1949c804-6cec-4d0f-8ce6-60671baa71ba · outbound

This paper cites Retain the top 50% individuals with the highest fitness scores for reproduction, and discard the rest.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Retain the top 50% individuals with the highest fitness scores for reproduction, and discard the rest

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:20.728079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d3aef4b-5319-4d42-9c99-85459cc190e5 · outbound

This paper cites Specifically, randomly choose two crossover points l and r such that 1≤l < r≤T , then exchange the subsequences πa l:r and πb l:r between the parents.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Specifically, randomly choose two crossover points l and r such that 1≤l < r≤T , then exchange the subsequences πa l:r and πb l:r between the parents

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:20.577025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03c17730-e14c-40b3-8d83-ef99cf9557ea · outbound

This paper cites This operation introduces diversity and prevents premature convergence.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining This operation introduces diversity and prevents premature convergence

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:20.423251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b23b15d1-2e89-4db3-9704-86e0d181c3ac · outbound

This paper cites The updated population then forms the basis for the next generation.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining The updated population then forms the basis for the next generation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:20.295019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af258ba9-0be5-4e55-ad45-dc83d3d26500 · outbound

This paper cites an unresolved cited work.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:23:20.167567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85ae5941-c52d-4bbc-9515-43189317bf57 · outbound

This paper cites an unresolved cited work.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:23:20.062878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bf735fc0-c025-4ee1-acb3-b85f6c372853 · outbound

This paper cites an unresolved cited work.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:23:19.840224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

Outbound references

Pith citing papers