Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning | Paper Citation Record

Observation a8017fb1-2e1d-4132-ab12-07fe79c50313 · outbound

This paper cites GPT-4 Technical Report.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning GPT-4 Technical Report

Reference 1

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Observation 64ff19d5-9f0c-40a0-8ddb-371d637b9fd8 · outbound

This paper cites Program synthesis with large language models,.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Program synthesis with large language models,

Reference 2

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Observation c6383f34-fcd3-4d72-9c85-f04d6f6fc320 · outbound

This paper cites Synthetic and natural noise both break neural machine translation.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Synthetic and natural noise both break neural machine translation

Reference 3

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Observation af64a94e-a360-448e-a6c1-55273f529e57 · outbound

This paper cites Instruction mining: Instruction data selection for tuning large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Instruction mining: Instruction data selection for tuning large language models

Reference 4

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This paper cites Alpagasus: Training a better alpaca with fewer data.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Alpagasus: Training a better alpaca with fewer data

Reference 5

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This paper cites Evaluating Large Language Models Trained on Code.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Evaluating Large Language Models Trained on Code

Reference 6

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Observation dcb52a8a-fa5f-4e88-82ec-173db342e596 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Training Verifiers to Solve Math Word Problems

Reference 7

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Observation 2ce42f07-e046-49af-b740-a91ab8df5261 · outbound

This paper cites SaulLM-7B: A pioneering Large Language Model for Law.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning SaulLM-7B: A pioneering Large Language Model for Law

Reference 8

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This paper cites Opencompass: A universal evaluation platform for foundation models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Opencompass: A universal evaluation platform for foundation models

Reference 9

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Observation 5e43cc12-4ca4-400e-aa0d-7b6273ca274b · outbound

This paper cites Auggpt: Leveraging chatgpt for text data augmentation.IEEE Transactions on Big Data, 2025.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Auggpt: Leveraging chatgpt for text data augmentation.IEEE Transactions on Big Data, 2025

Reference 10

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Observation 800f6515-9b60-4c4a-8258-06dbde6f080b · outbound

This paper cites MoDS: Model-oriented Data Selection for Instruction Tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning MoDS: Model-oriented Data Selection for Instruction Tuning

Reference 11

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Observation 89175dfa-d8f6-479c-af89-7dc6290407db · outbound

This paper cites The Llama 3 Herd of Models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning The Llama 3 Herd of Models

Reference 12

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Observation 41748293-7e56-485e-b8f8-4fcf640998d9 · outbound

This paper cites Understanding back-translation at scale.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Understanding back-translation at scale

Reference 13

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This paper cites A strategic coordination framework of small LMs matches large LMs in data synthesis.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning A strategic coordination framework of small LMs matches large LMs in data synthesis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:47.864522Z

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Observation 42cfd222-065e-48ae-a358-ca2839290159 · outbound

This paper cites Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 15

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This paper cites Measuring massive multitask language understanding.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Measuring massive multitask language understanding

Reference 16

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Observation b43fa31d-8d15-4189-bc7c-bc6fd2e183b0 · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Measuring mathematical problem solving with the MATH dataset

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:06.600924Z

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Observation 230e95de-d656-4f08-be23-1fc3692e095a · outbound

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Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 18

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Observation 661e6b99-0dc0-42b8-a46d-46840bce793d · outbound

This paper cites Boosting LLM via Learning from Data Iteratively and Selectively.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Boosting LLM via Learning from Data Iteratively and Selectively

Reference 19

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Observation d00aa0ce-d1f0-4bfa-bea4-7c5b645f2a0d · outbound

This paper cites GPT-4o System Card.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning GPT-4o System Card

Reference 20

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This paper cites Impossible distillation for paraphrasing and summarization: How to make high-quality lemonade out of small, low-quality model.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Impossible distillation for paraphrasing and summarization: How to make high-quality lemonade out of small, low-quality model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:06.440813Z

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Observation 0b958245-96b9-4969-8389-c43b6b7bdea9 · outbound

This paper cites Mistral 7B.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Mistral 7B

Reference 22

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Observation 5c72a8e7-2800-4c9a-aa04-2a104998d580 · outbound

This paper cites Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:06.107922Z

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Observation c54fe1ad-cf5f-47cc-b047-52d42c693555 · outbound

This paper cites Dataenvgym: Data generation agents in teacher environments with student feedback.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Dataenvgym: Data generation agents in teacher environments with student feedback

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:06.267613Z

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This paper cites The bigscience roots corpus: A 1.6 tb composite multilingual dataset.Advances in Neural Information Processing Systems, 35:31809–31826, 2022.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning The bigscience roots corpus: A 1.6 tb composite multilingual dataset.Advances in Neural Information Processing Systems, 35:31809–31826, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.857017Z

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Observation e90b2eb8-0286-4a9e-8d6b-12c1f020028e · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Efficient memory management for large language model serving with pagedattention

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.963328Z

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Observation b938a869-865a-480a-8b8a-a71e99dd5731 · outbound

This paper cites Synthetic data (almost) from scratch: Generalized instruction tuning for language models.Transactions on Machine Learning Research, 2025.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Synthetic data (almost) from scratch: Generalized instruction tuning for language models.Transactions on Machine Learning Research, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.601281Z

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Observation c4d1719e-d22e-4e65-b885-8ed177f60b69 · outbound

This paper cites Llm2llm: Boosting llms with novel iterative data enhancement.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Llm2llm: Boosting llms with novel iterative data enhancement

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.714925Z

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Observation 1718c927-0095-4271-8215-a1ea598ececc · outbound

This paper cites Superfiltering: Weak-to-strong data filtering for fast instruction-tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Superfiltering: Weak-to-strong data filtering for fast instruction-tuning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.101996Z

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Observation 0c284da9-b396-4e47-a75a-57e6f371b31e · outbound

This paper cites Selective reflection-tuning: Student-selected data recycling for llm instruction-tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Selective reflection-tuning: Student-selected data recycling for llm instruction-tuning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:05.327382Z

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Observation c2b5f4ad-4a6e-4e70-a8cb-d44e49e79eb6 · outbound

This paper cites Self-alignment with instruction backtranslation.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Self-alignment with instruction backtranslation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:04.726946Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2661dd3-1fb3-4ae8-99cb-ae95243a7bf5 · outbound

This paper cites From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning From quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning

Reference 32

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This paper cites Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review

Reference 33

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This paper cites One-shot learning as instruction data prospector for large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning One-shot learning as instruction data prospector for large language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:04.336612Z

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Observation f5960387-a6c2-4207-bd59-16072deec14f · outbound

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Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:14:03.070826Z

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Observation 96352ac5-8f70-4b77-80fc-b898d7e75b96 · outbound

This paper cites I-SHEEP: Self-alignment of LLM from scratch through an iterative self-enhancement paradigm.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning I-SHEEP: Self-alignment of LLM from scratch through an iterative self-enhancement paradigm

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:03.834861Z

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Observation 108ab5c8-8261-45f7-b84c-224be0ad27e3 · outbound

This paper cites Alignbench: Benchmarking chinese alignment of large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Alignbench: Benchmarking chinese alignment of large language models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:02.408046Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9bb512b-e2f8-4e4c-ba38-b83c78d145e0 · outbound

This paper cites What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning

Reference 38

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Observation 88500d71-ebd7-4223-a085-98d6c3f1d0e4 · outbound

This paper cites Condor: Enhance llm alignment with knowledge-driven data synthesis and refinement.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Condor: Enhance llm alignment with knowledge-driven data synthesis and refinement

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:01.993664Z

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Observation 2089b88d-7023-49d2-ba72-70cac8f31373 · outbound

This paper cites #instag: Instruction tagging for analyzing supervised fine-tuning of large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning #instag: Instruction tagging for analyzing supervised fine-tuning of large language models

Reference 40

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Observation c08811b7-2b65-48b0-ae42-58310340e4bf · outbound

This paper cites LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:13:54.979012Z

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Observation a0c0c67a-eae7-4cd3-b40e-23bd757753c7 · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 42

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Observation 5f10db38-41a3-4204-8093-386f8e7300de · outbound

This paper cites Instruction Tuning with GPT-4.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Instruction Tuning with GPT-4

Reference 43

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Observation c9d1c56b-416c-4da3-aa8a-7522a3d20686 · outbound

This paper cites Datadreamer: A tool for synthetic data generation and reproducible llm workflows.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Datadreamer: A tool for synthetic data generation and reproducible llm workflows

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:01.472633Z

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Observation ceddb5c0-b141-4d1f-b8de-54993c4cb4d6 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Gpqa: A graduate-level google-proof q&a benchmark

Reference 45

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

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:51.071623Z

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This paper cites On efficient training of large-scale deep learning models.ACM Computing Surveys, 57(3):1–36, 2024.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning On efficient training of large-scale deep learning models.ACM Computing Surveys, 57(3):1–36, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.984275Z

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Observation 7302009f-25b7-428f-9e58-d5142ff2376b · outbound

This paper cites GitHub - gururise/AlpacaDataCleaned: Alpaca dataset from Stanford, cleaned and curated — github.com.https://github.com/gururise/AlpacaDataCleaned, 2023.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning GitHub - gururise/AlpacaDataCleaned: Alpaca dataset from Stanford, cleaned and curated — github.com.https://github.com/gururise/AlpacaDataCleaned, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:01.176835Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ebbeb1c-b1aa-4ac9-bfaf-3b78b40c9e44 · outbound

This paper cites Hashimoto.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Hashimoto

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.796970Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa155859-eb25-4f6b-8048-03834626c0ec · outbound

This paper cites LAB: Large-Scale Alignment for ChatBots.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning LAB: Large-Scale Alignment for ChatBots

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:51.447707Z

Source-reported events for the cited work

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Observation acfb7437-85c6-4c3d-901e-0b369fec10bd · outbound

This paper cites Let’s synthesize step by step: Iterative dataset synthesis with large language models by extrapolating errors from small models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Let’s synthesize step by step: Iterative dataset synthesis with large language models by extrapolating errors from small models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.610564Z

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Observation eba1a0ba-7778-48b4-8f0f-60308b22a657 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:51.643664Z

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Observation 0a8b08e0-d54e-4bc0-9bf8-b2121d651f64 · outbound

This paper cites Eda: Easy data augmentation techniques for boosting performance on text classifica- tion tasks.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Eda: Easy data augmentation techniques for boosting performance on text classifica- tion tasks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.244690Z

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Observation 03c38a32-dd25-42c6-bfbf-9263f725d02b · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Self-instruct: Aligning language models with self-generated instructions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.413297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9cd97a2-5812-4b09-a460-43e1cf8482d2 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:59.883532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 404f97ed-e0d2-4bbc-a823-d4a0412e97b4 · outbound

This paper cites Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:14:00.119234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e841da40-4eef-4f28-b5b7-a2eb61b5bae8 · outbound

This paper cites Magpie: Alignment data synthesis from scratch by prompting aligned LLMs with nothing.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Magpie: Alignment data synthesis from scratch by prompting aligned LLMs with nothing

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:52.264909Z

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Observation a2eb3993-4f60-45d6-8c21-3b1ea6afc418 · outbound

This paper cites Rethinking the Instruction Quality: LIFT is What You Need.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Rethinking the Instruction Quality: LIFT is What You Need

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:52.181382Z

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Observation 86a9db3f-ebe2-448f-a05c-d93a58116d97 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:52.389523Z

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Observation 5f87a2dd-cf0e-4580-8b6b-03644ed445e4 · outbound

This paper cites Wavecoder: Widespread and versatile enhancement for code large language models by instruction tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Wavecoder: Widespread and versatile enhancement for code large language models by instruction tuning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:59.663763Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9887710-a834-4284-8905-1591e1d2c3b9 · outbound

This paper cites Long is more for alignment: a simple but tough-to-beat baseline for instruction fine-tuning.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Long is more for alignment: a simple but tough-to-beat baseline for instruction fine-tuning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:59.177262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 052fbefc-2e26-4fa4-8e4f-e62db30928f0 · outbound

This paper cites Automatic instruction evolving for large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Automatic instruction evolving for large language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:59.421993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cabeaa76-7fc6-4622-af0e-ed041ec82594 · outbound

This paper cites Llamafactory: Unified efficient fine-tuning of 100+ language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Llamafactory: Unified efficient fine-tuning of 100+ language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:58.779824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a134b35d-3cd1-4112-85e4-f020f9083638 · outbound

This paper cites Tree-instruct: A preliminary study of the intrinsic relationship between complexity and alignment.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Tree-instruct: A preliminary study of the intrinsic relationship between complexity and alignment

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:58.974897Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 949ded7a-cce9-40cf-9150-dd684eae55a5 · outbound

This paper cites Dataset quantization.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Dataset quantization

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:52.830369Z

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Observation 1254cb01-0c2d-46b7-825e-e19870ec40ce · outbound

This paper cites Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36: 55006–55021, 2023.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36: 55006–55021, 2023

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:52.726910Z

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Observation e3b15567-0fad-44d6-80df-c09a7efc2c30 · outbound

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

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Instruction-Following Evaluation for Large Language Models

Reference 67

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Observation cdd5d1b3-0b22-432c-83bf-6eb9c2c18755 · outbound

This paper cites Davir: Data selection via implicit reward for large language models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Davir: Data selection via implicit reward for large language models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:58.603876Z

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Observation fdb4b7a5-b9a3-4840-aac8-b9097a0e7ce6 · outbound

This paper cites Figure 9: Self-Alignment instruction score example.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Figure 9: Self-Alignment instruction score example

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:58.431472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6af83964-6f7b-463b-bedd-b7202b2f7a0d · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:58.269247Z

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Observation f83ec14f-9223-4220-ade5-c5b074e17e40 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:58.088756Z

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Observation b7375adb-c352-4d6a-afb7-8c7e11d89083 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.938886Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9bb2a03-9e1a-47b1-8dbc-abdec927d2fa · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.791235Z

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Observation 52526d15-e480-43fb-a2c1-2a47847bff39 · outbound

This paper cites Step 2 #Plan#:.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Step 2 #Plan#:

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:57.649246Z

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Observation 600e9b15-429c-42aa-9df3-0f103000c994 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.535684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d3239f4-c35d-46a4-ae85-48b8ef08411e · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.394760Z

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Observation a024d433-ca1e-42e4-ab54-819d9ebb2ab7 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.236699Z

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Observation 272c4836-8a7d-4800-84ce-0be2123ef5f3 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:57.107833Z

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Observation ca49b8a1-c920-4a7a-9064-efd5f8a955a6 · outbound

This paper cites Step 3 #Rewritten Prompt#: Find the most frequent number in this list: 3, 7, 2, 3, 5, 7.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Step 3 #Rewritten Prompt#: Find the most frequent number in this list: 3, 7, 2, 3, 5, 7

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:56.947365Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d0bb5cf-f6d6-4517-aeb5-5c9778f65087 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:56.813149Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c94ed0d-7466-48b0-bcd7-2a7ec58361b9 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:56.665171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c72e139b-0b03-414f-b4aa-045f7c2adcb6 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:56.510357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c788adf-6358-480c-ae38-d7881996f540 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:56.359455Z

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Observation 978d87ff-6504-4139-8fad-4c350c9aac48 · outbound

This paper cites Step 2 #Plan#:.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Step 2 #Plan#:

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:56.206862Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba8458ae-1735-4bb7-ab26-5c7a527ae259 · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:56.043074Z

Source-reported events for the cited work

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Observation 01567768-ee6c-45a2-9975-d95824ce4ccb · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:55.816703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c623063c-510b-4ec9-88e9-aea826d19b4d · outbound

This paper cites an unresolved cited work.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-05T14:13:55.641027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da7d506a-c0f3-4ca2-a1ea-05a5dfe0d23d · outbound

This paper cites Step 3 #Rewritten Prompt#:.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Step 3 #Rewritten Prompt#:

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:13:55.513188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de1154e6-c55d-447a-8c7a-76da4e1bdbda · outbound

This paper cites Program Synthesis with Large Language Models.

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Program Synthesis with Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:46.955790Z

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