Paper Citation Record · LEDGER

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching

As of 7 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2506.20480.

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

pith.paper-citation-record.v1
2506.20480 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:53:11.677172Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:11:52.402271Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

verified exact2
verified fuzzy11
unresolved48
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4279e4b9-21ca-46fa-94f3-cada9e133624 · outbound

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

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching LLaMA: Open and Efficient Foundation Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:05.965400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7f13a99-1c85-4bdd-b689-b62e5ec583bc · outbound

This paper cites GPT-4 Technical Report.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.019934Z

Source-reported events for the cited work

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Observation a01ffc72-89e7-4d0b-a202-20d7693dd302 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.091501Z

Source-reported events for the cited work

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Observation 2db738bf-7a44-401d-9cb0-b1ef775bb4da · outbound

This paper cites Scaling Laws for Neural Language Models.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Scaling Laws for Neural Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.144314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f77f77d-fd1c-43c7-bd11-457715cff51e · outbound

This paper cites Training Compute-Optimal Large Language Models.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Training Compute-Optimal Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.218136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37b81a4a-c2bf-4eb4-956f-4b92f8448e5c · outbound

This paper cites Emergent Abilities of Large Language Models.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Emergent Abilities of Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.266468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 465f1f82-0d91-4e23-b3d8-6971b039cd6b · outbound

This paper cites Learning and generalization in overparame- terized neural networks, going beyond two layers.Advances in neural information processing systems, 32, 2019.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Learning and generalization in overparame- terized neural networks, going beyond two layers.Advances in neural information processing systems, 32, 2019

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.733065Z

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 e9e9cbf0-7da7-45a2-b44b-f4fd84b9c1dd · outbound

This paper cites A convergence theory for deep learning via over-parameterization.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching A convergence theory for deep learning via over-parameterization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.371132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b4ca624-61d4-46d4-987e-d5a9b27a407c · outbound

This paper cites Train big, then compress: Rethinking model size for efficient training and inference of transformers.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Train big, then compress: Rethinking model size for efficient training and inference of transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.707367Z

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 64dfe37d-c439-45f1-a3f0-61f282a349f2 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.489958Z

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Observation 58fde576-b40e-4b83-816d-743a63056b15 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.540313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7d8c180-efd4-4507-a972-bcfb5d9685dc · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.589395Z

Source-reported events for the cited work

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Observation 4b9b6483-a566-4a9e-a75d-3caf64efde62 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.659060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5603007a-58f6-48f9-bd4b-e4075362620d · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.Advances in neural information processing systems, 36:21702–21720, 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Llm-pruner: On the structural pruning of large language models.Advances in neural information processing systems, 36:21702–21720, 2023

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.720235Z

Source-reported events for the cited work

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Observation e7eb27d7-51db-41fc-90f6-7031e5ebb259 · outbound

This paper cites DISCO: Distilling Counterfactuals with Large Language Models.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching DISCO: Distilling Counterfactuals with Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.787151Z

Source-reported events for the cited work

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Observation c91728f5-ac8c-4a43-a2e8-abafee15971e · outbound

This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.841926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbfa6518-13c0-4810-82e1-5926c680b7f2 · outbound

This paper cites Distilling reasoning capabilities into smaller language models.Findings of the Association for Computational Linguistics: ACL 2023, pages 7059–7073, 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Distilling reasoning capabilities into smaller language models.Findings of the Association for Computational Linguistics: ACL 2023, pages 7059–7073, 2023

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.917340Z

Source-reported events for the cited work

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Observation f3186e47-474f-458f-9fb6-1a8c9124c0c3 · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Zephyr: Direct Distillation of LM Alignment

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:06.984578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1636d911-4011-4e27-a4ab-2ebb3ade3241 · outbound

This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.047017Z

Source-reported events for the cited work

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Observation 4a5cb47b-850f-43e1-852f-ed5a46bdbbaa · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching A survey of quantization methods for efficient neural network inference

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.111671Z

Source-reported events for the cited work

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Observation 851377dd-f798-4706-8075-5456b74ead74 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115, 2023

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.174932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b29bd29c-62d9-41ac-b5aa-746d02b725a6 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.225669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bbeca5c-5abf-4875-9b0d-3b53db308ce8 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.279875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3806a0b-ba7e-442d-b07b-44ae477e95be · outbound

This paper cites Smac3: A versatile bayesian optimization package for hyperparameter optimization.Journal of Machine Learning Research, 23(54):1–9, 2022.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Smac3: A versatile bayesian optimization package for hyperparameter optimization.Journal of Machine Learning Research, 23(54):1–9, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.362157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e41432c4-f54a-45ce-8390-146fef628779 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.452648Z

Source-reported events for the cited work

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Observation e8c327d2-7bde-4178-9385-5a7a644ab2e4 · outbound

This paper cites LaCo: Large Language Model Pruning via Layer Collapse.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching LaCo: Large Language Model Pruning via Layer Collapse

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.541474Z

Source-reported events for the cited work

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Observation fe7ab95b-270e-4bc1-934d-b71894824905 · outbound

This paper cites Structural Pruning of Pre-trained Language Models via Neural Architecture Search.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Structural Pruning of Pre-trained Language Models via Neural Architecture Search

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:53:12.420176Z

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 9b9726de-5a39-47fe-80f2-41e55063db34 · outbound

This paper cites Weight averaging for neural networks and local resampling schemes.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Weight averaging for neural networks and local resampling schemes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.613961Z

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 de267e04-2bf7-4f35-9047-e98cb7a11f50 · outbound

This paper cites Editing Models with Task Arithmetic.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Editing Models with Task Arithmetic

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.747444Z

Source-reported events for the cited work

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Observation e75639c1-4837-4121-be38-e00f2a802326 · outbound

This paper cites Sampling Generative Networks.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Sampling Generative Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.816065Z

Source-reported events for the cited work

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Observation 7eff3e8c-0771-4c78-99fd-9d51ca32ccd2 · outbound

This paper cites Ties-merging: Resolving interference when merging models.Advances in Neural Information Processing Systems, 36, 2024.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Ties-merging: Resolving interference when merging models.Advances in Neural Information Processing Systems, 36, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.598729Z

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 7f274f58-6cbf-4fa8-8ecc-a274846f2511 · outbound

This paper cites Language models are super mario: Absorbing abilities from homologous models as a free lunch.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Language models are super mario: Absorbing abilities from homologous models as a free lunch

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.951088Z

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Observation 46b3c6ab-3a85-48a5-9153-383a7a9fac6f · outbound

This paper cites Evolutionary Optimization of Model Merging Recipes.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Evolutionary Optimization of Model Merging Recipes

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:07.973981Z

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Observation c759d429-66ca-4db6-b7af-7938b9f8506d · outbound

This paper cites Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:53:12.125172Z

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 36422beb-3a26-4377-89e0-fb0a14f494ce · outbound

This paper cites an unresolved cited work.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:53:13.573887Z

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

Observation 367ce059-2646-424e-86c9-4fb00746a905 · outbound

This paper cites Random forests.Machine learning, 45:5–32, 2001.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Random forests.Machine learning, 45:5–32, 2001

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:08.278018Z

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Observation fc2d9bc1-dd87-45d5-8bfe-9506f81140bc · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.https://github.com/open-compass/opencompass, 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Opencompass: A universal evaluation platform for foundation models.https://github.com/open-compass/opencompass, 2023

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:08.437254Z

Source-reported events for the cited work

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Observation 41561ae4-bfd9-40af-8997-2a4f8ee0063d · outbound

This paper cites CLUE: A Chinese Language Understanding Evaluation Benchmark.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching CLUE: A Chinese Language Understanding Evaluation Benchmark

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:08.643876Z

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Observation b8c353eb-a716-44b6-9ae5-3061b89903fa · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:08.839620Z

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Observation 89821421-ff23-4b03-9d9c-8cf56a881d5a · outbound

This paper cites Piqa: Reasoning about phys- ical commonsense in natural language.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Piqa: Reasoning about phys- ical commonsense in natural language

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:08.961805Z

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Unavailable: canonical work link unavailable.

Observation dcd3980f-7df1-4ec2-8e18-ed1924d02744 · outbound

This paper cites ChID: A Large-scale Chinese IDiom Dataset for Cloze Test.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching ChID: A Large-scale Chinese IDiom Dataset for Cloze Test

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.095979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5791ef2-0e0d-4452-b19c-1499f2a18da3 · outbound

This paper cites The winograd schema challenge.KR, 2012:13th, 2012.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching The winograd schema challenge.KR, 2012:13th, 2012

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.528903Z

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 0c5259a8-1617-4684-b53a-553a0452852d · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.383657Z

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Observation 9c2952d2-bc22-4724-8dbb-74f4ffb336c2 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.516180Z

Source-reported events for the cited work

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Observation a31962f2-2d6f-460b-b022-ffcebfd272be · outbound

This paper cites Measuring Massive Multitask Language Understanding.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Measuring Massive Multitask Language Understanding

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.643480Z

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Observation ced82713-2f5a-486b-8a20-f0183ebf6783 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching CMMLU: Measuring massive multitask language understanding in Chinese

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.736408Z

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Observation 99c88549-e03c-4d4e-8a29-6773c4eff872 · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.868001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24ab2e58-1480-481f-948b-4651f1687d8b · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:09.981515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71684b67-6e7c-47a0-9a8d-41cb31aecb01 · outbound

This paper cites Investigating prior knowledge for challenging chinese machine reading comprehension.Transactions of the Association for Computational Linguistics, 8:141–155, 2020.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Investigating prior knowledge for challenging chinese machine reading comprehension.Transactions of the Association for Computational Linguistics, 8:141–155, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.513704Z

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 5200d681-ec6e-4d96-a754-64b6e76360b8 · outbound

This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:10.226886Z

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Observation 60c85cda-9e71-493b-a449-e4337b1ba5e1 · outbound

This paper cites llama-2-coder-7b (revision d30d193), 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching llama-2-coder-7b (revision d30d193), 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.497655Z

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 b2753982-ae15-413d-b203-bdf956affde6 · outbound

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

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:10.477326Z

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Unavailable: canonical work link unavailable.

Observation 79f1ecbd-8bb8-4e42-aa5b-bcc535a5fc56 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:10.612011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceee6883-52bb-4deb-af80-e4cdd7be8823 · outbound

This paper cites Code alpaca: An instruction-following llama model for code generation.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Code alpaca: An instruction-following llama model for code generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:10.737231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ef822be-8ef7-41ba-b034-1612ba52e464 · outbound

This paper cites Shallow-deep networks: Understanding and mitigating network overthinking.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Shallow-deep networks: Understanding and mitigating network overthinking

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.471321Z

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 b2b52689-eac5-4880-893e-9e4446e96386 · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching The Unreasonable Ineffectiveness of the Deeper Layers

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:11.013316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0bfcfb1-4c52-4f1d-a23e-40942224123f · outbound

This paper cites Pointer sentinel mixture models, 2016.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Pointer sentinel mixture models, 2016

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:11.119687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6659eb75-c978-4863-bcaf-dd15fbd4173d · outbound

This paper cites The Llama 3 Herd of Models.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching The Llama 3 Herd of Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:53:11.283686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea187a13-c493-40e8-9ca5-d437aaa93a22 · outbound

This paper cites Code-llama-3-8b, 2023.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Code-llama-3-8b, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.327788Z

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 e6cf6ad8-219d-4136-bf6f-e3ee11477147 · outbound

This paper cites Mathcoder: Seamless code integration in LLMs for enhanced mathematical reasoning.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Mathcoder: Seamless code integration in LLMs for enhanced mathematical reasoning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:13.125836Z

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 c01d04ee-2e52-4705-a017-140f7501f9e5 · outbound

This paper cites Mathcoder2: Better math reasoning from continued pretraining on model- translated mathematical code, 2024.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Mathcoder2: Better math reasoning from continued pretraining on model- translated mathematical code, 2024

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:53:12.875414Z

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 a6c4cd08-7636-42a4-a958-c515d864637b · outbound

This paper cites Compressive Transformers for Long-Range Sequence Modelling.

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching Compressive Transformers for Long-Range Sequence Modelling

Reference 62

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:53:11.677172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 47ceea1d-3a54-4204-95d5-71d1de949ea4 · inbound

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs cites this paper.

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T08:11:52.402271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.