Paper Citation Record · LEDGER

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 9 inbound Pith citation observations for arXiv:2412.13795.

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

pith.paper-citation-record.v1
2412.13795 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:53:34.006065Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:23:40.957150Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy4
unresolved38
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 9e2913ca-7b58-473a-a07b-d225fe891b32 · outbound

This paper cites GPT-4 Technical Report.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN GPT-4 Technical Report

Reference 1

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Observation 2b1deeb8-dd1a-4640-9e3f-f5e1bb95b207 · outbound

This paper cites Language Models are Few-Shot Learners.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Language Models are Few-Shot Learners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:32.853082Z

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Observation f6ace1b8-c061-4a61-9d14-4e8a0ca68314 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 7

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Observation d133d81a-690d-4f03-b290-0a9c5698b328 · outbound

This paper cites The Llama 3 Herd of Models.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN The Llama 3 Herd of Models

Reference 10

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Observation 7f74acf5-21fb-4bfe-8626-94f4232b2402 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Measuring Massive Multitask Language Understanding

Reference 12

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Observation ed06f0fd-8f44-42a0-b0d5-8b117cd559df · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:33.131213Z

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Observation f4ac470d-ec52-4677-9a70-f0252183be11 · outbound

This paper cites SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:33.136998Z

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Observation bc1f7586-7abf-4540-9b99-d99cde294344 · outbound

This paper cites Mistral 7B.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Mistral 7B

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:33.147804Z

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Observation 14024b53-9046-4958-b7fb-8c969f243c8a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Adam: A Method for Stochastic Optimization

Reference 17

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Observation fb5ebb1c-00c7-4aa0-be79-e1ce97dacfae · outbound

This paper cites Outlier-weighed Layerwise Sampling for LLM Fine-tuning.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Outlier-weighed Layerwise Sampling for LLM Fine-tuning

Reference 19

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Observation 124b9d85-224e-4ce7-a286-632dcd38daa0 · outbound

This paper cites ReLoRA: High-Rank Training Through Low-Rank Updates.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN ReLoRA: High-Rank Training Through Low-Rank Updates

Reference 20

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Observation d682455c-7642-42e1-8fc2-ec22fd81fda7 · outbound

This paper cites More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

Reference 21

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Observation bf80b2ee-bcfd-4997-822a-9a4c671cd851 · outbound

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

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 22

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Observation efa6d420-7513-403b-91f7-c1a4ef59eafd · outbound

This paper cites Transformers without Tears: Improving the Normalization of Self-Attention.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Transformers without Tears: Improving the Normalization of Self-Attention

Reference 23

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Observation cfd28a2c-8728-4634-9af2-5ed19faba107 · outbound

This paper cites What Language Model to Train if You Have One Million GPU Hours?.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN What Language Model to Train if You Have One Million GPU Hours?

Reference 25

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Observation bf177aae-d0d4-4d2d-9508-ae8c7fc92ea7 · outbound

This paper cites GLU Variants Improve Transformer.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN GLU Variants Improve Transformer

Reference 26

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Observation 977e0f45-021d-4008-ab43-24c2cb03d611 · outbound

This paper cites A deeper look at depth pruning of LLMs.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN A deeper look at depth pruning of LLMs

Reference 27

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Observation 54e0d9f6-8fd2-4870-a4a6-b194b14a8c1c · outbound

This paper cites LLM Pruning and Distillation in Practice: The Minitron Approach.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN LLM Pruning and Distillation in Practice: The Minitron Approach

Reference 28

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Observation 120870c7-ed8e-40e2-9fd6-7bee2805dec7 · outbound

This paper cites The curse of depth in large language models.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN The curse of depth in large language models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:33.495804Z

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Observation b6af3a28-7b4a-4d08-afee-3df905087401 · outbound

This paper cites B2T Connection: Serving Stability and Performance in Deep Transformers.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN B2T Connection: Serving Stability and Performance in Deep Transformers

Reference 30

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Observation d434e55d-86b0-4de5-b89e-531de6c7414a · outbound

This paper cites Spike No More: Stabilizing the Pre-training of Large Language Models.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Spike No More: Stabilizing the Pre-training of Large Language Models

Reference 31

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Observation 702efa29-9c32-496a-b11e-03216cf85266 · outbound

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

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN LLaMA: Open and Efficient Foundation Language Models

Reference 32

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Observation 262e1765-1de1-4fe8-a16e-9acc0ef6d240 · outbound

This paper cites Learning Deep Transformer Models for Machine Translation.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Learning Deep Transformer Models for Machine Translation

Reference 33

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Observation 1d087676-a2f3-4ba3-8d1d-598c84008a3b · outbound

This paper cites Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 34

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Observation 74ef1976-1e15-49df-8559-fd95f273015c · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 35

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Observation 73e90714-fd9c-4bae-8892-7250baca33e9 · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Adam-mini: Use Fewer Learning Rates To Gain More

Reference 36

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Observation 2fd14319-3cd1-4860-8a31-7bbe07af6f5a · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 37

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Observation a1f9c56b-72c8-4fb1-8ffe-3db3596a63db · outbound

This paper cites BlockPruner: Fine-grained Pruning for Large Language Models.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN BlockPruner: Fine-grained Pruning for Large Language Models

Reference 38

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Observation fadcd210-41f2-4f4b-955c-e95ed7cd0822 · outbound

This paper cites Table 9 shows the most hyperparameters of LLaMA models across model sizes.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Table 9 shows the most hyperparameters of LLaMA models across model sizes

Reference 39

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Observation 1e080ea2-cc63-4c9e-a09d-54772a6b0d3e · outbound

This paper cites We observe that both Pre-LN and Mix-LN work effectively with Scaled Initialization.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN We observe that both Pre-LN and Mix-LN work effectively with Scaled Initialization

Reference 40

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Observation 5d8f817e-316a-415c-b1e5-5dcff6d3bb35 · outbound

This paper cites Xiong et al.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Xiong et al

Reference 41

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Observation 22b549b4-613c-4f3b-a89a-9b43ed948b43 · outbound

This paper cites Curse of Depth.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Curse of Depth

Reference 42

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Observation 926cebec-f703-4a47-9dbe-6da861ad3fb7 · outbound

This paper cites The Remarkable Robustness of LLMs: Stages of Inference?.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN The Remarkable Robustness of LLMs: Stages of Inference?

Reference 2014

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Observation 2ce600bf-ace7-48d1-b9a4-e9ed27fe4726 · outbound

This paper cites Adaptive Input Representations for Neural Language Modeling.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Adaptive Input Representations for Neural Language Modeling

Reference 2016

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Observation 3e3bee34-4311-4279-adc1-3cd8527c6b0b · outbound

This paper cites Training Deeper Neural Machine Translation Models with Transparent Attention.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Training Deeper Neural Machine Translation Models with Transparent Attention

Reference 2018

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Observation 9d419124-8153-42aa-accd-42ea74970e0d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

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Observation 41bb98ca-2250-4811-88ab-50ea42387b39 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2020

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Observation 40e63a6e-4406-4c1e-9e33-5a50942ae36b · outbound

This paper cites Exploiting Deep Representations for Neural Machine Translation.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Exploiting Deep Representations for Neural Machine Translation

Reference 2021

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Observation cd8136e4-e427-483d-95cb-56f0c8e19f97 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 2022

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Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN Layer Normalization

Reference 2023

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This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN The Unreasonable Ineffectiveness of the Deeper Layers

Reference 2024

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Observation c20078ba-f349-4cc3-a83a-8f194ff167f6 · outbound

This paper cites From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications.

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 2025

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Pith citing papers

Observation a31f75b4-e492-4d28-a9f1-391160482c09 · inbound

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture cites this paper.

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 20

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Observation 6ce60a9a-7ba9-4126-bdf4-772657ddd940 · inbound

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling cites this paper.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 44

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Observation a7ead766-3b43-466f-b27b-331c1a18ecf0 · inbound

GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling cites this paper.

GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 12

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Observation 0c3e22a9-0f0c-4540-bc49-1ae4682b283b · inbound

EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens cites this paper.

EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 13

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On Surjectivity of Neural Networks: Can you elicit any behavior from your model? cites this paper.

On Surjectivity of Neural Networks: Can you elicit any behavior from your model? Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 48

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Observation 031a2822-67d9-4a01-98b9-916d66d7168d · inbound

When Does Sparsity Mitigate the Curse of Depth in LLMs cites this paper.

When Does Sparsity Mitigate the Curse of Depth in LLMs Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 21

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Observation 8b954a05-1d0a-4d8f-b9ed-6508d39da1a8 · inbound

Layer Collapse in Diffusion Language Models cites this paper.

Layer Collapse in Diffusion Language Models Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 10

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Observation 842d8124-f168-400f-93e9-c1028eab88f4 · inbound

Layer Collapse in Diffusion Language Models cites this paper.

Layer Collapse in Diffusion Language Models Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 10

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Observation a024ae2e-df91-435a-b74e-9d398979b863 · inbound

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry cites this paper.

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-26T05:29:00.085809Z

Source-reported events for the cited work

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