Paper Citation Record · LEDGER

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning

As of 13 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2411.17426.

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

pith.paper-citation-record.v1
2411.17426 v3

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:13:55.350447Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:49:18.173939Z

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

57 of 57 outbound references displayed

verified exact0
verified fuzzy2
unresolved55
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

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

Outbound references

Observation 94ae2224-f413-47c8-b03c-e30bbcb817fb · outbound

This paper cites Phi-4 Technical Report.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Phi-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.118677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 939f3533-6a90-453f-b5e1-ad3e538ccc18 · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.131779Z

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Observation 03ed4dc3-d83b-4793-af30-70b4c2134d6b · outbound

This paper cites ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.140149Z

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Observation b8618e0a-7d68-4ce0-a1ce-951f534da253 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.154139Z

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Observation c58bd6b0-214b-406c-9126-2631c6f62248 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.127736Z

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Unavailable: canonical work link unavailable.

Observation a06b8540-cd2b-4cf5-ae83-42f9b05ebd21 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Reproducible scaling laws for contrastive language-image learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.158701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b283e41-b2fe-4436-99c0-8c1b9d840ee4 · outbound

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

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.162705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ced3d7ac-354d-4ae5-8102-0cd4742e08dd · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.179499Z

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Unavailable: canonical work link unavailable.

Observation 0861081f-5135-45b7-ab91-8303610ce173 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.183663Z

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Observation b58ca8af-8e8a-47d0-b4fc-e6a66fc6ec52 · outbound

This paper cites LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.187485Z

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Observation 3e02b31b-5f48-4c1e-b1e9-c8a2ca0c7c7f · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.197342Z

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Observation 929250b8-7d16-4f18-a628-02e444a1a923 · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Parameter-Efficient Transfer Learning with Diff Pruning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.201088Z

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Observation d57e66d5-ab66-4375-a10a-9026e95707f9 · outbound

This paper cites Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.205222Z

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Observation c2407cda-9fed-47bd-8506-aa1e49ebb98b · outbound

This paper cites WARP: Word-level Adversarial ReProgramming.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning WARP: Word-level Adversarial ReProgramming

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.209254Z

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Observation e7c08225-ea0f-443a-831a-3ce08e08557d · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.216750Z

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Observation c3d855c0-f8a4-4e2e-8759-7b3d1a4db797 · outbound

This paper cites MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.220785Z

Source-reported events for the cited work

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Observation 8efdc58e-b9ab-406d-97bb-8ff2e6959d99 · outbound

This paper cites A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.224786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db01a03d-8a12-44a8-8aa5-8b3b5e49098f · outbound

This paper cites Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.228601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d311e58-d3d8-4e1b-9ddb-e638311862f2 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.232251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a7a83bb-7620-44f0-b923-b969ae532d49 · outbound

This paper cites Svdqunat: Absorb- ing outliers by low-rank components for 4-bit diffusion models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Svdqunat: Absorb- ing outliers by low-rank components for 4-bit diffusion models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.236317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b615eb-650f-46e8-8ac2-7b40970045e4 · outbound

This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning SnapKV: LLM Knows What You are Looking for Before Generation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.239624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed97d7b7-c440-456b-8930-945c2dd856c0 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.243241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf7255d4-7e19-46c4-92bc-15777634fcfc · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.247477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14625551-786f-42c8-94b0-872174bdea33 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning RWKV: Reinventing RNNs for the Transformer Era

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.255781Z

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Observation 637fb8be-84dc-49d1-886d-96ed56542cc6 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.259515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15fe09b5-eda5-4c8f-bdf3-32cc9175b681 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.263459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04699dde-0e92-4539-bb01-aebb8cfbbd7c · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning SocialIQA: Commonsense Reasoning about Social Interactions

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.267389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d05018be-7809-413f-8ad5-dbff2ab5383b · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Fast Transformer Decoding: One Write-Head is All You Need

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.271636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 240575b6-03cd-4d5e-871b-dc610e9b5332 · outbound

This paper cites Lora vs full fine-tuning: An illusion of equivalence.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Lora vs full fine-tuning: An illusion of equivalence

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.276538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c71ffa1-36b8-427c-a235-614f1973af5c · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.280224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6ace2cf-c3aa-4b23-8867-ff849a4d6a98 · outbound

This paper cites You Only Cache Once: Decoder-Decoder Architectures for Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning You Only Cache Once: Decoder-Decoder Architectures for Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.284100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 660ef298-72e2-458d-93fb-9f1d8e881473 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.288218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 619506be-f886-471c-9b68-e780341c5a34 · outbound

This paper cites SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.292531Z

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Unavailable: canonical work link unavailable.

Observation 0d3f4acb-fb10-4e22-b983-6bdf1b072934 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Linformer: Self-Attention with Linear Complexity

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.296238Z

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Unavailable: canonical work link unavailable.

Observation 5a33cba9-a291-44de-8180-4e27d41b7b60 · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.300598Z

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Unavailable: canonical work link unavailable.

Observation be1725d7-4b52-4742-bf2c-e9f870eaeae6 · outbound

This paper cites LoRA-Pro: Are Low-Rank Adapters Properly Optimized?.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.304466Z

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Observation b6fbb1ab-9ef3-4c0d-ac01-74c4d80b5d1a · outbound

This paper cites Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.308012Z

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Unavailable: canonical work link unavailable.

Observation ea599320-144a-4c07-a7f6-5694d63993d4 · outbound

This paper cites Effectively Compress KV Heads for LLM.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Effectively Compress KV Heads for LLM

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.312088Z

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Unavailable: canonical work link unavailable.

Observation 3f9ac2cb-643d-4fe0-aa63-60f7bce531ba · outbound

This paper cites Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.315899Z

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Unavailable: canonical work link unavailable.

Observation 475ee90c-7f87-405d-a658-a7024e1635e5 · outbound

This paper cites B., Ravfogel, S., and Goldberg, Y.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning B., Ravfogel, S., and Goldberg, Y

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.319638Z

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Unavailable: canonical work link unavailable.

Observation 07cb534f-6e64-4e05-b2b6-ea55860b5e28 · outbound

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

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.323246Z

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Observation a7dbbdbe-94bc-49a0-8667-e3a1256942ec · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.326969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 843e2252-8382-4f64-b0e1-c34fe84d6465 · outbound

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

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.330699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da9fe209-f295-4c5d-8ad7-1d23fb2d2c2a · outbound

This paper cites Masking as an Efficient Alternative to Finetuning for Pretrained Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.334461Z

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Observation d09c4ece-256c-4ea1-96f7-028e913730ed · outbound

This paper cites Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.338087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3ed83cb-0398-441a-9e59-5dc460e7eb44 · outbound

This paper cites MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.342435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aeffe0e-bb90-436e-b275-c93b47495d8c · outbound

This paper cites Both PiSSA and CLOVER exhibit stable training performance.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Both PiSSA and CLOVER exhibit stable training performance

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:56.122693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e2247d2f-1422-47e2-9d58-7cb74167d6c7 · outbound

This paper cites WinoGrande (Sakaguchi et al., 2021)40,398 1,267 Fill-in-the-blank task with binary options.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning WinoGrande (Sakaguchi et al., 2021)40,398 1,267 Fill-in-the-blank task with binary options

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:56.111179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 22ab034e-e45a-4f54-a332-90fad52f2572 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.251169Z

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Observation 57bfdbea-7147-42fb-af31-eec34fd06814 · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.170833Z

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Observation 5fa3912f-45f4-4269-b69f-cf1d63b4892d · outbound

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

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.166830Z

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Observation 24e3e211-2a95-4ad7-ae8a-add9f701ff2f · outbound

This paper cites Reducing Transformer Key-Value Cache Size with Cross-Layer Attention.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.149273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6070763a-9f62-4b58-96cd-0a12f6c51a35 · outbound

This paper cites KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.213100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11bb79f1-156b-44b5-9a75-637491669c6b · outbound

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

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.145477Z

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Observation 437710f4-7ee4-4ccd-95db-a43f4d3c31ea · outbound

This paper cites Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.192490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00c88cea-f1f3-4847-a5af-51cb609aec90 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.123699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a30bc37d-1daa-4197-b60d-85e3ff4c62f6 · outbound

This paper cites Composable Sparse Fine-Tuning for Cross-Lingual Transfer.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Composable Sparse Fine-Tuning for Cross-Lingual Transfer

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:55.135912Z

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Pith citing papers

Observation ae8f960e-e435-4def-9d83-e29d039d6d53 · inbound

A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models cites this paper.

A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:51:19.728655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.