Paper Citation Record · LEDGER

Adaptive Pruning of Pretrained Transformer via Differential Inclusions

As of 12 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2501.03289.

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

pith.paper-citation-record.v1
2501.03289 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:10:59.345403Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:05:54.948805Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact3
verified fuzzy15
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc3e91d0-24e5-48db-8ac8-b412a1bda76e · outbound

This paper cites an unresolved cited work.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:10:59.948747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c715dd70-a24f-4afc-84f4-694f83ed9df5 · outbound

This paper cites (2013) for instance), while Assumption 1 (d) is also mild including all Lipschitz continuous convex function over a compact set.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions (2013) for instance), while Assumption 1 (d) is also mild including all Lipschitz continuous convex function over a compact set

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.933480Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 18dbb9c9-0989-4e53-9ffe-baa0b46337e8 · outbound

This paper cites definable.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions definable

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.768056Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7fb8bc14-58d4-4049-8780-cf907c0f3f6b · outbound

This paper cites According to van den Dries & Miller (1996); Bolte et al.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions According to van den Dries & Miller (1996); Bolte et al

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.752009Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0b4e4cd8-a8b6-4ab5-a658-bd0ffe18783c · outbound

This paper cites Compressing Deep Convolutional Networks using Vector Quantization.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Compressing Deep Convolutional Networks using Vector Quantization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.196491Z

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Observation 69a5b0d3-29d6-4429-90ff-5236a52a722f · outbound

This paper cites Problem complexity and method efficiency in optimization.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Problem complexity and method efficiency in optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:00.030247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ac8142a6-7d8f-4393-b1b4-a72da9e2dc3e · outbound

This paper cites UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.242964Z

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Observation 9c52fbd8-bfe6-4f07-b9ce-382d0097ad25 · outbound

This paper cites Compression of Generative Pre-trained Language Models via Quantization.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Compression of Generative Pre-trained Language Models via Quantization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.253760Z

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Observation 4e1a879a-e6ab-4467-bff0-e4a20f51ed17 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Training data-efficient image transformers & distillation through attention

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:00.003585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e674d731-893e-42a6-9d23-e4ebdd20f778 · outbound

This paper cites an unresolved cited work.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:10:59.964408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1f982b3-1081-481f-b28c-4c898ab20223 · outbound

This paper cites In the following, we present the sufficient descent property of Qk along the Lyapunov function F.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions In the following, we present the sufficient descent property of Qk along the Lyapunov function F

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.917200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d0e65f2d-9ed6-487c-91c2-c958d25748bf · outbound

This paper cites By the lower boundedness assumption of L(W ), both ¯L(P ) and F (Q) are lower bounded by their definitions, i.e., (9) and (15), respectively.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions By the lower boundedness assumption of L(W ), both ¯L(P ) and F (Q) are lower bounded by their definitions, i.e., (9) and (15), respectively

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.899501Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4307ff66-632f-4ba0-a0cc-58e00cc805d5 · outbound

This paper cites B.2 R ELATIVE ERROR PROPERTY In this subsection, we provide the bound of subgradient by the discrepancy of two successive iterates.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions B.2 R ELATIVE ERROR PROPERTY In this subsection, we provide the bound of subgradient by the discrepancy of two successive iterates

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.883969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d17d3b0e-0c52-4776-9d88-2ca9ee1710a6 · outbound

This paper cites an unresolved cited work.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:10:59.866657Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 381d0647-fd3a-4c9f-9580-ed0f84af4696 · outbound

This paper cites an unresolved cited work.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:10:59.850522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 690ce869-d02f-40a9-9b94-78b26f776411 · outbound

This paper cites According to (Łojasiewicz, 1965; Bochnak et al.,.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions According to (Łojasiewicz, 1965; Bochnak et al.,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.833909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a705bf66-f967-45f7-8d3e-9d8b01b1046a · outbound

This paper cites Some typical examples include polynomial functions, the indicator function of a semialgebraic set, and the Euclidean norm (Bochnak et al., 1998, page 26).

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Some typical examples include polynomial functions, the indicator function of a semialgebraic set, and the Euclidean norm (Bochnak et al., 1998, page 26)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.818628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c20dcac2-77e5-4b48-95aa-ba2ab674c051 · outbound

This paper cites The function is said to be real analytic on V ⊂ U if it is real analytic at each u ∈ V (Krantz & Parks, 2002, Definition 1.1.5).

Adaptive Pruning of Pretrained Transformer via Differential Inclusions The function is said to be real analytic on V ⊂ U if it is real analytic at each u ∈ V (Krantz & Parks, 2002, Definition 1.1.5)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.801722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba4dac57-e0d5-48f3-8352-62931ae7249a · outbound

This paper cites Let W i ∈ Rdi×di−1 be the weight matrix between the (i − 1)-th layer and the i-th layer for any i = 1,.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Let W i ∈ Rdi×di−1 be the weight matrix between the (i − 1)-th layer and the i-th layer for any i = 1,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.785259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 75727953-7db9-470a-8f89-b564d32dcb1c · outbound

This paper cites [Proof of Corollary 1] To justify this corollary, we only need to verify the associated Lyapunov function F satisfies Kurdyka-Łojasiewicz inequality.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions [Proof of Corollary 1] To justify this corollary, we only need to verify the associated Lyapunov function F satisfies Kurdyka-Łojasiewicz inequality

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.736140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d764f9c8-dec9-4e0a-b8f2-4de6a92fb9f2 · outbound

This paper cites Sparse Recovery via Differential Inclusions.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Sparse Recovery via Differential Inclusions

Reference 1983

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:59.477709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8abfe9e0-6fc1-4d17-ac31-bd13632028a7 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.213255Z

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Observation a43706f7-7408-4eff-a065-a05bd3d18b8f · outbound

This paper cites Imagenet: A large-scale hier- archical image database.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Imagenet: A large-scale hier- archical image database

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.185488Z

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Unavailable: canonical work link unavailable.

Observation 17b47be0-adfe-455d-86f9-0ed94064bbfa · outbound

This paper cites Choose your path wisely: gradient descent in a Bregman distance framework.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Choose your path wisely: gradient descent in a Bregman distance framework

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.174195Z

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Observation c5a4704d-a28f-44a8-b9de-6175aa5b35fe · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.179453Z

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Unavailable: canonical work link unavailable.

Observation d5ba5a33-b477-4cae-9389-e0c8063c6456 · outbound

This paper cites 11 Published as a conference paper at ICLR 2025 Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, and Yuan Yao.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions 11 Published as a conference paper at ICLR 2025 Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng, and Yuan Yao

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.191076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8e3a748-c201-412a-aeaa-e66bacdae694 · outbound

This paper cites Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization

Reference 2013

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:59.718723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba3a39de-a9d0-432e-a9fa-a4b9ad6e7596 · outbound

This paper cites The Need for Speed: Pruning Transformers with One Recipe.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions The Need for Speed: Pruning Transformers with One Recipe

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.208018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c71325b3-4b33-41e6-8984-5a6ebf5e48fa · outbound

This paper cites Metadistiller: Network self- boosting via meta-learned top-down distillation.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Metadistiller: Network self- boosting via meta-learned top-down distillation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:00.048453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a26e3f5-a931-4d7c-961c-90a0e53ffb03 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Distilling the Knowledge in a Neural Network

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.202773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 262d0261-78a1-4138-89a3-b14c718bc4e3 · outbound

This paper cites Model compression via distillation and quantization.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions Model compression via distillation and quantization

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.237256Z

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Unavailable: canonical work link unavailable.

Observation 84b643ac-92d3-497e-880d-5dc5aeb491e2 · outbound

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

Adaptive Pruning of Pretrained Transformer via Differential Inclusions A Simple and Effective Pruning Approach for Large Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:59.248680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f03a8dd-dbe9-4e41-ac13-0f0d3eb8b613 · outbound

This paper cites GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:59.390588Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17994d1a-6f7b-49f9-a90e-7d79e1e020df · outbound

This paper cites (2024) pruning method.

Adaptive Pruning of Pretrained Transformer via Differential Inclusions (2024) pruning method

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:59.985914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation a4b44001-d106-4a33-9d79-225cde32a359 · inbound

AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs cites this paper.

AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs Adaptive Pruning of Pretrained Transformer via Differential Inclusions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T17:05:54.948805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.