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Paper Citation Record · LEDGER

Sparse Fine-Tuning of Transformers for Generative Tasks

As of 7 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2507.10855.

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

pith.paper-citation-record.v1
2507.10855 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:30:14.998791Z

measured 58 of 58 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

58 of 58 outbound references displayed

  • verified exact8
  • verified fuzzy37
  • unresolved13
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9511c46e-1fea-4a3b-adb3-c6a4f55fc39f · outbound

This paper cites Decomposing and interpreting image representations via text in vits beyond CLIP.

Sparse Fine-Tuning of Transformers for Generative Tasks Decomposing and interpreting image representations via text in vits beyond CLIP

Reference 1

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

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Observation e9828c74-1039-4500-be17-5d60b142e616 · outbound

This paper cites A fast iterative shrinkage- thresholding algorithm for linear inverse problems.

Sparse Fine-Tuning of Transformers for Generative Tasks A fast iterative shrinkage- thresholding algorithm for linear inverse problems

Reference 2

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

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Observation 8b962cc0-b80a-4cc0-940d-2ff011c390ad · outbound

This paper cites Longformer: The Long-Document Transformer.

Sparse Fine-Tuning of Transformers for Generative Tasks Longformer: The Long-Document Transformer

Reference 3

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Source-reported events for the cited work

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Observation e633657a-3ad1-42fe-8421-9782d655f17a · outbound

This paper cites Rep- resentation learning: A review and new perspectives.

Sparse Fine-Tuning of Transformers for Generative Tasks Rep- resentation learning: A review and new perspectives

Reference 4

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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.

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Observation af27c774-498a-4188-a472-b857cabd314b · outbound

This paper cites Towards monosemanticity: De- composing language models with dictionary learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Towards monosemanticity: De- composing language models with dictionary learning

Reference 5

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

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Observation 5c1f3671-1906-4e2f-9f36-d72a314c457e · outbound

This paper cites Compressive sampling.

Sparse Fine-Tuning of Transformers for Generative Tasks Compressive sampling

Reference 6

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Source-reported events for the cited work

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Observation aa4eea79-b944-4551-90e3-cc858248af19 · outbound

This paper cites Pixart- σ: Weak-to-strong training of dif- fusion transformer for 4k text-to-image generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Pixart- σ: Weak-to-strong training of dif- fusion transformer for 4k text-to-image generation

Reference 7

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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.

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Observation 096e96c4-dabc-4ba2-8170-74f55abbc1b2 · outbound

This paper cites Large convolutional model tuning via filter subspace.

Sparse Fine-Tuning of Transformers for Generative Tasks Large convolutional model tuning via filter subspace

Reference 8

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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.

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Observation d69b7fec-a966-4382-8b22-dfebd119467e · outbound

This paper cites Graph Convolution with Low-rank Learnable Local Filters.

Sparse Fine-Tuning of Transformers for Generative Tasks Graph Convolution with Low-rank Learnable Local Filters

Reference 9

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verified exact
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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.

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Observation 3ce6576a-d060-42a4-9d2f-853414c06544 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Sparse Fine-Tuning of Transformers for Generative Tasks Generating Long Sequences with Sparse Transformers

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e8b053f3-b16f-4d1f-85f3-65e9c598153d · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Sparse Fine-Tuning of Transformers for Generative Tasks An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 11

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verified fuzzy
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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.

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Observation e733b948-10e0-42db-a9fa-084f9ae63278 · outbound

This paper cites Transcoders enable fine-grained interpretable circuit analy- sis for language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Transcoders enable fine-grained interpretable circuit analy- sis for language models

Reference 12

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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.

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Observation 7f56e7ad-4ed9-49fd-879a-daa117a23228 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Sparse Fine-Tuning of Transformers for Generative Tasks The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1a028349-fa93-4346-92c9-f99bbf7d92ae · outbound

This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Sparse Fine-Tuning of Transformers for Generative Tasks Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 14

Resolution
verified fuzzy
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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.

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Observation 7de6e6f4-2ea0-4c63-b085-3349a69f266c · outbound

This paper cites Conceptexpress: Harnessing diffusion models for single-image unsupervised concept extraction.

Sparse Fine-Tuning of Transformers for Generative Tasks Conceptexpress: Harnessing diffusion models for single-image unsupervised concept extraction

Reference 15

Resolution
verified fuzzy
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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.

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Observation 3a2ce792-89af-4915-befe-4a32437de344 · outbound

This paper cites Lora: Low- rank adaptation of large language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Lora: Low- rank adaptation of large language models

Reference 16

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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.

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Observation 6243c206-58f7-4441-873e-e681dc3c66d9 · outbound

This paper cites SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation.

Sparse Fine-Tuning of Transformers for Generative Tasks SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation

Reference 17

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Observation d56e6243-efed-47b1-91b9-b4f6a5e88808 · outbound

This paper cites Text embed- ding is not all you need: Attention control for text-to-image semantic alignment with text self-attention maps.

Sparse Fine-Tuning of Transformers for Generative Tasks Text embed- ding is not all you need: Attention control for text-to-image semantic alignment with text self-attention maps

Reference 18

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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.

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Observation 30ca9e2b-c1cb-4e00-8ac2-9606dd80a881 · outbound

This paper cites Learning to Customize Text-to-Image Diffusion In Diverse Context.

Sparse Fine-Tuning of Transformers for Generative Tasks Learning to Customize Text-to-Image Diffusion In Diverse Context

Reference 19

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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.

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Observation 3835d217-b7bd-41d3-8148-244890f980a7 · outbound

This paper cites An introduction to variational autoencoders.

Sparse Fine-Tuning of Transformers for Generative Tasks An introduction to variational autoencoders

Reference 20

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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.

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Observation a66c29ff-09fc-40b2-ab90-2e6810ee15d6 · outbound

This paper cites Segment any- thing.

Sparse Fine-Tuning of Transformers for Generative Tasks Segment any- thing

Reference 21

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Observation ad84e6bb-dc24-44e0-a49a-789dcff9a340 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Sparse Fine-Tuning of Transformers for Generative Tasks Multi-concept customization of text-to-image diffusion

Reference 22

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Observation da99d513-7753-4c4a-9aec-2bf4bf27ee7d · outbound

This paper cites FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference.

Sparse Fine-Tuning of Transformers for Generative Tasks FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference

Reference 23

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Observation a0c2c29f-1e56-4d25-85f1-c33a7507450e · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Sparse Fine-Tuning of Transformers for Generative Tasks DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

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Observation 4b1c59e3-0870-4802-8836-45fd52964225 · outbound

This paper cites Re-Imagining Multimodal Instruction Tuning: A Representation View.

Sparse Fine-Tuning of Transformers for Generative Tasks Re-Imagining Multimodal Instruction Tuning: A Representation View

Reference 25

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

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Observation 73856219-331c-496e-bcdb-fb41f05e0c1f · outbound

This paper cites CAME: Confidence-guided Adaptive Memory Efficient Optimization.

Sparse Fine-Tuning of Transformers for Generative Tasks CAME: Confidence-guided Adaptive Memory Efficient Optimization

Reference 26

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verified exact
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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.

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Observation d70aca7b-cc9b-47dd-a216-6d314df84f84 · outbound

This paper cites Supervised dictionary learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Supervised dictionary learning

Reference 27

Resolution
verified fuzzy
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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.

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Observation bb28413e-c0a7-476d-a41b-ae3380648e15 · outbound

This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Sparse Fine-Tuning of Transformers for Generative Tasks Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 28

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9e79c806-f05c-446c-90c1-3fbdd0f7001d · outbound

This paper cites Con- tinual learning with filter atom swapping.

Sparse Fine-Tuning of Transformers for Generative Tasks Con- tinual learning with filter atom swapping

Reference 29

Resolution
verified fuzzy
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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.

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Observation d9a52c6a-4468-409f-8888-94d639654a8a · outbound

This paper cites Spatiotemporal joint filter decomposition in 3d convolutional neural networks.

Sparse Fine-Tuning of Transformers for Generative Tasks Spatiotemporal joint filter decomposition in 3d convolutional neural networks

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.718494Z

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.

source=pdf_text observed=2026-08-06T17:30:14.884423Z digest=sha256:b50fb1aedf9723e66d88c37a5d22c59dd294feb0c75c6de2c65e8524b8887f93

Observation ad1d9996-b7bf-4c0c-9e6f-f041426bb985 · outbound

This paper cites Training diffusion models towards diverse image generation with reinforcement learning.

Sparse Fine-Tuning of Transformers for Generative Tasks Training diffusion models towards diverse image generation with reinforcement learning

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.706928Z

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.

source=pdf_text observed=2026-08-06T17:30:14.888677Z digest=sha256:f8e884d03ee59e7dcbb9b1e42c83b936a631dd7c183680e8042d4ab4616fa103

Observation a2cac26c-8e79-4f7d-9e21-ecd4ccb78517 · outbound

This paper cites Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization.

Sparse Fine-Tuning of Transformers for Generative Tasks Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.892909Z digest=sha256:f58d89e7c1725678c5e5a20e1d7e4425335903dde6691dd9a82f780162e24532

Observation 047baf86-a9dc-4380-b604-eefe4fe28c9d · outbound

This paper cites Coeff-tuning: A graph filter subspace view for tuning attention-based large models.

Sparse Fine-Tuning of Transformers for Generative Tasks Coeff-tuning: A graph filter subspace view for tuning attention-based large models

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.695264Z

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.

source=pdf_text observed=2026-08-06T17:30:14.897163Z digest=sha256:a45999b09d9811f02ec10e79325e1c96786d71305e6aebb025a87a701ebc8fe3

Observation 4226990e-0c4e-420d-bfd4-4b963543249b · outbound

This paper cites Emergence of simple- cell receptive field properties by learning a sparse code for natural images.

Sparse Fine-Tuning of Transformers for Generative Tasks Emergence of simple- cell receptive field properties by learning a sparse code for natural images

Reference 34

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raw_fallback, observed 2026-08-06T17:30:15.683911Z

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.

source=pdf_text observed=2026-08-06T17:30:14.900927Z digest=sha256:7e1db2b6a54bed455723e4663396fc3caf5e6e080ae1b2cf93d67d743be9cfa1

Observation 00d3af64-ebc0-49a5-9126-3751ca30ec2f · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Sparse Fine-Tuning of Transformers for Generative Tasks DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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unresolved
no resolver link, observed 2026-08-06T17:30:14.904940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.904940Z digest=sha256:a57115ec3e4c880c7dc56610cc9323fac4ac217df587c89d99d522c65a82315f

Observation 129da327-983e-4cae-894e-57db7ff3ddf4 · outbound

This paper cites Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement.

Sparse Fine-Tuning of Transformers for Generative Tasks Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:30:15.173147Z

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.

source=pdf_text observed=2026-08-06T17:30:14.909776Z digest=sha256:bcd0ef16d75829aff8f2f9f3cc68da6361a688d503694f7c95760cd098e156c6

Observation 82d6a9a0-8b77-4e86-b3d9-e90d4894b5a9 · outbound

This paper cites Scalable diffusion models with transformers.

Sparse Fine-Tuning of Transformers for Generative Tasks Scalable diffusion models with transformers

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.569159Z

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.

source=pdf_text observed=2026-08-06T17:30:14.914154Z digest=sha256:876a79e58f0b080dc076fa2358bc95f1a4366aab9ef394c74d3b8922433f7ef5

Observation 08d02a7f-6875-4edd-a70e-3a8a6c5bbcd4 · outbound

This paper cites Posterior sampling via Langevin dynamics based on generative priors.

Sparse Fine-Tuning of Transformers for Generative Tasks Posterior sampling via Langevin dynamics based on generative priors

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T17:30:14.917980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.917980Z digest=sha256:7f77af98089ad48dab10359b6d4f9decb21a7fe290f1847072ec33dc1fbe98b8

Observation 9d05a825-ac72-4027-9e9a-4b07fed1450b · outbound

This paper cites Dcfnet: Deep neural network with decomposed convolutional filters.

Sparse Fine-Tuning of Transformers for Generative Tasks Dcfnet: Deep neural network with decomposed convolutional filters

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.557582Z

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.

source=pdf_text observed=2026-08-06T17:30:14.921851Z digest=sha256:188d14b565295b693cd6582dae6df6bf6fc54b2ba9f3666931d32fe97b826391

Observation 0dbe1858-2f38-4fc6-adc9-797e0d1c4f37 · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.

Sparse Fine-Tuning of Transformers for Generative Tasks Controlling text-to-image diffusion by orthogo- nal finetuning

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.546721Z

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.

source=pdf_text observed=2026-08-06T17:30:14.926623Z digest=sha256:7e9b93540ada01ece4e29e6c55e37d151d77dad68a2e4b70de0b975d04dfd945

Observation ace7e34a-7272-4691-b644-997b24177ea2 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Sparse Fine-Tuning of Transformers for Generative Tasks Learning transferable visual models from natural language supervi- sion

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.534676Z

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.

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Observation 876665a2-20a8-42e7-8e31-45eaa8af2c1d · outbound

This paper cites Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention.

Sparse Fine-Tuning of Transformers for Generative Tasks Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.522994Z

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.

source=pdf_text observed=2026-08-06T17:30:14.934714Z digest=sha256:58864ddebe02f0176fb5fd7ae5a30783cdf16cffd901c2e01b133ec31a96c5fd

Observation b318066c-1caa-4b6d-89c2-7b4135f8deb3 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks High-resolution image syn- thesis with latent diffusion models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T17:30:14.938952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.938952Z digest=sha256:4aa653f09e66b084420da738c294ba513396036cc692597c6ee0182d528c5ce2

Observation eae68459-335e-42c9-9f79-2dd68d48081f · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.505290Z

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.

source=pdf_text observed=2026-08-06T17:30:14.943156Z digest=sha256:7ad8ad8196f8e772e7e2ba46c2cefa067310a1b71ae386ff723d5682774a890a

Observation f54b1b02-f063-4457-903f-236c4c0922c6 · outbound

This paper cites Unpacking sdxl turbo: Interpreting text-to-image models with sparse au- toencoders.

Sparse Fine-Tuning of Transformers for Generative Tasks Unpacking sdxl turbo: Interpreting text-to-image models with sparse au- toencoders

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T17:30:14.947269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.947269Z digest=sha256:549414b9a764bac8d4d26a42ec379bf828b662a745c03df1d5a3dbf3a5324c5e

Observation 29c649b9-94a5-4f6a-a97d-e665b6fa74cc · outbound

This paper cites Sparse sinkhorn attention.

Sparse Fine-Tuning of Transformers for Generative Tasks Sparse sinkhorn attention

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.494150Z

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.

source=pdf_text observed=2026-08-06T17:30:14.951564Z digest=sha256:ecc9100c80a27a90ad29d8e3c9122d96e2c18be36947b62150d3ad33a561f158

Observation 68793c4a-d8bc-43c8-b927-4c223eb6b933 · outbound

This paper cites Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques.

Sparse Fine-Tuning of Transformers for Generative Tasks Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:30:15.058609Z

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.

source=pdf_text observed=2026-08-06T17:30:14.955831Z digest=sha256:a011f377a8c2838c30e41403c3633288db0cbd336769e839d90dae8b544bbcaa

Observation dad8f7eb-841b-43dd-87e0-f4984279b962 · outbound

This paper cites Attention is all you need.

Sparse Fine-Tuning of Transformers for Generative Tasks Attention is all you need

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.482518Z

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.

source=pdf_text observed=2026-08-06T17:30:14.959840Z digest=sha256:ea769ebfa8bfd66b0fb2b9dabb0faf8485932f31f3b3e6f88c21d595d4edbe4b

Observation dbcc7baf-124c-4b95-9dd1-785ae1449fe4 · outbound

This paper cites Stochastic Conditional Generative Networks with Basis Decomposition.

Sparse Fine-Tuning of Transformers for Generative Tasks Stochastic Conditional Generative Networks with Basis Decomposition

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:30:15.040899Z

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.

source=pdf_text observed=2026-08-06T17:30:14.963651Z digest=sha256:17bff0f8efea89371284571a56605517940c2f799766a8df92ae2ca75d8a408c

Observation 8879c03b-fe14-48c9-8b58-fae899a69f9f · outbound

This paper cites Image generation using continuous filter atoms.

Sparse Fine-Tuning of Transformers for Generative Tasks Image generation using continuous filter atoms

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.470459Z

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.

source=pdf_text observed=2026-08-06T17:30:14.967362Z digest=sha256:a2bfa19cb74404201fc8257c8f155a9c3eb52df3d11b7dd5df04a96831eb3a15

Observation 1cde644b-e6fb-42af-8dd7-e72b3aa0e45b · outbound

This paper cites Adaptive convolutions with per-pixel dynamic filter atom.

Sparse Fine-Tuning of Transformers for Generative Tasks Adaptive convolutions with per-pixel dynamic filter atom

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.458829Z

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.

source=pdf_text observed=2026-08-06T17:30:14.970950Z digest=sha256:7aa2093898b434c8e0cb461d3820226cbad67d4219cb1c5e5beb098f2b9c6636

Observation 5148cfb5-adff-4abc-963d-589070319bdd · outbound

This paper cites Advancing parameter efficiency in fine-tuning via representation editing.

Sparse Fine-Tuning of Transformers for Generative Tasks Advancing parameter efficiency in fine-tuning via representation editing

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.446692Z

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.

source=pdf_text observed=2026-08-06T17:30:14.974721Z digest=sha256:efda73ce4a92a0997470a765c835aaafe2feaafbf46eb0a004de63d3c267e270

Observation 8b95fe18-a91b-4d50-abd3-f9d3c7096558 · outbound

This paper cites Reft: Representation finetuning for language models.

Sparse Fine-Tuning of Transformers for Generative Tasks Reft: Representation finetuning for language models

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.435755Z

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.

source=pdf_text observed=2026-08-06T17:30:14.978232Z digest=sha256:39428f79ce6f0e6eaef1cd3635c9346640a2e5154779bebb0f6766649845b671

Observation bba9694a-4656-4932-b54f-0b645d47c6db · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

Sparse Fine-Tuning of Transformers for Generative Tasks Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.425238Z

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.

source=pdf_text observed=2026-08-06T17:30:14.981779Z digest=sha256:bed5d0fef81b6b15acee6c911c722fa80a71627c20919cc3f132e5f397960d6a

Observation 316a5f64-7c05-4e57-bef7-91016736f517 · outbound

This paper cites an unresolved cited work.

Sparse Fine-Tuning of Transformers for Generative Tasks Unresolved cited work

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T17:30:14.985479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:30:14.985479Z digest=sha256:2ed2491ee8b8d6d065af7c86c4f7434f8cd7331b3a663feaeb8fb5d328e8b4d5

Observation 8bafb27a-d84f-488f-86c2-879c47e3b7a2 · outbound

This paper cites Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.404694Z

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.

source=pdf_text observed=2026-08-06T17:30:14.989372Z digest=sha256:768145cf2bf5b28174dff7f62de0da3fe28f0623eae4d43b36e270d5779d63fb

Observation bd515564-5b75-46c1-9d9f-cf33c271640c · outbound

This paper cites Object- conditioned energy-based attention map alignment in text-to- image diffusion models.

Sparse Fine-Tuning of Transformers for Generative Tasks Object- conditioned energy-based attention map alignment in text-to- image diffusion models

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.391151Z

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.

source=pdf_text observed=2026-08-06T17:30:14.993979Z digest=sha256:f408a9ebddd328d4ab4ed63a26d9f251c1dfd9ac2b24003a1b6b1d25ed91a161

Observation a9bd85a6-a7e8-4dd8-88cf-71992dbb7594 · outbound

This paper cites A grey ⟨V ⟩ wolf plushie.

Sparse Fine-Tuning of Transformers for Generative Tasks A grey ⟨V ⟩ wolf plushie

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:30:15.378776Z

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.

source=pdf_text observed=2026-08-06T17:30:14.998791Z digest=sha256:996089edc92deca7257e6463f8473d640bb3fd8eea3634c9414a0e91cfe30cda

Pith citing papers

No inbound Pith citation observations are available.