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Paper Citation Record · LEDGER

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.09015.

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

pith.paper-citation-record.v1
2509.09015 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:52:11.369713Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

18 of 18 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a62da8a-e83f-4095-aee5-066d40763e50 · outbound

This paper cites Using goal-driven deep learning models to understand sensory cortex,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Using goal-driven deep learning models to understand sensory cortex,

Reference 1

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T19:52:11.316027Z digest=sha256:bcf8d380855043816968051dea8d15972f4e809fa283203c264c246a61e93555

Observation f291f88c-032a-452e-a22a-e4eedc010377 · outbound

This paper cites Machine Learning for Neural Decoding,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Machine Learning for Neural Decoding,

Reference 2

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source=pdf_text observed=2026-08-04T19:52:11.319684Z digest=sha256:8c4597e91edd5d2ceb2137ec39ca6acb052ae92bb29f6b377b220d880f4f1403

Observation 5e3fc9a8-2b48-4c6e-9108-bc7a5ce3c16b · outbound

This paper cites Encoding and decoding in fMRI,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Encoding and decoding in fMRI,

Reference 3

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source=pdf_text observed=2026-08-04T19:52:11.322954Z digest=sha256:aa577a5636f89662713606cd8610f93497a3a2b9821c5b16aa0cbc2cb3bb5762

Observation 3b2e199e-e610-4186-928e-387d371d12aa · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Learning Transferable Visual Models From Natural Language Supervision

Reference 4

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source=pdf_text observed=2026-08-04T19:52:11.326069Z digest=sha256:7581c74b5501cd1d53f05724631a1055ec40f5b10f392c9fdfbf81f20b926869

Observation 0c99a33f-8c50-4fa6-b8e0-b90002794fbb · outbound

This paper cites A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence,

Reference 5

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source=pdf_text observed=2026-08-04T19:52:11.329448Z digest=sha256:4ae879b2c60dc3209c77238ed29720b97833ecea5120fab22449833c2358ad57

Observation 58c8f021-6702-4192-bf09-a10e13171eff · outbound

This paper cites Deep image re- construction from human brain activity,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Deep image re- construction from human brain activity,

Reference 6

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source=pdf_text observed=2026-08-04T19:52:11.332746Z digest=sha256:e1864893fcc719281f0ee5fd836efc9a3d1b8edd31b9a1284bd61a74652b512c

Observation f532790e-41fb-4012-96de-a6f46a911182 · outbound

This paper cites Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors

Reference 7

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source=pdf_text observed=2026-08-04T19:52:11.336397Z digest=sha256:29033b2293e12f21e0147631c094c378e18a7a0bf397f079ea0482c431ddf7ca

Observation fd7c712d-18e8-41be-bbc8-09426b2dbe16 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Learning transferable visual models from natural language supervision,

Reference 8

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source=pdf_text observed=2026-08-04T19:52:11.339406Z digest=sha256:2ff37e5157bd803a5df068e99d9e7d269a9d37503f50358365b51efc125e6f73

Observation 60e4ddef-81aa-4dca-b673-9c48203094e7 · outbound

This paper cites MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data

Reference 9

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source=pdf_text observed=2026-08-04T19:52:11.342503Z digest=sha256:6776c8c8b2e8436ca96c388964ba095952eb46e6ffc6da84186e857c43a96460

Observation 59378ebe-5fba-4b9a-8c7e-d3b2848b28ae · outbound

This paper cites MindBridge: A Cross-Subject Brain Decoding Framework.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI MindBridge: A Cross-Subject Brain Decoding Framework

Reference 10

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source=pdf_text observed=2026-08-04T19:52:11.345884Z digest=sha256:5590e4cccd2565d21ea6be47848b43f4165f2be766878ad11c3abb281a0d7c03

Observation 12e972e8-3b02-4adf-b5bc-d886f8c716be · outbound

This paper cites Token merging: Your ViT but faster,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Token merging: Your ViT but faster,

Reference 11

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source=pdf_text observed=2026-08-04T19:52:11.349038Z digest=sha256:8ad0147476563f1394511473d1a0d171706aadb88128be13afefdef8bb59b4db

Observation 2c5f1b57-27d3-437e-9de5-c40df6ae6f0b · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Tokens-to-token vit: Training vision transformers from scratch on imagenet,

Reference 12

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source=pdf_text observed=2026-08-04T19:52:11.352139Z digest=sha256:e1832866995d25e43664f7d13679ece5e113723b824e451b83f617cdec38e548

Observation 3b6588c4-3e71-4b34-8a32-cac915e6a073 · outbound

This paper cites Perceiver: General Perception with Iterative Attention,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Perceiver: General Perception with Iterative Attention,

Reference 13

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source=pdf_text observed=2026-08-04T19:52:11.355309Z digest=sha256:1a8ae80fb5722018fa5d670703d577b72f5d99b93db9cc1c69bb4ebd476a604e

Observation 6c62ce8b-5af6-49e7-801b-21a95a5f7041 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Blip-2: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 14

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source=pdf_text observed=2026-08-04T19:52:11.358365Z digest=sha256:cec80091985a304e9e98dfe37a7400ce42eb01c583aae5b53590800af4780fce

Observation 465def28-c9c5-4a4c-93d1-0867a736cbf2 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Microsoft COCO: Common Objects in Context

Reference 15

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source=pdf_text observed=2026-08-04T19:52:11.361198Z digest=sha256:1dbf0e789caca60dd0e7a2674fb2fd8d5e8f5bfdde55b5f80782b1c2d322bdd2

Observation 1653feee-2aa2-4bfe-9575-fd704e15310d · outbound

This paper cites Implicit neural representations with periodic activation functions,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Implicit neural representations with periodic activation functions,

Reference 16

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source=pdf_text observed=2026-08-04T19:52:11.364123Z digest=sha256:bedc5a7c4b559b97611f31418a13ba6de0fd9d8129cce36888afb25d7b4f1b19

Observation cb7f7c3e-8696-431c-a076-ad8ac0a8c908 · outbound

This paper cites BLIP: Bootstrapping language- image pre-training for unified vision-language understanding and generation,.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI BLIP: Bootstrapping language- image pre-training for unified vision-language understanding and generation,

Reference 17

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source=pdf_text observed=2026-08-04T19:52:11.366727Z digest=sha256:c8cabfa37f16d014760ac73b3f7f6a569465eb088d74557da03e1490ee9f905b

Observation 1e72f337-1941-4422-9afd-cf730e9183d9 · outbound

This paper cites Natural scene reconstruction from fMRI signals using generative latent diffusion.

VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI Natural scene reconstruction from fMRI signals using generative latent diffusion

Reference 18

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source=pdf_text observed=2026-08-04T19:52:11.369713Z digest=sha256:1fdc04a37e01356174f1c6ebfee14111d9466f2f5fa34d6f50fa6d272f15a726

Pith citing papers

No inbound Pith citation observations are available.