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Paper Citation Record · LEDGER

GG-SSMs: Graph-Generating State Space Models

As of 20 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2412.12423.

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

pith.paper-citation-record.v1
2412.12423 v2

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:00.501033Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

68 of 68 outbound references displayed

  • verified exact0
  • verified fuzzy53
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8fdb0eb-559a-4f4c-9ea8-b9b0a37a8011 · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 2502e19f-8022-4ab4-b477-b01e7ce8d8ef · outbound

This paper cites A 2- dimensional state space layer for spatial inductive bias.

GG-SSMs: Graph-Generating State Space Models A 2- dimensional state space layer for spatial inductive bias

Reference 2

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation d582ac99-aeae-4c62-bf5e-76baf0c23b36 · outbound

This paper cites Retina : Low-power eye tracking with event camera and spiking hardware.

GG-SSMs: Graph-Generating State Space Models Retina : Low-power eye tracking with event camera and spiking hardware

Reference 3

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1daa8956-fca1-4179-a977-40831533d926 · outbound

This paper cites A 240 × 180 130 db 3 µs latency global shutter spatiotemporal vision sensor.

GG-SSMs: Graph-Generating State Space Models A 240 × 180 130 db 3 µs latency global shutter spatiotemporal vision sensor

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9918315b-237e-4848-8381-b932ffde0331 · outbound

This paper cites Butler, Jonas Wulff, Garrett B.

GG-SSMs: Graph-Generating State Space Models Butler, Jonas Wulff, Garrett B

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7663de30-4c28-40e6-8b71-1e71bd39942e · outbound

This paper cites A minimum spanning tree algorithm with inverse-ackermann type complexity.

GG-SSMs: Graph-Generating State Space Models A minimum spanning tree algorithm with inverse-ackermann type complexity

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 889c6443-cf76-40cd-98f0-c813f22fff82 · outbound

This paper cites 3et: Efficient event-based eye tracking using a change-based convlstm network.

GG-SSMs: Graph-Generating State Space Models 3et: Efficient event-based eye tracking using a change-based convlstm network

Reference 7

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raw_fallback, observed 2026-08-11T14:11:02.029386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation c0dcbaee-7b03-4dbe-8cc5-52ed56570105 · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

GG-SSMs: Graph-Generating State Space Models Randaugment: Practical automated data augmentation with a reduced search space

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b6712030-0108-42b1-b428-6ab57ac63181 · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

GG-SSMs: Graph-Generating State Space Models Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a3a3c2e3-f9e6-4e76-abd4-3296822e9b27 · outbound

This paper cites Li, and Li Fei-Fei.

GG-SSMs: Graph-Generating State Space Models Li, and Li Fei-Fei

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 527f941e-259d-4ad0-9f0a-215291d83748 · outbound

This paper cites Memflow: Optical flow esti- mation and prediction with memory.

GG-SSMs: Graph-Generating State Space Models Memflow: Optical flow esti- mation and prediction with memory

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b2998587-2747-49fd-834a-8d77132df38a · outbound

This paper cites Rethinking op- tical flow from geometric matching consistent perspective.

GG-SSMs: Graph-Generating State Space Models Rethinking op- tical flow from geometric matching consistent perspective

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ee5a9f89-8f89-4c34-89cc-9ae3510eea54 · outbound

This paper cites Flownet: Learn- ing optical flow with convolutional networks.

GG-SSMs: Graph-Generating State Space Models Flownet: Learn- ing optical flow with convolutional networks

Reference 13

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ba0d7dcb-7412-4d6f-993e-cb22d0d32c65 · outbound

This paper cites K¨ubler, and Andrea Mazzei.

GG-SSMs: Graph-Generating State Space Models K¨ubler, and Andrea Mazzei

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 975bdb2a-34ae-4930-8072-0728231cc381 · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 15

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raw_fallback, observed 2026-08-11T14:11:01.853149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 4874b218-baba-4443-a02e-ec8e602223fa · outbound

This paper cites Fu, Tri Dao, Khaled K.

GG-SSMs: Graph-Generating State Space Models Fu, Tri Dao, Khaled K

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7c7d09d7-33a6-4572-8dbd-e09f39ffbcc4 · outbound

This paper cites Orchard, Chiara Bar- tolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, An- drew J.

GG-SSMs: Graph-Generating State Space Models Orchard, Chiara Bar- tolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, An- drew J

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 85a8caf1-f36f-4531-b95c-0fb7d070c0a3 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

GG-SSMs: Graph-Generating State Space Models Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation c55fdcab-bb57-4fc2-9471-5be11039a321 · outbound

This paper cites Mamba: Linear-time sequence mod- eling with selective state spaces.

GG-SSMs: Graph-Generating State Space Models Mamba: Linear-time sequence mod- eling with selective state spaces

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.771416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8f520e63-2ebd-44f4-9d8c-e2ebf0c38770 · outbound

This paper cites Combining recurrent, con- volutional, and continuous-time models with linear state- space layers.

GG-SSMs: Graph-Generating State Space Models Combining recurrent, con- volutional, and continuous-time models with linear state- space layers

Reference 20

Resolution
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raw_fallback, observed 2026-08-11T14:11:01.752933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1b2c0b10-9057-4e01-b328-a6cbdfaa5932 · outbound

This paper cites Efficiently mod- eling long sequences with structured state spaces.

GG-SSMs: Graph-Generating State Space Models Efficiently mod- eling long sequences with structured state spaces

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 14de0d7d-6cc3-44ab-8ee3-38b6cdee213f · outbound

This paper cites Deep residual learning for image recognition.

GG-SSMs: Graph-Generating State Space Models Deep residual learning for image recognition

Reference 22

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no resolver link, observed 2026-08-11T14:11:00.196435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.196435Z digest=sha256:84278890b6563bfa20d4c5aa1ec050238dfd9ffc7f62df79d97ed6c63abd14fe

Observation 092531cd-4a12-4d49-b255-1a466dd182b2 · outbound

This paper cites Zigma: A dit-style zigzag mamba diffusion model.

GG-SSMs: Graph-Generating State Space Models Zigma: A dit-style zigzag mamba diffusion model

Reference 23

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raw_fallback, observed 2026-08-11T14:11:01.691962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6465e043-c21c-408f-b85b-a0845805f04a · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

GG-SSMs: Graph-Generating State Space Models LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 24

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Observation 1e1f09ac-7f1b-497e-88e2-81a4de9f599d · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 25

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 38ecc308-8719-43e0-bd71-0b7dcf60f5fe · outbound

This paper cites K ´alm´an.

GG-SSMs: Graph-Generating State Space Models K ´alm´an

Reference 26

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e951adef-8674-4193-9e89-3df52d733b6b · outbound

This paper cites Kipf and Max Welling.

GG-SSMs: Graph-Generating State Space Models Kipf and Max Welling

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.623461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 2f86f256-0914-49f7-92ba-f4cc425ef75b · outbound

This paper cites Revisiting long- term time series forecasting: An investigation on linear map- ping.

GG-SSMs: Graph-Generating State Space Models Revisiting long- term time series forecasting: An investigation on linear map- ping

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.602537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.229564Z digest=sha256:ed13419dc8ab4acb9e1ce6497276dc71f317916ccc2bea88e4f0b934ae2fa5be

Observation a5caccdb-498a-46ad-b374-9db073bedb3d · outbound

This paper cites itransformer: In- verted transformers are effective for time series forecasting.

GG-SSMs: Graph-Generating State Space Models itransformer: In- verted transformers are effective for time series forecasting

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.584903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.235707Z digest=sha256:42d89a34c192f177b051f855535f0ae2b08d6b1943e0073e231a01261a9ca418

Observation c2090925-6778-40b2-9d62-7d9ba4455329 · outbound

This paper cites Vmamba: Visual state space model.

GG-SSMs: Graph-Generating State Space Models Vmamba: Visual state space model

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.557785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6463d82c-27c4-4838-86f6-70982b6e21c7 · outbound

This paper cites Swin trans- former: Hierarchical vision transformer using shifted win- dows.

GG-SSMs: Graph-Generating State Space Models Swin trans- former: Hierarchical vision transformer using shifted win- dows

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.535279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 367a300d-98f0-4733-b80b-ed2e0f2507df · outbound

This paper cites A convnet for the 2020s.

GG-SSMs: Graph-Generating State Space Models A convnet for the 2020s

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.517221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.252356Z digest=sha256:8f035ac9eef00e8e746be8ff4e61ec94f59aafc0dbdd9e0c6aceab41f31733f8

Observation 78b0e36a-6946-4fbf-8042-afdcc9232696 · outbound

This paper cites Decoupled weight decay regularization.

GG-SSMs: Graph-Generating State Space Models Decoupled weight decay regularization

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T14:11:00.257856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.257856Z digest=sha256:05fe1b85d69d59df9d4c7165b0af1583b9778a204da5113b0daf124b5d5db341

Observation 7b5d4269-c61b-4bcc-af17-929e073ec00d · outbound

This paper cites Chen, Huaijin Chen, and Dongfang Liu.

GG-SSMs: Graph-Generating State Space Models Chen, Huaijin Chen, and Dongfang Liu

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.479795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 970a277e-354a-44a7-99f7-26e036bf9a1d · outbound

This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation.

GG-SSMs: Graph-Generating State Space Models A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.459609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 20732687-f701-407f-b21c-74467f275b61 · outbound

This paper cites Downs, Preey Shah, Tri Dao, Stephen A.

GG-SSMs: Graph-Generating State Space Models Downs, Preey Shah, Tri Dao, Stephen A

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.439273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.272990Z digest=sha256:598b2fd45917e22cda592e416996240bd2aa3362e59b9bf9fa9da52a2f894036

Observation 081749ea-23d4-46b9-8d91-d91faf0a4688 · outbound

This paper cites Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.

GG-SSMs: Graph-Generating State Space Models Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.412513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.278689Z digest=sha256:f735988e17ac90e94191d83948adf095b25b4849d8c2a57f7d97d90a6261de6a

Observation 5b4dd3b9-308b-49a7-b628-11a8065a1ee5 · outbound

This paper cites Ssm meets video diffusion models: Efficient long-term video generation with structured state spaces,.

GG-SSMs: Graph-Generating State Space Models Ssm meets video diffusion models: Efficient long-term video generation with structured state spaces,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.393391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.283941Z digest=sha256:c769d5d9b950a03892553ef9714d3553fe534709990ee782abe44c27ad1e2bf4

Observation 4fed30bf-8a35-48c7-be7c-9cb94dc2dde7 · outbound

This paper cites Deep-learning-based pupil center detection and tracking technology for visible-light wearable gaze tracking devices.

GG-SSMs: Graph-Generating State Space Models Deep-learning-based pupil center detection and tracking technology for visible-light wearable gaze tracking devices

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.376533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.290564Z digest=sha256:b36dbbe18b40e83295fc826108fc9f500e6ebb8c9aa207485d0f1b3546abb9d5

Observation 518ded47-56e4-4f87-891a-f4914e3b7abe · outbound

This paper cites Sudderth, and Jan Kautz.

GG-SSMs: Graph-Generating State Space Models Sudderth, and Jan Kautz

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.359218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.296763Z digest=sha256:f01a22972a0df7cd815666b3e7168eadbb2dd2829e44ed3a7a5298d935f047f6

Observation bd1a2bb5-aeb8-4c51-98bb-2d9ddc897fa1 · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:11:01.339400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.301765Z digest=sha256:b4bd74b96b97ad7b17e7044e5efd4fc44dc9b799ffd298017e866da5e5244ee1

Observation 8226d345-b88e-4bc0-b101-5a3b2b3b1ac2 · outbound

This paper cites See, Hongwei Qin, Jifeng Dai, and Hongsheng Li.

GG-SSMs: Graph-Generating State Space Models See, Hongwei Qin, Jifeng Dai, and Hongsheng Li

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.313563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.306432Z digest=sha256:130977b1b3607e16be41fa3587e5918d4302d1766583addafd132614d796027e

Observation 6e7a0c5a-d7e7-4269-9589-84c4c00804f4 · outbound

This paper cites See, Hongwei Qin, Jifeng Dai, and Hongsheng Li.

GG-SSMs: Graph-Generating State Space Models See, Hongwei Qin, Jifeng Dai, and Hongsheng Li

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.292236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.313396Z digest=sha256:d89210ed15532dc988c92f12f820ef6de946555e589aa1876fce377dcb4dbe3e

Observation 8c9a0807-e6b1-4da0-a4a7-4c6f4e22ca3b · outbound

This paper cites Smith, Andrew Warrington, and Scott Linder- man.

GG-SSMs: Graph-Generating State Space Models Smith, Andrew Warrington, and Scott Linder- man

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.268000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.318305Z digest=sha256:8b71fca505457b627eb89afce1f43c271126c517ab0cfcfc2cd66383ec53505e

Observation b1e195f8-8a8b-42d6-9c56-a3affe3f6c75 · outbound

This paper cites S7: Selective and simplified state space layers for sequence modeling, 2024.

GG-SSMs: Graph-Generating State Space Models S7: Selective and simplified state space layers for sequence modeling, 2024

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.248488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.324363Z digest=sha256:d7e8f6ef02aa0770577d6a3a7a9602da2bf862132a75a055c006aad92bf414e1

Observation bb584a76-d6af-4dfd-a3d7-3f7569e8e11a · outbound

This paper cites Zhu, Guodong Guo, and Gezhong Li.

GG-SSMs: Graph-Generating State Space Models Zhu, Guodong Guo, and Gezhong Li

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.228718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.329648Z digest=sha256:245a501e4ca31269a7bb0830fd3e0e6b008c423bb10007467b2cda6f7b722c9c

Observation d389e8cf-6dde-42e4-bbbd-dbd0fe6fbd44 · outbound

This paper cites Jamba-1.5: Hybrid Transformer-Mamba Models at Scale.

GG-SSMs: Graph-Generating State Space Models Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T14:11:00.334311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.334311Z digest=sha256:579afb7b9c44b07e2a6bcdcccad506ac0044939a3a4a374bd82acd5fab452f57

Observation 4eb849e5-7519-4b88-8f4a-ab571c1d7fe4 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

GG-SSMs: Graph-Generating State Space Models Raft: Recurrent all-pairs field transforms for optical flow

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.210170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.340086Z digest=sha256:dc37291223e509358946e240ef2c124b54353a0284b78718d59a615540a34f80

Observation 67ed223f-9ab1-4cb3-88cf-8124f02f5224 · outbound

This paper cites Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained environments.

GG-SSMs: Graph-Generating State Space Models Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained environments

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.193865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.345353Z digest=sha256:e00cae8a12b4fadec42e15a334f247517f8cc364d2903f98feabbd862e5cb23f

Observation fa9ae1b4-6628-4511-b68e-3b01ba196530 · outbound

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

GG-SSMs: Graph-Generating State Space Models Train- ing data-efficient image transformers & distillation through attention

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.176370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.351191Z digest=sha256:ad6c41f90f55d0ffb148f7879e1eb93ec5a9a443ee1dfebe9484a80503015c37

Observation 849b19cf-1d6e-4c60-b956-eb8590d926d2 · outbound

This paper cites Attention is all you need.

GG-SSMs: Graph-Generating State Space Models Attention is all you need

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.156374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.358212Z digest=sha256:e9345e65bbaecf11abc5e147f2526eac5f042e5fdb71bc7e6a00305eb7419708

Observation e1b4f17c-c77b-4484-a2dd-97cd14af2165 · outbound

This paper cites Is Mamba Effective for Time Series Forecasting?.

GG-SSMs: Graph-Generating State Space Models Is Mamba Effective for Time Series Forecasting?

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T14:11:00.363838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.363838Z digest=sha256:454ee15e81ffb7ba3a252455f02409ae3ebd3b5cd8b7f06c60870485df0c7ddc

Observation 1acc1c55-d050-476c-bd78-6d3dd8ad8abe · outbound

This paper cites Timesnet: Temporal 2d- variation modeling for general time series analysis.

GG-SSMs: Graph-Generating State Space Models Timesnet: Temporal 2d- variation modeling for general time series analysis

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.135535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.369799Z digest=sha256:c45b062524d4206b4f110f33dcb3df3c7874a3df5ebb9a5142b354c4e2bce250

Observation 71b111f1-186f-4f25-971d-b6b6326d064c · outbound

This paper cites GrootVL: Tree Topology is All You Need in State Space Model.

GG-SSMs: Graph-Generating State Space Models GrootVL: Tree Topology is All You Need in State Space Model

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-11T14:11:00.375268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.375268Z digest=sha256:f339a9b16228b099691ad1827b4da1c42bda1504ae2889221376777e08899430

Observation 850ec9be-52d8-432a-8ce4-0abc7b3f80f3 · outbound

This paper cites Gmflow: Learning optical flow via global matching.

GG-SSMs: Graph-Generating State Space Models Gmflow: Learning optical flow via global matching

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.110739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.382689Z digest=sha256:654445f6012a6a577b6f287547b1b53d04dfdc6d2d8212b7bb9f514f063160e7

Observation 2e565b43-c237-410c-b7a9-195b6e7a96f5 · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:11:01.090313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.389220Z digest=sha256:a0e57110aa14e05d80e5f0090bcb364a9147dfbcb58189a9c2edf019f42c400f

Observation 3350f5b6-d90f-481c-8ced-0e1aae02cc83 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

GG-SSMs: Graph-Generating State Space Models Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.065541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.403106Z digest=sha256:5f0c8ec600de67a42d9d7b6a5547ac0d2575c88bd3bf351de9d2fdbfd988b709

Observation ce657e0d-5628-460f-b576-64a1989c75b5 · outbound

This paper cites Zhang, and Qiang Xu.

GG-SSMs: Graph-Generating State Space Models Zhang, and Qiang Xu

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.041280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.423392Z digest=sha256:ca0c46ed29a7e7331f0679faca4b6519cde916b16297e1127f637c950562f2d2

Observation a86968ed-cb5c-4665-b7c4-920d0c475cab · outbound

This paper cites mixup: Beyond empirical risk minimization.

GG-SSMs: Graph-Generating State Space Models mixup: Beyond empirical risk minimization

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:01.018300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.431517Z digest=sha256:361d66f4e848116f90292c0a1953e01f7d96bcc8b75b0ae9b9f0a60994e15a05

Observation 44efd3c1-4d68-41fd-bf2e-dd1fa8531378 · outbound

This paper cites HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling.

GG-SSMs: Graph-Generating State Space Models HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T14:11:00.442638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:11:00.442638Z digest=sha256:2c357a45f1c6bd1e1f78aedc67c3425d82e72f3531494fc4cbeca8d24a5a0d6b

Observation 556e7fe9-32f5-46ff-8517-f45fda867639 · outbound

This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting.

GG-SSMs: Graph-Generating State Space Models Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.993904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.456076Z digest=sha256:84dd1e5b1e9211efd35114db111b680677183790dfca12fa6edaeba26f4c43dc

Observation 0293c1c5-a8b5-4ef5-a8ee-d5855502dc05 · outbound

This paper cites Ev-eye: Rethink- ing high-frequency eye tracking through the lenses of event cameras.

GG-SSMs: Graph-Generating State Space Models Ev-eye: Rethink- ing high-frequency eye tracking through the lenses of event cameras

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.970406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.462432Z digest=sha256:cb1ef1465542b56cba53d9fcc431fb36505f6ee6bb06b9d9943538d6852ebd96

Observation 246e9cd0-e47f-499e-bd96-f18cead81966 · outbound

This paper cites an unresolved cited work.

GG-SSMs: Graph-Generating State Space Models Unresolved cited work

Reference 64

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:11:00.946734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.470432Z digest=sha256:131ca15c3df4cebeb0b086f7d2e5cd4c49ba66eeacd2292d1bf26d6ea8d5e9bd

Observation e01a7b96-74f2-4410-a59e-f7e6bda54862 · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model.

GG-SSMs: Graph-Generating State Space Models Vision mamba: Efficient visual representation learning with bidirectional state space model

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.918076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.477143Z digest=sha256:83c888336717de7ea68f2928e7fef7d4ff3f9be70e38c24b4fadfc4b4355eb2c

Observation 617bccda-2f3f-4197-ab3e-a041b7743193 · outbound

This paper cites An effective loss function for generating 3d models from single 2d image without render- ing.

GG-SSMs: Graph-Generating State Space Models An effective loss function for generating 3d models from single 2d image without render- ing

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.890105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.482339Z digest=sha256:785d01d5fd7360b8121f140e4e5ead60a6c4a03a8db93ab61cacf20718710356

Observation 5ec50ff1-74a5-4a4a-a15b-37bb27589411 · outbound

This paper cites From chaos comes order: Ordering event rep- resentations for object recognition and detection.

GG-SSMs: Graph-Generating State Space Models From chaos comes order: Ordering event rep- resentations for object recognition and detection

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.866185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.489431Z digest=sha256:858688660c051c0a52e26f9015876f1136e9a46885141a36c104d30184412529

Observation ba967ad1-9c56-421a-b754-f8be74bad47a · outbound

This paper cites State space models for event cameras.

GG-SSMs: Graph-Generating State Space Models State space models for event cameras

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.833747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.494609Z digest=sha256:ae91e4e8e3c3620e6fc8d1757c7ede999c1ce407ce7685d3e040c39d8916081d

Observation 34baa129-8012-43e5-8218-fac9e93469fe · outbound

This paper cites Limits of deep learning: Sequence modeling through the lens of complexity theory, 2024.

GG-SSMs: Graph-Generating State Space Models Limits of deep learning: Sequence modeling through the lens of complexity theory, 2024

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:11:00.810935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:11:00.501033Z digest=sha256:49c987554acc1b6511e1bd044c47ffb53fa72a3881b45c6562ba9223ea7734ea

Pith citing papers

No inbound Pith citation observations are available.