Paper Citation Record · LEDGER

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos

As of 2 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2602.05638.

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

pith.paper-citation-record.v1
2602.05638 v3

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T07:16:29.588452Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-01T06:32:01.292127+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:30:57.042487Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact8
verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65c8424e-6828-4ea6-8cf6-2bab48d67022 · outbound

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

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos DINOv2: Learning Robust Visual Features without Supervision

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-16T07:17:30.329076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c7eaac3f-bcd2-4ab7-9889-f3bd8f9f0e80 · outbound

This paper cites Masked autoencoders are scalable vision learners.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Masked autoencoders are scalable vision learners

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.042736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 5a94e565-6873-4479-8a3f-d1166deb91bb · outbound

This paper cites Masked autoencoders as spatiotemporal learners.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Masked autoencoders as spatiotemporal learners

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.049984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation a8c9586c-5198-4534-9ffa-53d9cfbe7d17 · outbound

This paper cites Endovit: pretraining vision transformers on a large collection of endoscopic images.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Endovit: pretraining vision transformers on a large collection of endoscopic images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.045207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f0317426-8f8a-4b0f-8a0b-9561d3a8b46c · outbound

This paper cites Foundation model for endoscopy video analysis via large- scale self-supervised pre-train.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Foundation model for endoscopy video analysis via large- scale self-supervised pre-train

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.052145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation bf41add1-0ecf-4883-a5fa-aa24103cf21a · outbound

This paper cites General surgery vision transformer: A video pre-trained foundation model for general surgery.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos General surgery vision transformer: A video pre-trained foundation model for general surgery

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:17:30.324703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 87a0759a-26b3-4e95-bce9-2fc11c56f802 · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.054747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation d34de7fd-0b84-4b29-b33b-c8fcc05c2a96 · outbound

This paper cites Videomae v2: Scaling video masked autoencoders with dual masking.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Videomae v2: Scaling video masked autoencoders with dual masking

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.056970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 098ed1b4-6051-48cc-9cfb-d45625b74eef · outbound

This paper cites Dissecting self-supervised learning methods for surgical computer vision.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Dissecting self-supervised learning methods for surgical computer vision

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.047502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 71913d2d-5ac9-4783-a5dc-4b4654ef6414 · outbound

This paper cites Endonet: a deep architecture for recognition tasks on laparoscopic videos.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Endonet: a deep architecture for recognition tasks on laparoscopic videos

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.059469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 100195a1-baa4-47bc-a1c2-67a51dad23bf · outbound

This paper cites Pitvis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Pitvis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.016048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b2615ac1-326c-4387-9d6a-beaf33b18a48 · outbound

This paper cites Egosurgery-phase: a dataset of surgical phase recognition from egocentric open surgery videos.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Egosurgery-phase: a dataset of surgical phase recognition from egocentric open surgery videos

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.026298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b991f193-b445-45b3-8239-99d6d20e37d5 · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-16T07:17:30.319773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation b5c27669-8051-4f81-9fca-a33b4e8e9d6e · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T07:17:30.309716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1f629bb9-cf2f-445b-a19a-b81cd415a272 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised learn- ing.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Bootstrap your own latent: A new approach to self-supervised learn- ing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.021009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4e21a4e5-bfbb-4062-9c7e-20bf4afa6a3a · outbound

This paper cites Internvideo2: Scaling video foundation models for multimodal video understanding.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Internvideo2: Scaling video foundation models for multimodal video understanding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.018463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7214fd01-073e-495c-ba87-88203e7ec15b · outbound

This paper cites Internvideo-next: Towards general video foundation models without video-text supervision.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Internvideo-next: Towards general video foundation models without video-text supervision

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:17:30.296414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 8d73de36-f6d3-4ab2-bc36-7e6bf6778573 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Emerging properties in self-supervised vision transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.023621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1021f588-4279-4938-b861-5e2df567ecaf · outbound

This paper cites DINOv3.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos DINOv3

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T07:17:30.314610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4e6d5886-4dd6-4273-a38c-99dd91c43f7c · outbound

This paper cites Gastronet-5m: A multicenter dataset for developing foundation models in gastrointestinal endoscopy.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Gastronet-5m: A multicenter dataset for developing foundation models in gastrointestinal endoscopy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.028649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 1c07827e-2596-4bfa-be7d-42d423e629cc · outbound

This paper cites Self-supervised learning for endoscopic video analysis.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Self-supervised learning for endoscopic video analysis

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.011155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ebc9fd4d-172e-470d-8857-de06490ac087 · outbound

This paper cites Endomamba: an efficient founda- tion model for endoscopic videos via hierarchical pre-training.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Endomamba: an efficient founda- tion model for endoscopic videos via hierarchical pre-training

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.005691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 6fd0697f-7fca-40d5-a3fa-587ab0e66800 · outbound

This paper cites Scaling up self-supervised learning for improved surgical foundation models.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Scaling up self-supervised learning for improved surgical foundation models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:17:30.292026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation a0969ef8-c80e-4c83-8510-7030bec8b518 · outbound

This paper cites Learn- ing multi-modal representations by watching hundreds of surgical video lectures.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Learn- ing multi-modal representations by watching hundreds of surgical video lectures

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.002969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ae41b1ce-250c-4a0b-86ad-af55f87cc6f9 · outbound

This paper cites The TUM LapChole dataset for the M2CAI 2016 workflow challenge.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos The TUM LapChole dataset for the M2CAI 2016 workflow challenge

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-16T07:17:30.299900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 881fa467-b602-416e-b807-a77cf03bdad8 · outbound

This paper cites Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.000856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7ee17f12-9e50-4697-b874-622379a6e908 · outbound

This paper cites Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.998477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 91c4640e-79da-496f-a89b-21038eaea380 · outbound

This paper cites Surgical workflow recognition and blocking effectiveness detection in laparoscopic liver resection with pringle maneuver.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Surgical workflow recognition and blocking effectiveness detection in laparoscopic liver resection with pringle maneuver

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.031094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation bd78fb99-e91a-4db8-b9b3-a2cb15f72130 · outbound

This paper cites Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Ophnet: A large-scale video benchmark for ophthalmic surgical workflow understanding

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.013524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f4bda7a8-62b1-4c61-89a2-f4f9cae6c375 · outbound

This paper cites Analyzing surgical technique in diverse open surgical videos with multitask machine learning.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Analyzing surgical technique in diverse open surgical videos with multitask machine learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.985662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation bcaee95c-e5e9-48f3-97a5-39cdcec28c13 · outbound

This paper cites A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.038165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 416044c4-e24f-4216-8ad4-2198076a5ebd · outbound

This paper cites Aixsuture: vision-based assessment of open suturing skills.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Aixsuture: vision-based assessment of open suturing skills

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.033391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7f67398d-310d-4c54-844f-8d8438a43f0a · outbound

This paper cites Video retrieval in laparoscopic video recordings with dynamic content descriptors.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Video retrieval in laparoscopic video recordings with dynamic content descriptors

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.983458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0e5c71cd-c19b-4398-98b6-8ffae8dba498 · outbound

This paper cites Contrastive transformer- based multiple instance learning for weakly supervised polyp frame detection.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Contrastive transformer- based multiple instance learning for weakly supervised polyp frame detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.035865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation f98b61b0-9374-4ff1-a737-679468ca8c54 · outbound

This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.989951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2cc3a194-d676-4fae-88de-d27188eeae67 · outbound

This paper cites Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.040582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7df0091d-7d66-432f-a3d1-bdbd0e6f5b4f · outbound

This paper cites Colonoscopy 3d video dataset with paired depth from 2d-3d registration.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Colonoscopy 3d video dataset with paired depth from 2d-3d registration

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:31.008591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 7a94bfda-bcd3-46bc-a550-8a6d427f0949 · outbound

This paper cites Cataracts: Challenge on automatic tool annotation for cataract surgery.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Cataracts: Challenge on automatic tool annotation for cataract surgery

Reference 38

Resolution: verified exact; doi, observed 2026-05-16T07:17:30.179924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation eef2e08a-40b3-4bf1-b948-f20326cd3f64 · outbound

This paper cites Challenges in multi-centric generalization: phase and step recog- nition in roux-en-y gastric bypass surgery.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Challenges in multi-centric generalization: phase and step recog- nition in roux-en-y gastric bypass surgery

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.976472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 2d9333d2-f622-4027-8bdd-c18428c6e1b9 · outbound

This paper cites Copesd: A multi- level surgical motion dataset for training large vision-language models to co-pilot endoscopic submucosal dissection.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Copesd: A multi- level surgical motion dataset for training large vision-language models to co-pilot endoscopic submucosal dissection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.978987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation fe24adf8-d2a4-435a-b1e2-1636d1db2711 · outbound

This paper cites Towards holistic surgical scene understanding.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Towards holistic surgical scene understanding

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.974178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 00a9411f-b337-447d-82ab-0ab0a47cb83b · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Kvasir-seg: A segmented polyp dataset

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.971963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 10bfce8b-2270-4931-b1ae-c7ab493c2d0e · outbound

This paper cites A benchmark for endoluminal scene segmentation of colonoscopy images.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos A benchmark for endoluminal scene segmentation of colonoscopy images

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.981215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 0d5a1ad6-0bd0-4022-9370-4de51db83397 · outbound

This paper cites Towards automatic polyp detection with a polyp appearance model.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Towards automatic polyp detection with a polyp appearance model

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.987823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 59a4ce96-7c01-40eb-8b5b-5b2c83f43849 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.992302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 91b31e61-a7de-4ceb-8c61-a2b21be828ba · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Pranet: Parallel reverse attention network for polyp segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.969766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 4d2cb680-6211-4d5a-b3bc-b180c933ea31 · outbound

This paper cites Uacanet: Uncertainty augmented context attention for polyp segmentation.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Uacanet: Uncertainty augmented context attention for polyp segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T07:17:30.967308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 243a59d5-e604-4e35-950e-9cf958af9d13 · outbound

This paper cites Pranet-v2: Dual-supervised reverse attention for medical image segmentation.

SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos Pranet-v2: Dual-supervised reverse attention for medical image segmentation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:17:30.304570Z

Source-reported events for the cited work

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Pith citing papers

Observation 0ba885f5-863f-45d2-9ca0-d3b792d444ab · inbound

LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition cites this paper.

LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T11:30:57.042487Z

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