Paper Citation Record · LEDGER

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2605.25495.

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

pith.paper-citation-record.v1
2605.25495 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T22:06:55.455943Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

26 of 26 outbound references displayed

verified exact3
verified fuzzy0
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cd6c758-7344-4bdd-b987-dc3ea4dcd7e7 · outbound

This paper cites SAM-Adapter: Adapting seg- ment anything in underperformed scenes.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation SAM-Adapter: Adapting seg- ment anything in underperformed scenes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7ef633-f6eb-4ae4-9d2b-8491eaf63874 · outbound

This paper cites RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.IEEE Trans- actions on Geoscience and Remote Sensing, 62:1–17,.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.IEEE Trans- actions on Geoscience and Remote Sensing, 62:1–17,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 8f4d2a01-dfc6-4435-b2b9-bb76b366ca31 · outbound

This paper cites Segmenting unknown 3D objects from real depth images using Mask R-CNN trained on synthetic data.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Segmenting unknown 3D objects from real depth images using Mask R-CNN trained on synthetic data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation a380d5f8-c8be-4e31-84fc-a9254b02bb38 · outbound

This paper cites GraspNet-1Billion: A large-scale benchmark for general object grasping.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation GraspNet-1Billion: A large-scale benchmark for general object grasping

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 085188b5-c24a-400b-b162-fe677ee2b867 · outbound

This paper cites Foun- dation models in robotics: Applications, challenges, and the fu- ture.International Journal of Robotics Research, 44(5):701–739,.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Foun- dation models in robotics: Applications, challenges, and the fu- ture.International Journal of Robotics Research, 44(5):701–739,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 88e8e0c9-559a-45f3-9485-111277d5d7a9 · outbound

This paper cites La-LoRA: Parameter-efficient fine-tuning with layer-wise adaptive low-rank adaptation.Neural Networks, 194:108095,.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation La-LoRA: Parameter-efficient fine-tuning with layer-wise adaptive low-rank adaptation.Neural Networks, 194:108095,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

Source-reported events for the cited work

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Observation 9662db7b-fba3-4832-89fd-c76979889ef8 · outbound

This paper cites Model based training, detection and pose estima- tion of texture-less 3D objects in heavily cluttered scenes.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Model based training, detection and pose estima- tion of texture-less 3D objects in heavily cluttered scenes

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e8e8cf6-4730-49f8-8bd7-25a37baef657 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eacc9dc5-4770-4e70-a1d4-822ab3b1e233 · outbound

This paper cites Segment anything in high quality.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Segment anything in high quality

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 539e3b48-2bb3-4508-ae79-a896da785d95 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dol- lár, and Ross Girshick.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Berg, Wan-Yen Lo, Piotr Dol- lár, and Ross Girshick

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 498fab7e-ae51-4506-b33c-6a4310a1cc59 · outbound

This paper cites Kopiczko, Tijmen Blankevoort, and Yuki M.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Kopiczko, Tijmen Blankevoort, and Yuki M

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 8e7ac8f8-7c1e-437b-98ec-cac28dc6ddf2 · outbound

This paper cites Similarity of neural network representations revisited.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Similarity of neural network representations revisited

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 4c25298c-d16a-496f-8d67-66a7cf619f8b · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation DoRA: Weight-decomposed low-rank adaptation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation de4f5980-df85-4971-912c-4f6f993ad055 · outbound

This paper cites Im- proving SAM for camouflaged object detection via dual stream adapters.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Im- proving SAM for camouflaged object detection via dual stream adapters

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 9134353f-c651-4add-8d2a-db2f9e08b071 · outbound

This paper cites Segment anything in medical images.Nature Communications, 15(1):654,.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Segment anything in medical images.Nature Communications, 15(1):654,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 367b2c62-b2e9-4318-85d0-4aecc04a1705 · outbound

This paper cites Dex-Net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Dex-Net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation ce1bd263-b0f2-4b88-b86a-7e94ee937a57 · outbound

This paper cites 6-DOF GraspNet: Variational grasp generation for object manipulation.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation 6-DOF GraspNet: Variational grasp generation for object manipulation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Unavailable: canonical work link unavailable.

Observation 1b499786-f0fb-4124-9b8d-7dd9ae476739 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation SAM 2: Segment Anything in Images and Videos

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:14:00.180742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ece7b255-58ed-4de2-aa71-d4234ceec49c · outbound

This paper cites Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, and Shuran Song.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, and Shuran Song

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 6b1ea87c-26a5-41f6-b2aa-cf50fc7beae0 · outbound

This paper cites EasyLabel: A semi-automatic pixel-wise object annotation tool for creating robotic RGB-D datasets.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation EasyLabel: A semi-automatic pixel-wise object annotation tool for creating robotic RGB-D datasets

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation a13fbbe3-554b-4ffd-8492-3fde99d15891 · outbound

This paper cites PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 8c993287-fd48-4fee-b200-950b4c85baa1 · outbound

This paper cites EfficientSAM: Leveraged masked image pretraining for efficient segment anything.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation EfficientSAM: Leveraged masked image pretraining for efficient segment anything

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 8f8be492-e698-44f1-964d-bf980161cc79 · outbound

This paper cites an unresolved cited work.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Observation 2dc36d7e-8813-447f-9284-4e8350e3daa9 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:14:00.182120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef10b953-f2e7-49bd-8b41-9719339ad774 · outbound

This paper cites Fast Segment Anything.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Fast Segment Anything

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:14:00.174342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81db9179-3a88-487d-a3eb-cc6e1eedc925 · outbound

This paper cites Ga- Lore: Memory-efficient LLM training by gradient low-rank pro- jection.

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation Ga- Lore: Memory-efficient LLM training by gradient low-rank pro- jection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T22:06:55.455943Z

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Pith citing papers

No inbound Pith citation observations are available.