{"as_of":"2026-08-09T09:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:760569cbf86d064971a58719589cd6de867f68975270b2c521045ed413df8d7b","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T22:06:55.455943Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.25495/citation-record","integrity":"/paper/2605.25495/integrity","json":"/paper/2605.25495/citation-record.json","paper":"/paper/2605.25495"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"SAM-Adapter: Adapting seg- ment anything in underperformed scenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:3fb46fa8b38d37f004a813dde3fa9dffb2d85d3b8e8435098da07b7d2dd2f7d3","observation_id":"8cd6c758-7344-4bdd-b987-dc3ea4dcd7e7","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"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,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:25ea2c717bc0b60c319e6f4e66351f99afddc53764ee739af1007df17ae6a003","observation_id":"ed7ef633-f6eb-4ae4-9d2b-8491eaf63874","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Segmenting unknown 3D objects from real depth images using Mask R-CNN trained on synthetic data","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:0becace69644921d222f248e1c520193437ffdc2b91a8c0c4818852e4d0ed7ee","observation_id":"8f4d2a01-dfc6-4435-b2b9-bb76b366ca31","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"GraspNet-1Billion: A large-scale benchmark for general object grasping","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:ab6200541d75b0ded82799596da3df7371bcfb91e778ab1ee961009b8267d1f6","observation_id":"a380d5f8-c8be-4e31-84fc-a9254b02bb38","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Foun- dation models in robotics: Applications, challenges, and the fu- ture.International Journal of Robotics Research, 44(5):701–739,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:6906bd6e8eb751de8e349603e4331e652a9c4bc31148f37cf1559cbb74c535d8","observation_id":"085188b5-c24a-400b-b162-fe677ee2b867","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"La-LoRA: Parameter-efficient fine-tuning with layer-wise adaptive low-rank adaptation.Neural Networks, 194:108095,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:9b308a0801b83bae528fee352acfd59a192b082ee481bd8b98a44e2b570445df","observation_id":"88e8e0c9-559a-45f3-9485-111277d5d7a9","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Model based training, detection and pose estima- tion of texture-less 3D objects in heavily cluttered scenes","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:2df636f43871771ebd0c25067005f0f39e58dd854a34336026a3eabf42650b21","observation_id":"9662db7b-fba3-4832-89fd-c76979889ef8","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:22c62bc467e6dcdc4bc2f312adf043b76e66f2d777fa79ccd8fcc305eefeb06f","observation_id":"3e8e8cf6-4730-49f8-8bd7-25a37baef657","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Segment anything in high quality","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:4f28d31ce1fec598dfe94f505b2f41f368b8f25172b8ee5ec8c5382f5295d331","observation_id":"eacc9dc5-4770-4e70-a1d4-822ab3b1e233","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Berg, Wan-Yen Lo, Piotr Dol- lár, and Ross Girshick","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:1901ab656ad05e312429e47e3cf0af14cf794fe88dc166b8c0a63685472fe0e5","observation_id":"539e3b48-2bb3-4508-ae79-a896da785d95","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Kopiczko, Tijmen Blankevoort, and Yuki M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:214dbb2f44e7b810ea8ad5bb1a1d760fc57f2e4230a24fd8c9b43c15d8f78ae3","observation_id":"498fab7e-ae51-4506-b33c-6a4310a1cc59","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Similarity of neural network representations revisited","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:5e5f3391ca8759657de8538259783a61144ec69b28cfb3152ae232e0234eaea0","observation_id":"8e7ac8f8-7c1e-437b-98ec-cac28dc6ddf2","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"DoRA: Weight-decomposed low-rank adaptation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:edef6fba31ecc2b16a16cb401255a6202fb2937d07acedba5a9a753c4adbb4bc","observation_id":"4c25298c-d16a-496f-8d67-66a7cf619f8b","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Im- proving SAM for camouflaged object detection via dual stream adapters","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:c740ce9c9c24043bc4fbd3fd4ff9930589f9614b83f25065d564094428e0c71b","observation_id":"de4f5980-df85-4971-912c-4f6f993ad055","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Segment anything in medical images.Nature Communications, 15(1):654,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:e81a04a42edaed34dd4d8397b92817ee7db02539847e240855253369b735ced0","observation_id":"9134353f-c651-4add-8d2a-db2f9e08b071","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Dex-Net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:cbdf1320392df3c203bd21c9123a852c52f336ee5c31aa953e46709dd2e2dfcd","observation_id":"367b2c62-b2e9-4318-85d0-4aecc04a1705","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"6-DOF GraspNet: Variational grasp generation for object manipulation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:b40e330fecc47fcf5822e0b1072242064ce8b8d1287b0085fa9fd35f04043c35","observation_id":"ce1bd263-b0f2-4b88-b86a-7e94ee937a57","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":"2408.00714","doi":"10.1038/s41598-025-97590-3","metadata_source":"pith","pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAM 2: Segment Anything in Images and Videos","venue":"cs.CV","work_id":"acc13f66-d814-44f9-9688-375688bf2d4a","year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:6b307ca74c5dfd2a53a0abecfe503f5e089b25bc6bd5aa9246187cfb0d64b56b","observation_id":"1b499786-f0fb-4124-9b8d-7dd9ae476739","resolution":{"observed_at":"2026-06-29T22:14:00.180742Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-24T04:24:23.885301+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-24T04:24:23.885301+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Sajjan, Matthew Moore, Mike Pan, Ganesh Nagaraja, Johnny Lee, Andy Zeng, and Shuran Song","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:2b8dfdbea82d6b0967f454ce5cf63eb565e24844700883d94ab1343cf7febc33","observation_id":"ece7b255-58ed-4de2-aa71-d4234ceec49c","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"EasyLabel: A semi-automatic pixel-wise object annotation tool for creating robotic RGB-D datasets","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:697f9ca4c83e47ef657e9cbd0b8695f1334efe4a9d19dd93a80b5fee2eb8638f","observation_id":"6b1ea87c-26a5-41f6-b2aa-cf50fc7beae0","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:aaae7d11e17592f41a018bc2eda80f15939597351f417ceb1964ce54d12b5385","observation_id":"a13fbbe3-554b-4ffd-8492-3fde99d15891","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"EfficientSAM: Leveraged masked image pretraining for efficient segment anything","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:946137c4e59cd65b553a4cee18af157a02c7512cd0d2f5bac03a36607463edc9","observation_id":"8c993287-fd48-4fee-b200-950b4c85baa1","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:eb98e2e54787a1307e888c6c321d1d45eac970926815b08049d96b98bf88acf6","observation_id":"8f8be492-e698-44f1-964d-bf980161cc79","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14289","last_updated":"2023-07-01T07:26:22Z","snapshot_observed_at":"2026-08-08T16:17:33.420400Z","submitted_at":"2023-06-25T16:37:25Z","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","version":2},"cited_work":{"arxiv_id":"2306.14289","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.14289","snapshot_observed_at":"2026-07-04T21:00:09.639501Z","title":"Faster Segment Anything: Towards Lightweight SAM for Mobile Applications","venue":"cs.CV","work_id":"d159afc6-0f47-4693-a3c0-908e661ff652","year":2023},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"cited_paper":"/paper/2306.14289","citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:2fc3212586647cd32f88efadb9ebc61bd14f4e202ba6c5df8b9fca6c450db16e","observation_id":"2dc36d7e-8813-447f-9284-4e8350e3daa9","resolution":{"observed_at":"2026-06-29T22:14:00.182120Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12156","last_updated":"2023-06-21T10:08:29Z","snapshot_observed_at":"2026-07-06T15:45:01.961896Z","submitted_at":"2023-06-21T10:08:29Z","title":"Fast Segment Anything","version":1},"cited_work":{"arxiv_id":"2306.12156","doi":"10.48550/arxiv.2306.12156","metadata_source":"arxiv_reference","pith_arxiv_id":"2306.12156","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2306.12156 (2023) 31","venue":"arXiv (Cornell University)","work_id":"feed3d9f-cc9f-42db-90e6-e9ff051cef57","year":2023},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"cited_paper":"/paper/2306.12156","citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:0f5377f85a62942a57064f06ff311bbb2b7d4c62953230c0040aa5dcae7244e0","observation_id":"ef10b953-f2e7-49bd-8b41-9719339ad774","resolution":{"observed_at":"2026-06-29T22:14:00.174342Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:06:55.455943Z","title":"Ga- Lore: Memory-efficient LLM training by gradient low-rank pro- jection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:55.455943Z"},"links":{"citing_paper":"/paper/2605.25495"},"observation_digest":"sha256:9e094739f8d20830b050adc4e6d16d383342b37459f09ccb5d717522f3c58f04","observation_id":"81db9179-3a88-487d-a3eb-cc6e1eedc925","resolution":{"observed_at":"2026-06-29T22:06:55.455943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.25495","last_updated":"2026-05-25T06:56:42Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-05T14:20:57.279165Z","submitted_at":"2026-05-25T06:56:42Z","title":"RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"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."}