{"as_of":"2026-08-10T18:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8b39e3861289cb221c3c4cd73cf510b106e4a7217034c83a65963c2fb876eb20","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-15T13:28:23.934522Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2603.10963/citation-record","integrity":"/paper/2603.10963/integrity","json":"/paper/2603.10963/citation-record.json","paper":"/paper/2603.10963"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"La nguage models are few-shot learners","venue":null,"work_id":"f755d588-cf0e-40d0-b7b9-6b94426d84da","year":1901},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:526811e858c6a0b5167982cbba9c78cfed688e68c5ad5f66cdb89204669f6909","observation_id":"3b59bbfe-da12-4e1b-893e-3fc4c31a1a37","resolution":{"observed_at":"2026-05-15T13:30:01.914004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Objave rse: A universe of annotated 3d objects","venue":null,"work_id":"bc5c7e24-6f10-457f-abc8-75c1a53128bf","year":2023},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:56bc7f4330b6d9852cdd988ba90a79bbdd4ad31b7d198b849e065d7e663d7f4f","observation_id":"dd03de7d-f215-41f8-aa42-dd595a3cf2fb","resolution":{"observed_at":"2026-05-15T13:30:01.887129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":"1810.04805","doi":"10.1111/jofi.12885","metadata_source":"pith","pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","venue":"cs.CL","work_id":"ed240a10-5b19-406c-baa5-30803f465785","year":2018},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:5d92b30d92c2ccef7546f164b7d6675a21eea454d9db33f903234393d3fead90","observation_id":"f3f86e20-6c34-4dc6-99a9-307d89f8b4d3","resolution":{"observed_at":"2026-05-15T13:30:01.613409Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"468609d5-185a-46fd-b751-afd93bce6d5b","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:9c076c0da5283d3b5876ba66189ad82db81a956a6b4844853be1241e5899f2df","observation_id":"80eb9871-7ac8-4677-9efb-e2c6bca1a5ac","resolution":{"observed_at":"2026-05-15T13:30:01.871854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f92cbf74-5a9f-4ab4-be64-b146160273bc","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:2a96328f6a9bdb63509a7d14a3fb6a96c58e394eb9738746175f633093fb411c","observation_id":"24cef866-47e3-4faa-8024-ae979ab1c613","resolution":{"observed_at":"2026-05-15T13:30:01.860700Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Point transf ormer","venue":null,"work_id":"c99d2222-c24d-4ccd-bb23-a50823c10002","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:3079bdec03349c70a5623705ab386344b8c1107e2a9cf1bd98007b6b7dfa4552","observation_id":"a358559e-7c45-4ffa-a378-287fdcf1b924","resolution":{"observed_at":"2026-05-15T13:30:01.858184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Moment: A family of open time-series foundation models","venue":null,"work_id":"86d35287-ea37-46a6-a1a0-bb39344c242f","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:7db72215f9af49387b07397874345a0b021d5529ad60e2e67d37beca7501bac6","observation_id":"a727fad0-1f03-4497-8eb4-d423ea1de09d","resolution":{"observed_at":"2026-05-15T13:30:01.893456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"3d semantic segmentation with submanifold sparse convolutional networks","venue":null,"work_id":"090915bb-058b-4e29-852e-12184c79a36a","year":2018},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c267a7301061c84997db592961a4d7dca93cbd569b6736711573db09a1801b5f","observation_id":"b9556d88-869b-45db-a0b7-bd24575bbd30","resolution":{"observed_at":"2026-05-15T13:30:01.900884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pct: Point cloud transformer","venue":null,"work_id":"43a3ca41-083a-4b08-9b4c-3de7c80aeda4","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c1adea76329384838a78f4711375459a68265031438341668da665df65b5885a","observation_id":"ae9cc7ff-6e2d-4b98-aa5c-eeee2d98d538","resolution":{"observed_at":"2026-05-15T13:30:01.903115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.00615","last_updated":"2023-09-01T17:59:47Z","snapshot_observed_at":"2026-08-08T23:14:57.166208Z","submitted_at":"2023-09-01T17:59:47Z","title":"Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following","version":1},"cited_work":{"arxiv_id":"2309.00615","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.00615","snapshot_observed_at":"2026-07-04T03:09:29.321784Z","title":"Point-bind & point-llm: Aligning point cloud with multi-modality for 3d understanding, generation, and instruction following","venue":null,"work_id":"99ba2617-dd10-4f2d-bb63-cc88e743aa78","year":2023},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"cited_paper":"/paper/2309.00615","citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:373d118b6cc89a54b7e2f1ba373c04cec6708cdade1d7d0ecaf4fa1ade697799","observation_id":"26024ed0-c9be-4d31-be8a-7c3e922ab0e1","resolution":{"observed_at":"2026-05-15T13:30:01.609235Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Delving de ep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":"38c130b9-6a6c-45e6-9f10-bcf1156ebe3d","year":2015},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:0cf329f736fc7b7b3677c8193026865cd1590518a69c81b028bb69b93afeccc9","observation_id":"20dc8fba-09f5-4aba-a3d5-c427417c8c9d","resolution":{"observed_at":"2026-05-15T13:30:01.869517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep res idual learning for image recognition","venue":null,"work_id":"6d383bb8-2fb4-4cfb-97d8-bd1d4cfb8f70","year":2016},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:91fcb9b07bce345dd28748aac8f3e6f5be6f57281c4dce1eac1357463bdc217e","observation_id":"37187f19-6b7b-4fbc-94a6-6c7885bd8552","resolution":{"observed_at":"2026-05-15T13:30:01.916000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03327","last_updated":"2024-01-09T06:20:23Z","snapshot_observed_at":"2026-08-09T22:22:32.126759Z","submitted_at":"2024-01-09T06:20:23Z","title":"Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models","version":1},"cited_work":{"arxiv_id":"2402.03327","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.03327","snapshot_observed_at":"2026-07-08T01:44:26.161403Z","title":"Uni3d-llm: Unifying point cloud perception, generation and editing with large language models","venue":"cs.CV","work_id":"d65ba5aa-953d-4317-b3db-e346d500159b","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"cited_paper":"/paper/2402.03327","citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c71137fabeb92822221cf7c38da50afb5f0569ae46f6124c7fb43efac916437e","observation_id":"d487824a-a027-44a7-b132-8b8654624798","resolution":{"observed_at":"2026-05-15T13:30:01.604001Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Openshape: Scaling up 3d shape representation towards open-world understanding","venue":null,"work_id":"409a45d5-78e8-479e-9f38-62cfd42de0fb","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:1ce54325c8a76679a5ede1076ecb32dc342a26486863be16fa3da15f50a70383","observation_id":"bc6c082b-835c-4817-97aa-20d86913979a","resolution":{"observed_at":"2026-05-15T13:30:01.884860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Swin transformer: Hierarchical vision transformer using shifted windo ws","venue":null,"work_id":"b0a241fd-ddae-47e2-822f-a7a06873abd6","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:f17d3ae0139baa76a8f6dc271e191bfa676ad9e7bf83a7ea8459c3e35b377d6b","observation_id":"647491fb-77c3-4640-a11a-ce1dc17cdb25","resolution":{"observed_at":"2026-05-15T13:30:01.908535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rethinking network design and local ge- ometry in point cloud: A simple residual MLP framework","venue":null,"work_id":"d57d4c4b-4628-46f3-8392-1a6362445aa0","year":2022},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:dddab6770708f6461fa5b7ae32aa344e2ec71b5d8dad5a4ee4a2e82fced278a1","observation_id":"9a441bc7-4ec0-45d9-af42-7af71e745344","resolution":{"observed_at":"2026-05-15T13:30:01.925427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Opendlign: Ope n-world point cloud understanding with depth-aligned images","venue":null,"work_id":"8d056931-b34d-414c-aff5-449531d69cfe","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:ec2c0326c7e7287c04651a98aba62113de141e9165593f859c765aa6660b671e","observation_id":"26f2a6d8-bba8-4bb5-9b89-cfc817d27be5","resolution":{"observed_at":"2026-05-15T13:30:01.895610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Vo, and Marc Szafraniec et al","venue":null,"work_id":"4dc91c0f-e8fd-4c71-9764-40c77d91c512","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c62134485806e91d82880a975a8cf2e2a026a1d28db32aeab6b09a8c0a4f20ea","observation_id":"c60d0907-3b6c-4f8d-aefb-6bdf0c888479","resolution":{"observed_at":"2026-05-15T13:30:01.905647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Masked au- toencoders for point cloud self-supervised learning","venue":null,"work_id":"58eba510-bdc4-4d23-9a4e-9ddd344f9260","year":2022},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:0c838bd9b6823705d3ac197aac1628e539baedcc1c4c52bb5d0009e127b1a828","observation_id":"7aaa9453-d694-4324-bbb2-db49dfa93233","resolution":{"observed_at":"2026-05-15T13:30:01.893012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pointnet : Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":"d9e32068-69d1-46f1-9b56-c990db975b6e","year":2017},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:a1dac6933e4d75511e7a0c0e99d9db82bc925158de97ca573d98b47db81a1fad","observation_id":"d8de796b-1f05-449d-bf8a-a86d17094a72","resolution":{"observed_at":"2026-05-15T13:30:01.918145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Poin tnet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":"0d95a666-a828-4313-bb14-c26e0309a8da","year":2017},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:f7b5017683fb026ff6cdbd04d93ba2e1f003c3b05730a710c143339460b478ce","observation_id":"8bd87137-1482-4de2-9fcc-c24c81473e10","resolution":{"observed_at":"2026-05-15T13:30:01.887939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Contrast with reconstruct: Contrastive 3d representation learning guided by g enerative pretraining","venue":null,"work_id":"ccb9b330-9565-4007-8f95-bffa65b62099","year":2023},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:4ba1f568abd9e1aef36ab13e07ddf4375a0360c0a74bfaa01cba0789f8c1913e","observation_id":"41f8e393-554a-4485-9f5a-9d332d03f9d4","resolution":{"observed_at":"2026-05-15T13:30:01.890533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":"b51ed95c-1dee-4c4f-a3bc-adff2340ef9f","year":2019},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:6067e0fc744212eca87b86855d8b55ce8e11e0b11e41921403d7a78953e01b9b","observation_id":"f31c02cd-ee45-447f-8c78-4dbbbda85458","resolution":{"observed_at":"2026-05-15T13:30:01.874173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Revisiting point cloud classification: A new benchmark dataset and classificatio n model on real-world data","venue":null,"work_id":"980d069f-fad4-4f56-a13d-0f3df5ff4dbb","year":2019},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:4e61e1820f187e71fce58122b31d7f6c607fdf2ec011f5fd700d9424d5d47808","observation_id":"1211dc57-bd4f-40a6-8764-956f6445dcff","resolution":{"observed_at":"2026-05-15T13:30:01.880857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Attention is all you need","venue":null,"work_id":"50eec442-6ea4-43e3-8daa-dfc056364e30","year":2017},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:885b658ff62fb3900264ff6330b839efabf6054ca5ff86afa8dbadefd512e8f0","observation_id":"8e28401d-1a70-4ad4-bd9b-953513c9f9bf","resolution":{"observed_at":"2026-05-15T13:30:01.882873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic graph cnn for learning on point clouds","venue":null,"work_id":"62979045-1b42-4d1c-a2ba-4c301988bc73","year":2019},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c132e95e3ce201bd18435b14e4427d27c9e8df53da9aac6c0fae099573b96822","observation_id":"d4ccc1f5-bb8d-48f0-9ba2-6e144b4f4405","resolution":{"observed_at":"2026-05-15T13:30:01.920563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":"e6eefd1c-568e-479f-b74d-6c5cd544a5ee","year":1912},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:8184a580f65ef409d348f9b8a26f87fe30397e1f59d008152ebeca2fca105421","observation_id":"6e37387f-e884-4d60-99c9-a6524b9a02e1","resolution":{"observed_at":"2026-05-15T13:30:01.877169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pointllm: Empowering large language models to understand point clouds","venue":null,"work_id":"0266caf0-b9c3-425f-98d6-a27d05e92b6f","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:40c8aca4e772cbb9c4470cc7e3894eb9c6a3a44ca3d31f5b618ecc4c598f41e7","observation_id":"63445cee-9723-48ef-b19b-34f439587170","resolution":{"observed_at":"2026-05-15T13:30:01.911782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ulip-2: Towards scala ble multimodal pre-training for 3d understanding","venue":null,"work_id":"8beb27a7-e8ea-4bfd-82a3-6641de7ad07e","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:13dc1d5eec5de1132a97e256d297c9b04809ba6574d882499ad45ce04959e097","observation_id":"c20417e4-e2e6-45ca-9c13-1e7830131882","resolution":{"observed_at":"2026-05-15T13:30:01.898283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Point-bert: Pre-training 3d point cloud transformers with masked point modeling","venue":null,"work_id":"737b32ab-a7c1-4cbd-9f90-fdd1dd584c50","year":2022},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:aa6606d8164768e56c04967bfe70d28faad9d7d52f261c953c6c8d8e4828cd74","observation_id":"9027f8b6-ba93-4ef5-bf21-958edcf86fb7","resolution":{"observed_at":"2026-05-15T13:30:01.889059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tamm: Triada pter multi-modal learning for 3d shape understanding","venue":null,"work_id":"17baec53-caee-4773-abcb-0347c7c12483","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:c08fb96558c1d21c62e6cb019eb872dd1c71e2e2d8006c9c25a10127391cca42","observation_id":"a1cf1af0-c162-47d9-96ad-81bf865a5919","resolution":{"observed_at":"2026-05-15T13:30:01.891433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Point transformer","venue":null,"work_id":"30987e60-0428-4538-a935-9b4dfc91d3b6","year":2021},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:1b5902ce3e790a699d419d44bfdd4f956ae1b54c799bcad0fba439c95a644b7d","observation_id":"34063148-4dd8-41fc-9d52-aed3eddb8de5","resolution":{"observed_at":"2026-05-15T13:30:01.867289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Uni3d: Exploring unified 3d representation at scale","venue":null,"work_id":"b22118af-c2a6-4e39-9f05-3e49fbf970d2","year":2024},"citing_paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-15T13:28:23.934522Z"},"links":{"citing_paper":"/paper/2603.10963"},"observation_digest":"sha256:69ef7dcb536a8aa7c75689318f7ba647a4ca54d423dddab943d875a7b8fa126e","observation_id":"bbdc1f5b-cc79-4248-adb0-4998d42aef1c","resolution":{"observed_at":"2026-05-15T13:30:01.922698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2603.10963","last_updated":"2026-03-11T16:50:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T22:48:44.253503Z","submitted_at":"2026-03-11T16:50:46Z","title":"Pointy - A Lightweight Transformer for Point Cloud Foundation Models"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":0,"verified_exact":3,"verified_fuzzy":29},"total_outbound_references":33},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2603.10963."}