{"as_of":"2026-08-21T16:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:568b9a360b8aa081ee63b55f9d482c6a2bc453e62c673217184d427d9000b1fb","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":"2026-08-06T19:37:40.351084Z","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":"2026-08-21T06:32:19.484+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/2507.05099/citation-record","integrity":"/paper/2507.05099/integrity","json":"/paper/2507.05099/citation-record.json","paper":"/paper/2507.05099"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.241722Z","title":"Deep Learning for 3D Point Clouds: A Survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.241722Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:b5b7658da51d9ec361b2c19c52de8cff09f5d7797ec38f5b17e529def938fca1","observation_id":"5d56cdde-94e6-48c9-aac5-ffc62f0d2095","resolution":{"observed_at":"2026-08-06T19:37:40.241722Z","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":"2026-08-06T19:37:40.245580Z","title":"Review: Deep Learning on 3D Point Clouds","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.245580Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:30a93e0a03dd9294792ea8214004cad7f14e8f958c27a1c98b1bb22579cb287f","observation_id":"d11abc90-f902-4d9c-b204-de392ccd5803","resolution":{"observed_at":"2026-08-06T19:37:40.245580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00593","last_updated":"2017-04-10T22:25:25Z","snapshot_observed_at":"2026-08-14T21:27:28.037090Z","submitted_at":"2016-12-02T08:40:40Z","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.00593","snapshot_observed_at":"2026-08-06T19:37:40.249133Z","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.249133Z"},"links":{"cited_paper":"/paper/1612.00593","citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:b416336c679775e63a95f34b4eb5faa53f56d4d2aa074c0364874904e9590542","observation_id":"c3971cd3-d746-42b8-8f9c-914aca733123","resolution":{"observed_at":"2026-08-06T19:37:40.249133Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.931631Z","title":"Dynamic graph cnn for learning on point clouds","venue":null,"work_id":"02531ed5-7598-4255-8edf-19f2889404be","year":2019},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.253300Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:788926a27e9aa6297ba8ddf04aceadd0f123622f3804ac882d9d6d89d4334ff4","observation_id":"f3a74806-5dc8-48d8-bfb2-c8a8436d732a","resolution":{"observed_at":"2026-08-06T19:37:40.935577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.921384Z","title":"AGConv: Adaptive Graph Con- volution on 3D Point Clouds","venue":null,"work_id":"d83afafe-68d6-47a7-93c0-03ad3e46d5e3","year":2023},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.256759Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:314e09d69b81175c91409e1a03aac4383935a45e3391fb7225dd80aea747b370","observation_id":"615be9a2-9a5f-4ebc-af92-06b8d8fc857b","resolution":{"observed_at":"2026-08-06T19:37:40.925202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04179","last_updated":"2024-03-03T10:05:47Z","snapshot_observed_at":"2026-08-21T05:25:51.193983Z","submitted_at":"2023-06-07T06:23:12Z","title":"Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks","version":2},"cited_work":{"arxiv_id":"2306.04179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.04179","snapshot_observed_at":"2026-08-06T19:37:40.680047Z","title":"Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks","venue":"hep-ex","work_id":"d493f757-9a39-4409-a79b-bc490c34e045","year":2023},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.260379Z"},"links":{"cited_paper":"/paper/2306.04179","citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:423c611a66b6e932f95dbac6c56071d259850d7c71f19e6ff9969b47a82b8a8b","observation_id":"d321e270-6e35-40fd-b18d-0980669a9f21","resolution":{"observed_at":"2026-08-06T19:37:40.683863Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.912217Z","title":"Jet Tagging via Par- ticle Clouds","venue":null,"work_id":"ad43f684-e7f7-49c6-b219-aabc2b9006d4","year":2020},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.264238Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:a5705e94ec03fd6775610ae3d026190dde288b659837a73216930abf92abbc38","observation_id":"1592c84d-332b-44ab-a238-19baa8cbbc58","resolution":{"observed_at":"2026-08-06T19:37:40.915790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.59892","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.663750Z","title":"Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruc- tion in High Energy Physics","venue":null,"work_id":"f06f41e7-55ee-447d-90f8-00a60326abaf","year":2020},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.267252Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:9f6b147aaeefd8a75dd7d0c90a8218f41b37a95911b31dd0f8e58eebbc309858","observation_id":"1cac5efd-2e45-442a-8a71-626f29d39eaa","resolution":{"observed_at":"2026-08-06T19:37:40.670301Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.902597Z","title":"Abe et al","venue":null,"work_id":"09787bb2-fc21-4957-b2a8-ed9c5bf47cc7","year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.270635Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:190454ca6c14706d5174ca4996e35ff4939e18351c9f8aea02785397a526d6b9","observation_id":"4030540a-ecba-447b-967f-45eb860eda7a","resolution":{"observed_at":"2026-08-06T19:37:40.905893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2172/2510878","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"CMS Physics: Technical Design Report V olume 1: Detector Performance and Software","venue":null,"work_id":"265d1902-b71c-4d7d-bb18-e90e27136075","year":2006},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.281518Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:7060377e5ee2653b2dfbe4f55d3200b0601c248367aea21600c9a91d438a913b","observation_id":"53c49339-51cd-475e-8d37-46b4db41926a","resolution":{"observed_at":"2026-08-06T19:37:40.399359Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02192","last_updated":"2025-03-04T02:02:14Z","snapshot_observed_at":"2026-08-16T12:53:22.959122Z","submitted_at":"2025-03-04T02:02:14Z","title":"Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system","version":1},"cited_work":{"arxiv_id":"2503.02192","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.02192","snapshot_observed_at":"2026-08-06T19:37:40.588417Z","title":"Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system","venue":"hep-ex","work_id":"36ac50e6-4253-4aad-bb74-742b6278d628","year":2025},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.284923Z"},"links":{"cited_paper":"/paper/2503.02192","citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:4d8a7c79f223ea28c71dae38a691b1b89997bef107643dad5dc3d25fa60f8a1a","observation_id":"56fad8b2-36e7-4563-a874-4e00f149be60","resolution":{"observed_at":"2026-08-06T19:37:40.592118Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.288536Z","title":"CMS. The TriDAS project. Technical design report, vol. 1: The trigger systems","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.288536Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:1b24aae1626582e6cb85f7f65d3229d3bc5ce6a4c6f8109aa196ca88b2744205","observation_id":"cc30c262-6d69-4ccd-ad8f-32afe322f4ed","resolution":{"observed_at":"2026-08-06T19:37:40.288536Z","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":"2026-08-06T19:37:40.291934Z","title":"Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.291934Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:01442e2da6c0cdaf624b3b5e85608057dbe20178d2bedea94a96458cb0c68fb8","observation_id":"55099256-ef49-4c3a-8b46-c860f55b714c","resolution":{"observed_at":"2026-08-06T19:37:40.291934Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.875393Z","title":"FINN-R: An end-to-end deep- learning framework for fast exploration of quantized neural networks","venue":null,"work_id":"5a9d7ac2-08b6-4f6a-980a-1a3d4766a629","year":2018},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.295246Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:ec14800d32c4deeb8552c13033866f1946f74494e346aebe087050cdf15826c3","observation_id":"3590d478-b6a5-4055-80fd-cfbeec1950d4","resolution":{"observed_at":"2026-08-06T19:37:40.879280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.298767Z","title":"fastmachinelearning/hls4ml","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.298767Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:e0606608f9989f987ff962213ee2cef84278edd57c9562e46662e7f2b384f95d","observation_id":"a31622fc-cbc4-4818-8822-b206eaa6be2a","resolution":{"observed_at":"2026-08-06T19:37:40.298767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.07987","last_updated":"2019-07-24T15:43:05Z","snapshot_observed_at":"2026-08-14T17:13:16.714750Z","submitted_at":"2019-02-21T11:57:12Z","title":"Learning representations of irregular particle-detector geometry with distance-weighted graph networks","version":2},"cited_work":{"arxiv_id":"1902.07987","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.07987","snapshot_observed_at":"2026-08-06T19:37:40.573544Z","title":"Learning representations of irregular particle-detector geometry with distance-weighted graph networks","venue":"physics.data-an","work_id":"3752243f-ab0d-43f6-8799-565b8e880a2f","year":2019},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.302075Z"},"links":{"cited_paper":"/paper/1902.07987","citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:dc45a3bba5b50486df7c8a219f0298d9c4d137112e79e0184591be3de030ce85","observation_id":"9a408d6e-70e5-4e27-a4fe-6ef6fa42d552","resolution":{"observed_at":"2026-08-06T19:37:40.578396Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.866282Z","title":"The Belle II Physics Book","venue":null,"work_id":"a72df1c6-8a8f-435d-97d8-c457d97e00a5","year":2018},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.305774Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:df27addcfe978f60a28b465856a5e56b178118dcbdf4e643e5ab24b0ae9388f7","observation_id":"852f4c8d-91b5-4d36-a811-40a5ade9fb2b","resolution":{"observed_at":"2026-08-06T19:37:40.869719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.309102Z","title":"PyTorch 2: Faster Machine Learn- ing Through Dynamic Python Bytecode Transformation and Graph Compilation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.309102Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:566368cc0b716ef6fbb3ed93808a48694ffa05b560daa52512701b2cd3d08f94","observation_id":"fc514ca8-ff89-4cd0-a62e-61e98995b27e","resolution":{"observed_at":"2026-08-06T19:37:40.309102Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.855405Z","title":"https://github.com/NVIDIA/TensorRT","venue":null,"work_id":"e0a040cd-02d8-4eeb-a455-d045455a4f77","year":2025},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.313116Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:c3857b07fc3c91be74128c936e540d5517e995b2dea59d01d0c50956ff52d471","observation_id":"85c311fd-1bf0-4c8b-826a-ac33c99c940e","resolution":{"observed_at":"2026-08-06T19:37:40.859840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.846016Z","title":"https : / / github","venue":null,"work_id":"cdee4df1-c4b3-40ad-9a2e-908d63e8596a","year":2025},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.316469Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:cabe385d7312bc2c4165be932c75da4147fc146443b062a8bdb07df80482c0d1","observation_id":"9c5a0421-ee6f-43c7-a84b-e4f458740bee","resolution":{"observed_at":"2026-08-06T19:37:40.849488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.836734Z","title":"Fast Graph Compute","venue":null,"work_id":"096e0c76-91f5-4267-9064-26c68a2eef3c","year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.319623Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:7acb686ce7cb2caf38c38b0eee47c9a8d4946a7209606f4109ecc3362e859cfc","observation_id":"d46ad208-6e08-49bc-bd8c-3501f084e18e","resolution":{"observed_at":"2026-08-06T19:37:40.840084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.326299Z","title":"Status of the electromagnetic calorimeter trigger system at Belle II","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.326299Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:e1d0cf559e58953d9bc40f00b9d98ee717ffb8a19e5f4d5f0da54e1a2e7501c5","observation_id":"dc9a3ba2-6aa0-4320-a41c-f958ec9bee97","resolution":{"observed_at":"2026-08-06T19:37:40.326299Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-06T19:37:40.329400Z","title":"The Belle II Detector Upgrades Frame- work Conceptual Design Report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.329400Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:2d3e9cf5b3654fe7139615e6468e87703966669809d17e8818ab4df810fac2ac","observation_id":"b7b8dc6e-57e1-47f1-a761-057656eb0662","resolution":{"observed_at":"2026-08-06T19:37:40.329400Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.815753Z","title":"Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics","venue":null,"work_id":"a7589413-4ad6-4501-8cd7-85893766bd71","year":2024},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.332304Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:c0c6342a8e6c3ed4a2a70ed1e846736c41574a744136c8ab36565ea1dba91c70","observation_id":"a40cc420-d121-47e9-b0dd-775cb8ec08df","resolution":{"observed_at":"2026-08-06T19:37:40.820214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.806103Z","title":"Point-X: A Spatial-Locality-Aware Architecture for Energy- Efficient Graph-Based Point-Cloud Deep Learning","venue":null,"work_id":"4550956a-b3f8-4ec0-8e22-c3db15ecc7fb","year":2021},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.335476Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:111a237b607bd2fab03d408d8a2128d3be6535fd806ef512ef4b493432a60ebd","observation_id":"9c54992d-34a7-42f6-9122-17687d5ab187","resolution":{"observed_at":"2026-08-06T19:37:40.809786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.795938Z","title":"DeepBurning-GL: An Auto- mated Framework for Generating Graph Neural Net- work Accelerators","venue":null,"work_id":"3ec059ac-1bf3-4d2d-a324-81b9e4f8c3de","year":2020},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.338447Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:8d9bc0c351bded4dfa3e8685f509c201ba1e3fc0c31907c94835ffbc1aaa5971","observation_id":"6e251586-2337-45f5-9cd3-6d989b7247b9","resolution":{"observed_at":"2026-08-06T19:37:40.799367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.786098Z","title":"FlowGNN: A Dataflow Architec- ture for Real-Time Workload-Agnostic Graph Neural Network Inference","venue":null,"work_id":"e962e4eb-c09b-4b14-a1c8-84f4ed70f768","year":2023},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.341566Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:4177b30d77084b7727adeb9f45f7e591c87ed7e906c61b61d7980060576d5da0","observation_id":"b10134b2-bdef-4bd6-9b05-5d36f8049fc2","resolution":{"observed_at":"2026-08-06T19:37:40.789571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.776327Z","title":"GNNBuilder: An Automated Framework for Generic Graph Neu- ral Network Accelerator Generation, Simulation, and Optimization","venue":null,"work_id":"d848f364-06e9-488a-8a35-bdb82569ca10","year":2023},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.344980Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:035d0c054faa5500747c035ef251e4a149895eddc19b896838a44d7cedb4eb64","observation_id":"b0062d73-cee0-412a-b2c1-3179b18da5f9","resolution":{"observed_at":"2026-08-06T19:37:40.780196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.765589Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","venue":null,"work_id":"fdeb9412-18b2-4782-b3c1-d68b189f33f1","year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.347940Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:c68bbffb8f1219a2819c391d142d1f661861fd8e95368e1b0b1787f3c9bd6282","observation_id":"3af7ee40-06fb-4d4b-8311-5c95d9136f18","resolution":{"observed_at":"2026-08-06T19:37:40.769969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.891902Z","title":null,"venue":null,"work_id":"bfe13726-4d2b-45fd-a6a5-8b828d211f6c","year":2025},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":352,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.278028Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:4f940496495e0e6c9dd53dba9497d3df0bef40689c7b7773f0b728bc95c6fa19","observation_id":"c9b7e8b9-581c-46e2-bbce-6d72fd64e448","resolution":{"observed_at":"2026-08-06T19:37:40.895518Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1011.0352","last_updated":"2010-11-01T15:42:12Z","snapshot_observed_at":"2026-08-15T05:04:16.712983Z","submitted_at":"2010-11-01T15:42:12Z","title":"Belle II Technical Design Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1011.0352","snapshot_observed_at":"2026-08-06T19:37:40.274039Z","title":"arXiv: 1011","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.274039Z"},"links":{"cited_paper":"/paper/1011.0352","citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:8dc25f55687c38e543bab2c3d63dde2266b4bb0a6d14b68d4bfea3e046d81c6a","observation_id":"e5794225-971f-4ae3-a5ca-a83ef9f298fb","resolution":{"observed_at":"2026-08-06T19:37:40.274039Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:37:40.826568Z","title":"com / jkiesele/FastGraphCompute%7D%7D","venue":null,"work_id":"5696fccb-5781-4cdb-a228-6802203d71db","year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.322998Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:467e1c017c11ad860812d67660caec662280eaa90670f2d5c5084f357c949c0b","observation_id":"66b276cf-b9ba-4d93-bc13-acb3c4743a14","resolution":{"observed_at":"2026-08-06T19:37:40.830375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-06T19:37:40.756336Z","title":"5281 / zenodo","venue":null,"work_id":"ad1bf1bc-c4d4-450e-8fc8-474896a3357d","year":null},"citing_paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T19:37:40.351084Z"},"links":{"citing_paper":"/paper/2507.05099"},"observation_digest":"sha256:588d14d2d5b625adc0b2cff9dd4cd7feb4438196a9ccedc366c9f3f015f54266","observation_id":"78f93199-a99b-4ef4-a895-6187652613a3","resolution":{"observed_at":"2026-08-06T19:37:40.759620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.05099","last_updated":"2025-07-07T15:19:04Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-08T14:06:51.040620Z","submitted_at":"2025-07-07T15:19:04Z","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":2,"metadata_mismatch":1,"parse_uncertain":1,"unresolved":9,"verified_exact":3,"verified_fuzzy":17},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.05099."}