{"as_of":"2026-08-06T10:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:253fe8f52b56e5d1631f2628f532dc5e14dba9cb390e22a3cac176c7616c26bf","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":"2026-06-29T08:48:16.052118Z","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":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.29879/citation-record","integrity":"/paper/2605.29879/integrity","json":"/paper/2605.29879/citation-record.json","paper":"/paper/2605.29879"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Seeground: See and ground for zero-shot open-vocabulary 3d visual grounding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:40fe50a462997d0643405b4f74a7d5c0ce1887ba13b9f1fa1172e091993052ee","observation_id":"13f0b8c7-3c94-45fa-8661-0ea53ab30af3","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:e1ca39db56e9f2fc461353755e30f89a8e6785ddddad88255f874283e299a32f","observation_id":"da4d9877-947b-4636-860a-8a024848cb57","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Omnimap: A general mapping framework integrating optics, geometry, and semantics,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:521ebf76c2bf8f904069c2dc3f7ab5ef61f32770d2293047d997193b78687de5","observation_id":"6c739ea4-65b8-4362-a87a-44c9d6d48532","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Dynamic open-vocabulary 3d scene graphs for long-term language- guided mobile manipulation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:9e4ace2a965c38dc1cb1362fe35aab9869c555c27a639a83631cd210c1386330","observation_id":"a0e707d5-132e-4a99-92e6-1113170a01e3","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Dynamicgsg: Dynamic 3d gaussian scene graphs for environment adaptation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:4f568f7d6bbda4b2cc64a57c416d42ffe3c8a54470f422bc741ea58d1f09d851","observation_id":"7ecfbd5a-1798-41fa-9e69-183fc407009c","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:0a7af63df9d1d6207a9977640e365d38344d790d1eab117427b3efbb0b4ecc33","observation_id":"a13b9823-8495-492e-9e99-47df7368ec09","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Hierarchical Open-V ocabulary 3D Scene Graphs for Language- Grounded Robot Navigation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:663ced5f578fb622288df4379875ea3f62a783de3c20d52cc8f85547774fe2c3","observation_id":"827155c4-023c-4509-b2c8-377b7f3ba172","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Opengs- slam: Open-set dense semantic slam with 3d gaussian splatting for object-level scene understanding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:4ece5738e159714ac345e880b9d3f2a60e6a9594e550d859879e6620eb667525","observation_id":"f005f4c8-6f91-493f-9c3f-c13071ecf4c1","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Langsplat: 3d language gaussian splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:4b2c86ccb8834f063e9d89dbfd82ed444b71433b0bceb7180c42af5e01d22dc9","observation_id":"e01d4992-9c72-4b13-ace2-bc8101e313a7","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Opengs-fusion: Open-vocabulary dense mapping with hybrid 3d gaussian splatting for refined object-level understanding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:b30aa870f4987bd491a489ab73613025d9cf378492880ba3f5be4ec833b8c8f9","observation_id":"1c5590a6-2761-4313-8cb6-4aae38ff2217","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Gaussian grouping: Segment and edit anything in 3d scenes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:17992e3d40e6d7f30a3e99d6ea9243ff39b45963d01432700c00c11121b37cad","observation_id":"04e898ff-c1e1-4af8-ae01-735e430993da","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Objectgs: Object-aware scene reconstruction and scene understanding via gaussian splatting,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:cf51fa1734a3125f409889285d1fd3c30bf1067fa06710a0ad408d37e8068057","observation_id":"ee17c3de-89d0-482d-858a-e48b38746a7c","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Visual programming for zero-shot open-vocabulary 3d visual grounding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:9191e7bbc57cf80187b35b4feee1b2362c88f812333f3f1b460f9903092f99f5","observation_id":"99ff33f3-130f-4fa8-a478-b920bdcc2ade","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Spazer: Spatial-semantic progressive reasoning agent for zero-shot 3d visual grounding,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:8c13c1978e0abfaf2521463328294d06dd3f1c459e7e66abf547565fc22ece20","observation_id":"aee81a7b-bdf6-4a8e-a678-6b6f346005a9","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Splatam: Splat track & map 3d gaussians for dense rgb-d slam,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:df06a6bf4e2cc7e63d3c43336c907aa11e5819d58797a4dec2449c3ad4afd04e","observation_id":"3709b2c3-3c3d-407d-9caa-2c82a220a1aa","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Rgbd gs-icp slam,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:4589c5a11f7b010fd53e0772e82c43a1056147462a9d2b10fd303889e02b2c12","observation_id":"6581dd9d-1ecb-4181-a91b-602bdcaac698","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Gsfusion: Online rgb-d mapping where gaussian splatting meets tsdf fusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:886d4449f7a82cb8fd8ef5480fce3956612518174c9e77b706f3669135f90ade","observation_id":"a6046ede-bd2b-44be-b0ce-9d4cd8c87adc","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Segs-slam: Structure-enhanced 3d gaussian splatting slam with appearance embedding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:597355bbd4dfb7581d52b82962f6c9f5e0da697f617921d376e98dcdfefd8722","observation_id":"de15dc67-a832-45a3-9435-5a93bc61261a","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Sentence-bert: Sentence embeddings using siamese bert-networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:2af8407d2e0fe31c9675744252d4e74d3880fc4fa0f6c9a42990851d14d37549","observation_id":"7232e885-99f6-478f-804e-98bc4f19963c","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Sigmoid loss for language image pre-training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:e0eca7aa88af6d1e87204a0df3c9e85a6d220ae6bae61162e0a2cb53a2f47ba8","observation_id":"15d47d34-3967-46c2-9fde-4c2cb7c20c5e","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Foundations of spatial perception for robotics: Hierarchical representations and real-time systems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:08a9d16c883cb2262bc74c22abd2af3190aed629be6a642e5811b618936bb419","observation_id":"6ce662a3-65e4-4eca-80d4-51c77066186e","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Beyond bare queries: Open- vocabulary object grounding with 3d scene graph,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:3b7edd47a6c33577f7c4d522f812f74d373684d13881abdd5f34aae4b3c4f144","observation_id":"67a12a2c-7a9b-4cc2-beb2-3d7451c0d138","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15487","last_updated":"2024-10-08T05:32:25Z","snapshot_observed_at":"2026-08-05T09:27:23.060259Z","submitted_at":"2024-02-23T18:27:17Z","title":"RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation","version":2},"cited_work":{"arxiv_id":"2402.15487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.15487","snapshot_observed_at":"2026-07-01T21:16:13.640526Z","title":"Roboexp: Action-conditioned scene graph via interactive ex- ploration for robotic manipulation","venue":null,"work_id":"cf22893d-bdb3-4925-826a-917d45ae7eb7","year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"cited_paper":"/paper/2402.15487","citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:137483c0dbf07e0aa645e6422b94982d674ee652565345d33eef282750b45070","observation_id":"2fdc8292-6dc1-47ba-956d-8b00ff602a64","resolution":{"observed_at":"2026-06-29T08:53:16.007678Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Accelerated coordinate encoding: Learning to relocalize in minutes using rgb and poses,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:58cc3b052342881fa7120d61e5a34e93ef03bf91fbaac3f8b8986818853d2804","observation_id":"0958e5e1-f072-4c59-a6aa-86e2b3f440dc","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Llm-grounder: Open-vocabulary 3d visual grounding with large language model as an agent,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:0b91b7ae97dc59f13a1c2ccd63391da167c8423ce97c28c1fec1b249ccae7a96","observation_id":"97958f82-d9f1-47e8-a153-0e94764c2294","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Yolo- world: Real-time open-vocabulary object detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:a1007bb25e3315ad5e4129e1ec7df9a913c18eedc0bcb094ae52c0e389b04984","observation_id":"5ef619a7-9592-414e-9774-4c1fb0b9ea10","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:ca902b6e08cc5c9dae86408ba1127bde39e445a7d9c9e05a1b35b22a298d31ce","observation_id":"e057250a-6929-421b-af5e-5d2740269b0e","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Describe anything: Detailed localized image and video captioning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:f002d934ecfa25d9cf7e61bdbd2c96649bdd2111703ddda3308bfa6f95741511","observation_id":"04c8ec63-ecc6-4a39-89cb-fbd0f448a4ee","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":"2502.13923","doi":"10.48550/arxiv.2502.13923","metadata_source":"pith","pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-VL Technical Report","venue":"cs.CV","work_id":"69dffacb-bfe8-442d-be86-48624c60426f","year":2025},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:63b7f80240607c059af74354c635813f3652c0b635d08afa6cdbf5cefd6eda54","observation_id":"0a4a622b-4af3-4319-aca2-0962c382884d","resolution":{"observed_at":"2026-06-29T08:53:16.009949Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-12T05:19:13.082554+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T05:19:13.082554+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05797","last_updated":"2019-06-13T16:29:58Z","snapshot_observed_at":"2026-08-01T13:51:16.469557Z","submitted_at":"2019-06-13T16:29:58Z","title":"The Replica Dataset: A Digital Replica of Indoor Spaces","version":1},"cited_work":{"arxiv_id":"1906.05797","doi":"10.48550/arxiv.1906.05797","metadata_source":"pith","pith_arxiv_id":"1906.05797","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The Replica Dataset: A Digital Replica of Indoor Spaces","venue":"cs.CV","work_id":"8145b3bf-a202-46d8-a3bc-fad366dd5d4a","year":2019},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"cited_paper":"/paper/1906.05797","citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:a8c244d063bdba19710f7bd2caf184f90a0a70e0f24794692130bc5826f80d5b","observation_id":"e2769a21-3dd3-4fa4-8d49-336944b5210d","resolution":{"observed_at":"2026-06-29T08:53:16.004673Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-23T17:23:43.390569+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T17:23:43.390569+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Scannet: Richly-annotated 3d reconstructions of indoor scenes,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:d22feae0ce1573c4cf69914529a5b5a2e8b75ed95d2cac739c46ebac98428d08","observation_id":"90cd5d4b-4f18-4bde-bd5a-344e464f2e26","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Scanrefer: 3d object localization in rgb-d scans using natural language,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:3e583135a5c930ba258d2856814addb4f8b6cfdd3d81de0b831b22d096a5f3b8","observation_id":"8b024085-387e-4da5-b4e6-7e1afdff34d7","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T08:48:16.052118Z","title":"Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T08:48:16.052118Z"},"links":{"citing_paper":"/paper/2605.29879"},"observation_digest":"sha256:21ad4071af8d9569c24811b53cbe2ee21e00811d3ee3649b151892bb5e054313","observation_id":"34d74557-cfbb-4cbc-9fd6-9df0ed8a0426","resolution":{"observed_at":"2026-06-29T08:48:16.052118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.29879","last_updated":"2026-05-29T04:39:13Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T23:39:15.620929Z","submitted_at":"2026-05-28T13:04:17Z","title":"DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":3,"verified_fuzzy":0},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2605.29879."}