{"as_of":"2026-08-09T22:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c20964a70d55b49d0be934189faf3b93f375439fa0f5a140e615d55b2f7e7c3","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T00:58:27.279537Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.04078/citation-record","integrity":"/paper/2508.04078/integrity","json":"/paper/2508.04078/citation-record.json","paper":"/paper/2508.04078"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T00:58:28.161410Z","title":"ACM Trans","venue":null,"work_id":"c302c1be-7da4-41f6-b430-fae1cc0c8448","year":1999},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.306115Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:fef46c55c4491d0724f1a5e85c77c194dde51446045023ed9c53e2e78f82fa91","observation_id":"c67534d0-68ca-430f-b0f8-465c76a5370e","resolution":{"observed_at":"2026-08-06T00:58:28.252489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12892","last_updated":"2025-08-21T12:52:11Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T16:31:44Z","title":"3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12892","snapshot_observed_at":"2026-08-06T00:58:26.478743Z","title":"arXiv preprint arXiv:2409.12892","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.478743Z"},"links":{"cited_paper":"/paper/2409.12892","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:923e91b268bb0ecbb2c1b1d5263395b3f6274a57ed76d01385b387499d47b1a3","observation_id":"cfa12657-adc5-41c5-b0d9-dd2f4faf6770","resolution":{"observed_at":"2026-08-06T00:58:26.478743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.24366","last_updated":"2025-03-31T17:46:18Z","snapshot_observed_at":"2026-08-07T16:19:45.760477Z","submitted_at":"2025-03-31T17:46:18Z","title":"StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.24366","snapshot_observed_at":"2026-08-06T00:58:26.810934Z","title":"Advances in Neural Information Processing Systems , 37: 80965–80986","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.810934Z"},"links":{"cited_paper":"/paper/2503.24366","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:1e5bb07593d2ad97faee73075924362a7b214d9ac15c2a34c600ba9cb6089c51","observation_id":"30095f28-500c-41a5-bd61-7587e077c7b5","resolution":{"observed_at":"2026-08-06T00:58:26.810934Z","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":"2026-08-06T00:58:27.675842Z","title":"Scene PSNR SSIM LPIPS Count (M) drjohnson 29.88 0.910 0.232 3.27 playroom 30.65 0.913 0.234 2.33 Table 6: Per-scene quantitative results on the Deep Blending Dataset (Hedman et al","venue":null,"work_id":"bf4d777d-963b-43d4-99f7-be502a0a7233","year":2018},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:27.193940Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:894ef053abdce42a2452efbac763f32df48705c146f8f2e3d972ac1b03208783","observation_id":"404aa247-dd49-47fe-970e-d1170ed7ca59","resolution":{"observed_at":"2026-08-06T00:58:27.775001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T00:58:27.502423Z","title":null,"venue":null,"work_id":"fb4f06ef-2200-4d67-bf42-e489dedcb156","year":2022},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:27.279537Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:e77b8b131eff30368a69f151c7f5f5b4ec1bb5cb2c0f010882097db51e10b03c","observation_id":"7dfa49ea-87fb-4c6d-a312-3a871220977a","resolution":{"observed_at":"2026-08-06T00:58:27.570742Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.03003","last_updated":"2017-04-10T18:25:29Z","snapshot_observed_at":"2026-08-01T21:11:47.069345Z","submitted_at":"2017-04-10T18:25:29Z","title":"Automated Curriculum Learning for Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.03003","snapshot_observed_at":"2026-08-06T00:58:26.094269Z","title":"In Siggraph, volume 96, 43–54","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":1996,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.094269Z"},"links":{"cited_paper":"/paper/1704.03003","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:37a3b4cb64830f272138a2189c4f2578716c7cad7cffe5cab3cad5356948f15f","observation_id":"f1a6b01a-0119-4096-8c03-4fb7068d0b91","resolution":{"observed_at":"2026-08-06T00:58:26.094269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1259","last_updated":"2014-10-07T18:08:30Z","snapshot_observed_at":"2026-07-06T03:53:24.366023Z","submitted_at":"2014-09-03T21:03:41Z","title":"On the Properties of Neural Machine Translation: Encoder-Decoder Approaches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1259","snapshot_observed_at":"2026-08-06T00:58:25.838967Z","title":"Advances in neural information processing systems , 29","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:25.838967Z"},"links":{"cited_paper":"/paper/1409.1259","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:02c836d55bf16e6f34969e097ab3a4ba494dc72d8c0d48078bfade73a66aa92a","observation_id":"1899822b-5377-4729-a8b7-d04de85595d8","resolution":{"observed_at":"2026-08-06T00:58:25.838967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T00:58:27.063408Z","title":"In Proceedings of the International Conference on Machine Learning (ICML)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:27.063408Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:7571a80aab60a4d7dfdc235e80568b3ccf9001bd603a3703f902c30d6c3202c1","observation_id":"2ad1f97e-8c91-42e9-8a42-7309d31f006c","resolution":{"observed_at":"2026-08-06T00:58:27.063408Z","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":"2026-08-06T00:58:28.513523Z","title":"In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2623–2631","venue":null,"work_id":"c7d70344-fa06-4c24-9e37-b05edf0cc7e7","year":null},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:25.672434Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:66414040a6cc711593604ee222aab7d4db445b3ca33d65ce9befeba4c4a5d540","observation_id":"85a90acb-40c1-49d9-9669-c82aec6a7c12","resolution":{"observed_at":"2026-08-06T00:58:28.612121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T00:58:27.914183Z","title":"ACM Transactions on Graphics, 42(4)","venue":null,"work_id":"d04c1782-bd86-41e6-b186-1a286203fa57","year":2024},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.631936Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:bd5be79594bb0b82e61e98d749076136623c2757cc9d9e321c3e662cb1b32e6c","observation_id":"ac87dd5f-dbe4-4673-9dda-6fd64fa843ee","resolution":{"observed_at":"2026-08-06T00:58:28.059007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T00:58:28.364538Z","title":"Advances in neural infor- mation processing systems, 37: 140138–140158","venue":null,"work_id":"cf50b1bb-c489-4e95-9d38-74741c799968","year":2019},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:25.960144Z"},"links":{"citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:0981317da1fa96c623644aeb2a80d221f26443644fc9c58ea8b3ff82227a9c02","observation_id":"ed4edba3-2379-4682-97fa-edf07e0e150f","resolution":{"observed_at":"2026-08-06T00:58:28.423329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13975","last_updated":"2025-01-27T15:20:11Z","snapshot_observed_at":"2026-08-07T22:15:09.114944Z","submitted_at":"2025-01-22T22:28:11Z","title":"3DGS$^2$: Near Second-order Converging 3D Gaussian Splatting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13975","snapshot_observed_at":"2026-08-06T00:58:26.977041Z","title":"arXiv:2501.13975","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T00:58:26.977041Z"},"links":{"cited_paper":"/paper/2501.13975","citing_paper":"/paper/2508.04078"},"observation_digest":"sha256:06867418965bd762e4d7f94c2c3f987235c17b864b994b16aa0643459788cdfa","observation_id":"5e0238f2-f720-416a-9481-d0d8beb92f8b","resolution":{"observed_at":"2026-08-06T00:58:26.977041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.04078","last_updated":"2025-08-06T04:37:39Z","latest_version":1,"primary_category":"cs.GR","snapshot_observed_at":"2026-08-06T00:58:23.286501Z","submitted_at":"2025-08-06T04:37:39Z","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2508.04078."}