{"as_of":"2026-08-09T23:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8a9a9d11f74e856920884939f41317fe604246428459536f30bcb9a6525dcb63","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:22:13.702596Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-14T23:07:42.838562Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.06785","last_updated":"2022-12-13T17:59:20Z","snapshot_observed_at":"2026-07-06T14:30:12.658809Z","submitted_at":"2022-12-13T17:59:20Z","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders","version":1},"cited_work":{"arxiv_id":"2212.06785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.06785","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2212.06785 , year=","venue":null,"work_id":"30b87911-a3ff-415b-b71b-524cd2e82709","year":null},"citing_paper":{"arxiv_id":"2303.16199","last_updated":"2024-09-18T23:54:36Z","snapshot_observed_at":"2026-08-06T06:36:02.994951Z","submitted_at":"2023-03-28T17:59:12Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","version":3},"reference_index":190,"source":"arxiv_source","source_observed_at":"2026-05-14T23:07:42.245641Z"},"links":{"cited_paper":"/paper/2212.06785","citing_paper":"/paper/2303.16199"},"observation_digest":"sha256:a67209db6f4d20c785cdbd1289b35df94e6cdeda10c1e1cee8e00f0bfd9da00d","observation_id":"2803aabb-95af-4a63-a2d0-a5601300ee6e","resolution":{"observed_at":"2026-05-14T23:07:42.841762Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06785","last_updated":"2022-12-13T17:59:20Z","snapshot_observed_at":"2026-07-06T14:30:12.658809Z","submitted_at":"2022-12-13T17:59:20Z","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06785","snapshot_observed_at":"2026-08-07T12:22:13.702596Z","title":"Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24641","last_updated":"2025-05-30T14:28:06Z","snapshot_observed_at":"2026-08-09T19:45:38.985316Z","submitted_at":"2025-05-30T14:28:06Z","title":"A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T12:22:13.702596Z"},"links":{"cited_paper":"/paper/2212.06785","citing_paper":"/paper/2505.24641"},"observation_digest":"sha256:cb76fcd2db0e360e3331bf72d1c02f82b8c6d2d8e841ba430ec4adb3fb24a7ae","observation_id":"e27b9a77-4e79-48e2-bd2f-1a1936a009dd","resolution":{"observed_at":"2026-08-07T12:22:13.702596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2212.06785/citation-record","integrity":"/paper/2212.06785/integrity","json":"/paper/2212.06785/citation-record.json","paper":"/paper/2212.06785"},"outbound":[],"paper":{"arxiv_id":"2212.06785","last_updated":"2022-12-13T17:59:20Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T14:30:12.658809Z","submitted_at":"2022-12-13T17:59:20Z","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.06785."}