{"as_of":"2026-08-12T13:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f6fb4b5352d8b3cef40c58a4d424e700bb757bad742b0430f15ad4fe4cb23634","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:52:39.169470Z","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":"2026-07-03T19:28:52.490598Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-12T05:52:39.169470Z","title":"Fouhey, and Joyce Chai","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19774","last_updated":"2025-04-04T20:29:02Z","snapshot_observed_at":"2026-08-12T08:06:19.747349Z","submitted_at":"2024-11-29T15:20:29Z","title":"PerLA: Perceptive 3D Language Assistant","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:39.169470Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2411.19774"},"observation_digest":"sha256:fce5859754406fba210f839968549b9a09d7513e90f16b6fc05cfcf30e4fcdef","observation_id":"bc72bc50-87ed-4809-9aa5-94633375f916","resolution":{"observed_at":"2026-08-12T05:52:39.169470Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-11T23:48:15.239557Z","title":"3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02193","last_updated":"2025-03-11T05:58:39Z","snapshot_observed_at":"2026-08-11T23:42:18.928866Z","submitted_at":"2024-12-03T06:15:04Z","title":"LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:48:15.239557Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2412.02193"},"observation_digest":"sha256:a6d9da4f248fab0462f57d48b74efcbf42e6823db7bfa869a2accf304298f594","observation_id":"4c3d2d28-f487-4537-897c-3bcef10e31c9","resolution":{"observed_at":"2026-08-11T23:48:15.239557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-11T11:54:12.260380Z","title":"F.; and Chai, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14837","last_updated":"2024-12-19T13:27:58Z","snapshot_observed_at":"2026-08-12T07:04:24.020090Z","submitted_at":"2024-12-19T13:27:58Z","title":"ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T11:54:12.260380Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2412.14837"},"observation_digest":"sha256:3040ecc8daa979507f64c5d6017fbe8a352caaeb316bbc4fbc5beffc1bfd04ce","observation_id":"eaef4128-3b70-423e-ada1-6e307dfbd13e","resolution":{"observed_at":"2026-08-11T11:54:12.260380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-10T22:32:38.742812Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01366","last_updated":"2025-07-07T20:17:31Z","snapshot_observed_at":"2026-08-11T22:16:36.261774Z","submitted_at":"2025-01-02T17:20:41Z","title":"ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T22:32:38.742812Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2501.01366"},"observation_digest":"sha256:4dba50504d8f33f7282e98c18a1c1a203e409c74613a92edc2d6256ff28ea990","observation_id":"113b5b40-d4b0-477c-b9f7-d05a9c4c79e5","resolution":{"observed_at":"2026-08-10T22:32:38.742812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-10T21:48:27.536522Z","title":"F., and Chai, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.03879","last_updated":"2025-01-07T15:42:32Z","snapshot_observed_at":"2026-08-12T01:26:25.176273Z","submitted_at":"2025-01-07T15:42:32Z","title":"CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T21:48:27.536522Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2501.03879"},"observation_digest":"sha256:3a7bd01cfaac69e8514124ccdaa54664524ac699b7a625c0844e5b609b7e49dc","observation_id":"ba4c14e4-3ec0-4d57-bf35-ae7e34aae2ce","resolution":{"observed_at":"2026-08-10T21:48:27.536522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-08-07T15:02:56.480933Z","title":"3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16663","last_updated":"2025-05-22T13:27:54Z","snapshot_observed_at":"2026-08-07T14:55:11.946052Z","submitted_at":"2025-05-22T13:27:54Z","title":"CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T15:02:56.480933Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2505.16663"},"observation_digest":"sha256:918a4ba46e51ffd62603a3c3eb8269e2c42a9caebf5ed0201f38a5a05e66e5c4","observation_id":"902165a0-6a26-4d05-ae92-bfeb755b600c","resolution":{"observed_at":"2026-08-07T15:02:56.480933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":"2406.05132","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-07-03T19:28:52.490598Z","title":"3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination.arXiv preprint arXiv:2406.05132, 2024","venue":null,"work_id":"cf785472-7523-4fd2-ba65-86686b4316c3","year":2024},"citing_paper":{"arxiv_id":"2605.28490","last_updated":"2026-05-27T13:45:34Z","snapshot_observed_at":"2026-08-07T17:54:05.464503Z","submitted_at":"2026-05-27T13:45:34Z","title":"SSR3D-LLM: Structured Spatial Reasoning via Latent Steps for Fine-Grained Grounding in Unified 3D-LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T13:54:04.992565Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2605.28490"},"observation_digest":"sha256:2a63be97f3fcb48b7699d9c59f53bf87f6713e61af63e5f2dda32437c697a371","observation_id":"e43dfb42-8727-4567-8a44-e6994a9b983f","resolution":{"observed_at":"2026-06-29T14:03:29.798402Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination","version":3},"cited_work":{"arxiv_id":"2406.05132","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.05132","snapshot_observed_at":"2026-07-03T19:28:52.490598Z","title":"3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination.arXiv preprint arXiv:2406.05132, 2024","venue":null,"work_id":"cf785472-7523-4fd2-ba65-86686b4316c3","year":2024},"citing_paper":{"arxiv_id":"2606.17391","last_updated":"2026-06-16T01:04:55Z","snapshot_observed_at":"2026-08-12T12:18:03.805791Z","submitted_at":"2026-06-16T01:04:55Z","title":"NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T01:48:08.794210Z"},"links":{"cited_paper":"/paper/2406.05132","citing_paper":"/paper/2606.17391"},"observation_digest":"sha256:bde6d46c9d0971b3ee68be97bce143aa53cd9c047376bd7be54f85b37a874b36","observation_id":"8193472b-0835-44d0-bfd3-3974a0e5e1d5","resolution":{"observed_at":"2026-07-03T19:28:52.492234Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.05132/citation-record","integrity":"/paper/2406.05132/integrity","json":"/paper/2406.05132/citation-record.json","paper":"/paper/2406.05132"},"outbound":[],"paper":{"arxiv_id":"2406.05132","last_updated":"2025-03-20T23:06:14Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T08:00:27.226661Z","submitted_at":"2024-06-07T17:59:59Z","title":"3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.05132."}