{"as_of":"2026-08-07T23:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fd1cf6fc6b21bdd91caad2ad3ed525a8aa8275fb600ada633ed0e1f5da5cab16","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:28:52.598295Z","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":"2026-07-04T03:29:31.158429Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":"2404.00308","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-07-04T03:29:31.158429Z","title":"St-llm: Large language models are effective tem- poral learners","venue":null,"work_id":"f3061161-8e9c-4661-b0e5-c9fb55fddf74","year":2024},"citing_paper":{"arxiv_id":"2404.16994","last_updated":"2024-04-29T14:52:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-04-25T19:29:55Z","title":"PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T20:21:57.873354Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2404.16994"},"observation_digest":"sha256:4d92507d6fc9e0514f8ea361e492b4145cc4a28f2c766d2adfcfa9354f5a2377","observation_id":"665d2878-2e21-48d5-9f86-71d404dab043","resolution":{"observed_at":"2026-05-15T20:21:58.003748Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":"2404.00308","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-07-04T03:29:31.158429Z","title":"St-llm: Large language models are effective tem- poral learners","venue":null,"work_id":"f3061161-8e9c-4661-b0e5-c9fb55fddf74","year":2024},"citing_paper":{"arxiv_id":"2408.16500","last_updated":"2024-08-29T12:59:12Z","snapshot_observed_at":"2026-08-05T11:54:14.447608Z","submitted_at":"2024-08-29T12:59:12Z","title":"CogVLM2: Visual Language Models for Image and Video Understanding","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-16T20:10:27.633010Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2408.16500"},"observation_digest":"sha256:d5305ee73236571d30c1e989de0a66ee3d6ba1127a13c4ae217b9c967522f8e7","observation_id":"ed258d1e-0ec7-43b8-9f8e-ec97490736e6","resolution":{"observed_at":"2026-05-16T20:10:27.812133Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":"2404.00308","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-07-04T03:29:31.158429Z","title":"St-llm: Large language models are effective tem- poral learners","venue":null,"work_id":"f3061161-8e9c-4661-b0e5-c9fb55fddf74","year":2024},"citing_paper":{"arxiv_id":"2501.05067","last_updated":"2026-04-20T07:42:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-09T08:43:57Z","title":"LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-23T06:01:00.775721Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2501.05067"},"observation_digest":"sha256:bf8869d1ceab33301ab3883125451e009f6f1d069e304c1cd921b89fa19fd3bd","observation_id":"78580b7e-cc0b-43eb-a14e-cdcdd67b983e","resolution":{"observed_at":"2026-05-23T06:02:37.327862Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-08-07T10:28:52.598295Z","title":"St-llm: Large language models are effective temporal learners.arXiv preprint arXiv:2404.00308, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05260","last_updated":"2025-06-05T17:21:16Z","snapshot_observed_at":"2026-08-07T10:19:37.470353Z","submitted_at":"2025-06-05T17:21:16Z","title":"LeanPO: Lean Preference Optimization for Likelihood Alignment in Video-LLMs","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T10:28:52.598295Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2506.05260"},"observation_digest":"sha256:89ae70ea86da4b90120f6a3e9c90cc64ff63a57402c001a5070c813514ed12f4","observation_id":"b1649634-71a0-4263-bd39-1fb94d03ec3e","resolution":{"observed_at":"2026-08-07T10:28:52.598295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-08-06T22:36:37.979162Z","title":"St-llm: Large language models are effective temporal learners.arXiv:2404.00308, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21184","last_updated":"2025-06-26T12:43:43Z","snapshot_observed_at":"2026-08-07T05:11:16.145844Z","submitted_at":"2025-06-26T12:43:43Z","title":"Task-Aware KV Compression For Cost-Effective Long Video Understanding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:36:37.979162Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2506.21184"},"observation_digest":"sha256:872aca68c02c44b8f73b7268c8ca699b5b6a25d3a29def7a6656e78ac69544c2","observation_id":"8e75c140-9583-4936-995e-a5ad9f726e48","resolution":{"observed_at":"2026-08-06T22:36:37.979162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-08-06T22:00:06.708059Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00068","last_updated":"2025-06-28T12:12:06Z","snapshot_observed_at":"2026-08-07T14:17:56.782121Z","submitted_at":"2025-06-28T12:12:06Z","title":"MANTA: Cross-Modal Semantic Alignment and Information-Theoretic Optimization for Long-form Multimodal Understanding","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T22:00:06.708059Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2507.00068"},"observation_digest":"sha256:ac2f5f6b6d032b7fb2a5b4f3076397920d8b3cbd1db70948c5866a242079fc54","observation_id":"320634d2-39d7-4fb8-96d5-2317345521d9","resolution":{"observed_at":"2026-08-06T22:00:06.708059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners","version":1},"cited_work":{"arxiv_id":"2404.00308","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00308","snapshot_observed_at":"2026-07-04T03:29:31.158429Z","title":"St-llm: Large language models are effective tem- poral learners","venue":null,"work_id":"f3061161-8e9c-4661-b0e5-c9fb55fddf74","year":2024},"citing_paper":{"arxiv_id":"2606.19706","last_updated":"2026-06-18T02:05:14Z","snapshot_observed_at":"2026-07-06T23:54:56.685241Z","submitted_at":"2026-06-18T02:05:14Z","title":"NEST: Narrative Event Structures in Time for Long Video Understanding","version":1},"reference_index":284,"source":"arxiv_source","source_observed_at":"2026-06-26T17:57:55.366051Z"},"links":{"cited_paper":"/paper/2404.00308","citing_paper":"/paper/2606.19706"},"observation_digest":"sha256:96b25b1804bfa8ad7c65a3d848b2c964bb7f052e6d34361a1bc369ce4775a81f","observation_id":"8a599412-eb10-4a8e-bca7-196d225759ce","resolution":{"observed_at":"2026-07-04T03:29:31.160170Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2404.00308/citation-record","integrity":"/paper/2404.00308/integrity","json":"/paper/2404.00308/citation-record.json","paper":"/paper/2404.00308"},"outbound":[],"paper":{"arxiv_id":"2404.00308","last_updated":"2024-03-30T10:11:26Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-03T16:21:15.084317Z","submitted_at":"2024-03-30T10:11:26Z","title":"ST-LLM: Large Language Models Are Effective Temporal Learners"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.00308."}