{"as_of":"2026-08-10T08:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d8e93fbdede7b31ab59b10c03bfd43707809514a3ca10f7657c5bf6603620ad1","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:30:01.599045Z","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":"2026-07-03T14:08:22.075048Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-08-07T15:30:01.599045Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models.arXiv preprint arXiv:2501.16566, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17090","last_updated":"2025-05-20T22:14:38Z","snapshot_observed_at":"2026-08-08T00:26:41.300341Z","submitted_at":"2025-05-20T22:14:38Z","title":"EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:01.599045Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2505.17090"},"observation_digest":"sha256:ad12740a690596d399359e0474a693d9ed2fff9a284ac05223f3c5d6ad486ab4","observation_id":"2709bacd-1e47-4e65-b0df-341064a66a43","resolution":{"observed_at":"2026-08-07T15:30:01.599045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-08-06T13:07:27.597574Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.21015","last_updated":"2025-07-28T17:28:08Z","snapshot_observed_at":"2026-08-09T20:11:38.562500Z","submitted_at":"2025-07-28T17:28:08Z","title":"Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T13:07:27.597574Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2507.21015"},"observation_digest":"sha256:f5925bbadd80168f53542dbc8603c0dfb78aa88e49b663015fac97f6bc00a9b3","observation_id":"9ff15cd6-87af-4345-9f2f-47c7635f8d50","resolution":{"observed_at":"2026-08-06T13:07:27.597574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-08-02T20:14:04.044246Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models.arXiv preprint arXiv:2501.16566, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.23802","last_updated":"2026-07-08T14:47:50Z","snapshot_observed_at":"2026-08-08T13:47:11.850462Z","submitted_at":"2026-02-27T08:42:52Z","title":"EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T20:14:04.044246Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2602.23802"},"observation_digest":"sha256:2712dc047bf0301c234b62d02d97640c793e79e0ed872138c5f44398dc3de767","observation_id":"53e94391-8752-4c87-91f6-da012278d3c6","resolution":{"observed_at":"2026-08-02T20:14:04.044246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":"2501.16566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-03T14:08:22.075048Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":"09f0c970-e564-4bbb-a3ab-e2543da72e9f","year":2025},"citing_paper":{"arxiv_id":"2604.23348","last_updated":"2026-04-25T15:10:25Z","snapshot_observed_at":"2026-07-06T23:09:34.300163Z","submitted_at":"2026-04-25T15:10:25Z","title":"EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-08T08:34:14.297331Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2604.23348"},"observation_digest":"sha256:cad3cdf7a5ef6ff574f07049bbbd8c787c364453dcccfa44418c88e52cad4f2e","observation_id":"a6e8d0c8-77e7-4658-85ff-ef5eb51f3f29","resolution":{"observed_at":"2026-05-11T20:31:15.298129Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":"2501.16566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-03T14:08:22.075048Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":"09f0c970-e564-4bbb-a3ab-e2543da72e9f","year":2025},"citing_paper":{"arxiv_id":"2605.08847","last_updated":"2026-05-09T10:01:21Z","snapshot_observed_at":"2026-08-02T06:59:30.684410Z","submitted_at":"2026-05-09T10:01:21Z","title":"EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-12T02:32:35.333227Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2605.08847"},"observation_digest":"sha256:31f1561ede1f390b151ed02b6d9c841c1b5b3e99466ff4dec5d677ea9f348b75","observation_id":"2596fc17-bdad-4a8a-80d2-2093b1c4b358","resolution":{"observed_at":"2026-05-12T07:31:33.427912Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":"2501.16566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-03T14:08:22.075048Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":"09f0c970-e564-4bbb-a3ab-e2543da72e9f","year":2025},"citing_paper":{"arxiv_id":"2605.09703","last_updated":"2026-05-10T18:51:34Z","snapshot_observed_at":"2026-08-03T00:51:48.886685Z","submitted_at":"2026-05-10T18:51:34Z","title":"MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-12T02:50:10.046572Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2605.09703"},"observation_digest":"sha256:01f123333730399ffc72d19efd39f63ef467abc647535158e84fdde50557bc07","observation_id":"d08fce51-bbd1-4dd9-ba08-0544184343ba","resolution":{"observed_at":"2026-05-12T02:51:17.689880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":"2501.16566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-03T14:08:22.075048Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":"09f0c970-e564-4bbb-a3ab-e2543da72e9f","year":2025},"citing_paper":{"arxiv_id":"2606.11385","last_updated":"2026-07-31T21:56:40Z","snapshot_observed_at":"2026-08-06T23:11:20.249226Z","submitted_at":"2026-06-09T19:08:00Z","title":"DeceptionX: From Multimodal Evidence to Explainable Deception Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T13:15:49.796647Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2606.11385"},"observation_digest":"sha256:384886f2635626d167168d7b5e1cbf21ac617105849159d580210bbc9ff6ca5b","observation_id":"c9ace2b9-3300-4a4d-b359-546770cf6fdd","resolution":{"observed_at":"2026-07-03T05:27:40.107506Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-08-04T03:03:32.875571Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.11385","last_updated":"2026-07-31T21:56:40Z","snapshot_observed_at":"2026-08-06T23:11:20.249226Z","submitted_at":"2026-06-09T19:08:00Z","title":"DeceptionX: From Multimodal Evidence to Explainable Deception Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T03:03:32.875571Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2606.11385"},"observation_digest":"sha256:781327211fc4ce00392c70eddc881396fda4298e1849e3b55164744c374a9111","observation_id":"b03e81a9-caae-4d49-98d9-88d8ae409121","resolution":{"observed_at":"2026-08-04T03:03:32.875571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":"2501.16566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-03T14:08:22.075048Z","title":"Affectgpt: A new dataset, model, and benchmark for emotion understanding with multimodal large language models","venue":null,"work_id":"09f0c970-e564-4bbb-a3ab-e2543da72e9f","year":2025},"citing_paper":{"arxiv_id":"2606.13192","last_updated":"2026-06-11T11:00:16Z","snapshot_observed_at":"2026-08-07T11:57:31.923028Z","submitted_at":"2026-06-11T11:00:16Z","title":"Reasoning for Mobile User Experience with Multimodal LLMs: Task, Benchmark, and Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T07:12:44.999056Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2606.13192"},"observation_digest":"sha256:1087f394f1cab114dc12c7eeae5c1d240bc55a10d1c553aa24dd9da4e964e04c","observation_id":"35ba8b94-5697-4183-ac91-a172fc11c732","resolution":{"observed_at":"2026-07-03T14:08:22.076550Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-07-31T14:47:03.023064Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models , shorttitle =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28204","last_updated":"2026-07-30T13:43:40Z","snapshot_observed_at":"2026-08-09T21:37:13.572537Z","submitted_at":"2026-07-30T13:43:40Z","title":"Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony","version":1},"reference_index":293,"source":"arxiv_source","source_observed_at":"2026-07-31T14:47:03.023064Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2607.28204"},"observation_digest":"sha256:4a6e4045e796f8f858de17ab083078971129aa913ac0c1f15ef4389ac2dede23","observation_id":"0cde136d-44ef-459f-8cce-3807cd4da7be","resolution":{"observed_at":"2026-07-31T14:47:03.023064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16566","snapshot_observed_at":"2026-08-03T00:52:08.338727Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28649","last_updated":"2026-06-02T13:44:54Z","snapshot_observed_at":"2026-08-06T19:46:32.809672Z","submitted_at":"2026-06-02T13:44:54Z","title":"COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T00:52:08.338727Z"},"links":{"cited_paper":"/paper/2501.16566","citing_paper":"/paper/2607.28649"},"observation_digest":"sha256:5e3ab2f5e0b2c460baf3acc2f5e9c646d7fbea41dc3ead02b141cf96b5c50904","observation_id":"d1a33118-3b7d-49d1-b429-eb24363fd0d6","resolution":{"observed_at":"2026-08-03T00:52:08.338727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2501.16566/citation-record","integrity":"/paper/2501.16566/integrity","json":"/paper/2501.16566/citation-record.json","paper":"/paper/2501.16566"},"outbound":[],"paper":{"arxiv_id":"2501.16566","last_updated":"2025-05-07T13:20:08Z","latest_version":2,"primary_category":"cs.HC","snapshot_observed_at":"2026-08-09T21:36:51.990576Z","submitted_at":"2025-01-27T23:18:39Z","title":"AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2501.16566."}