{"as_of":"2026-08-06T01:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67cedb25fdcf1737e88ec4d386d2534a8e6047feac45449d6b247cd8dc4b23d2","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T06:56:13.966770Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2607.11120/citation-record","integrity":"/paper/2607.11120/integrity","json":"/paper/2607.11120/citation-record.json","paper":"/paper/2607.11120"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.19328","last_updated":"2026-04-14T11:07:41Z","snapshot_observed_at":"2026-07-06T21:30:14.357703Z","submitted_at":"2025-05-25T21:29:00Z","title":"BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.19328","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Bah dataset for am- bivalence/hesitancy recognition in videos for behavioural change,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/2505.19328","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:1313e3d45d06fe09030bb1c67abba35730c1f5e2d74776c4be1c9236e5f237dd","observation_id":"2d68d521-0104-4ade-8ce8-39e6064b0451","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Conflict-aware multi- modal fusion for ambivalence and hesitancy recognition,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:457275dcc0242e8a9dc9fc1c95f72eb3968a89e1356fc9d9de6096572e2c2a55","observation_id":"8213a5da-2a24-4e0f-8153-a5300a718842","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10399","last_updated":"2025-03-13T14:21:46Z","snapshot_observed_at":"2026-07-06T20:51:59.624697Z","submitted_at":"2025-03-13T14:21:46Z","title":"HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10399","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Hsemotion team at abaw-8 com- petition: Audiovisual ambivalence/hesitancy, emotional mimicry intensity and facial expression recognition,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/2503.10399","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:866b5677ad125ba7b1f2bcc5cd8f3f2d7a54cd5fb1cb7e8c52eab6aa0b7b376a","observation_id":"a73b7550-8d18-4c32-9fdc-cec8b4ad8eb9","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"The 6th affec- tive behavior analysis in-the-wild (abaw) competition,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:8f2113f7b22d09b75f3011d58283942b1a7f9cd6789e9cac5f0a1ead2c43b859","observation_id":"8d0f254b-22be-4e25-9e68-f8b8378e88eb","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Context-dependent sen- timent analysis in user-generated videos,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:7a6a8ed27e2cab267f1cac0d7f18a9d5f66f97f3d41be9ccaf0cd41aeba26a52","observation_id":"08a55e9e-99b4-41f7-89f1-ec068d77001f","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Verbal and nonverbal clues for real-life deception detection,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:178c4a75a632855284d1dab449a93fffb0d5d156e1db29492fd0f234b8259c86","observation_id":"eba1441a-cb51-4604-94da-ae1f6375d3ff","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Videomae: Masked autoencoders are data-efficient learners for self- supervised video pre-training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:97b751fa3a08e848d1500a3eb5174723f7119eed36d0cfb3b919a30b66904393","observation_id":"91d87e8c-50a2-44df-acfd-68eccaf69a81","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Hubert: Self- supervised speech representation learning by masked pre- diction of hidden units,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:6cd1b96f9d48d22c355466984e920fe217f24fce65cb365e607e3155089c283b","observation_id":"a533b303-1935-49e3-8f21-57684199402a","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Dawn of the transformer era in speech emotion recog- nition: closing the valence gap,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:a624eab0e5aab45314b7e7911ba2c81289efafb81d18f1238c826640b60857bb","observation_id":"03c1026c-abac-443e-82d8-760aed9b4c35","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Roberta: A robustly optimized bert pretraining ap- proach,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:e38ef7f4440f0d54633cee01d75361639783ae56cbd7fe7a1f8a5e876da58d01","observation_id":"f1f90285-246c-4a54-a800-39991673c3c4","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Goemotions: A dataset of fine-grained emotions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:3a731a8e297fec5af78725a2dba70b6df57a12cd30967bd77f07d6e6c9064d43","observation_id":"29534ab9-534b-453c-a415-ec13352c8385","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.03654","last_updated":"2021-10-06T21:02:00Z","snapshot_observed_at":"2026-07-06T09:26:29.068023Z","submitted_at":"2020-06-05T19:54:34Z","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.03654","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Deberta: Decoding-enhanced bert with disentangled attention,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/2006.03654","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:7d949966e436ec70bf37073344b934c81554219f3d23e4957ac4483b0a30e25f","observation_id":"e8199d11-9267-438e-95ee-20b14e83425e","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"On calibration of modern neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:fc1dcbdc4346af64f736c61d0edbf646d901ab2a552be1030fc114803ad94995","observation_id":"008f573a-bcd0-4f65-9b60-6141c06c66c0","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:7c2ef6e04e65c9d1ed195ba1df24b1c6a63dad4ba4ee421fe85c9fd6699d98ab","observation_id":"b1c1e0f5-bed7-459f-83a0-b2fcfa5f994e","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"wav2vec 2.0: A framework for self-supervised learning of speech representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:1af39270071b1151a707932cc16a8a3b1de78608e232c958d280a680fcf13108","observation_id":"9d7998e1-9932-4f38-9009-4a8d4cda96b5","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Rethinking the inception architecture for computer vision,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:c88eabd5cd5d459c18249b39d673245b74bd7f0d1f4801aa145488722135c874","observation_id":"d2e60520-42bf-4977-9cb8-27646d0a34be","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Robust speech recog- nition via large-scale weak supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:2839c5890736c898192b80472b93fb0578a38a37ecd6be73efd4b91d52e99552","observation_id":"d6c0700b-eba1-4f5f-8270-bf59e8e92731","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:2ee7dd70cff55f88f5d57720993c5faa28f36e95278d6173cf4a10a0d5757722","observation_id":"7b979ff9-573b-4314-bd4f-80793cbb780b","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"R-drop: Regularized dropout for neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:e5ac5e894bc312cd65b158326e1b76091bf8858d138b2718bb4e914dfd502f6d","observation_id":"fe2d5514-f9e1-4557-ae6d-e1ef7aa8a9cf","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:74d41ff4907f856f6dfaedfa9cbc46dbd86cb8e6b2f7d3f29d43f750c5b471e1","observation_id":"a51271b6-66c9-4d06-91ba-dbeab03e03cd","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Transformers: State-of-the-art natural language process- ing,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:d55c68e20282fa40c054955550dd021b7692a05dbbe1e7437a189594a08a8d66","observation_id":"982a2056-e8e4-422d-bbae-6810b73a47ae","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Efron and R","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:9e300a0ab5935fb543436dfe6c01f26b6f76e9513eb6f3c3c841846ee7361af3","observation_id":"99e7f937-23c8-4c38-bf0f-b17542b3d652","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.08172","last_updated":"2019-06-14T05:49:22Z","snapshot_observed_at":"2026-08-05T19:15:35.517082Z","submitted_at":"2019-06-14T05:49:22Z","title":"MediaPipe: A Framework for Building Perception Pipelines","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.08172","snapshot_observed_at":"2026-07-14T06:56:13.966770Z","title":"Mediapipe: A frame- work for building perception pipelines,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T06:56:13.966770Z"},"links":{"cited_paper":"/paper/1906.08172","citing_paper":"/paper/2607.11120"},"observation_digest":"sha256:5b1f221322eb78ee4f0f282a89c21aab4134b5eff75306fa6f9a9787b67e619f","observation_id":"0b66660e-3f1f-4291-8729-ed3d0c723bfb","resolution":{"observed_at":"2026-07-14T06:56:13.966770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11120","last_updated":"2026-07-13T05:52:40Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-16T23:19:12.682428Z","submitted_at":"2026-07-13T05:52:40Z","title":"Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":23},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.11120."}