{"as_of":"2026-08-08T10:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:854875450e90c4f35d1a680f453f43dd81bc386d929fb89d02b54ac15c0c6031","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T22:36:33.675765Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T07:10:36.316701Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.09867","snapshot_observed_at":"2026-08-03T07:10:36.316701Z","title":"Quantitative and quali- tative comparison of generative models for subject-specific gaze synthesis: Diffusion vs gan.arXiv preprint arXiv:2511.09867, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.21057","last_updated":"2026-07-23T15:51:24Z","snapshot_observed_at":"2026-08-06T15:59:07.787688Z","submitted_at":"2026-01-28T21:24:50Z","title":"Privatization of Synthetic Gaze: Attenuating State Signatures in Diffusion-Generated Eye Movements","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T07:10:36.316701Z"},"links":{"cited_paper":"/paper/2511.09867","citing_paper":"/paper/2601.21057"},"observation_digest":"sha256:8712708c35e24cc20452e6832be531b2b8dc78fe5936ea4cc2eef7c65e5f6c09","observation_id":"028e9353-76c2-4568-9986-c04041a4133c","resolution":{"observed_at":"2026-08-03T07:10:36.316701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2511.09867/citation-record","integrity":"/paper/2511.09867/integrity","json":"/paper/2511.09867/citation-record.json","paper":"/paper/2511.09867"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T22:36:33.234772Z","title":"Advances in eye tracking technology: theory, algorithms, and applications.Computational intelligence and neuroscience, 2016:7831469, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.234772Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:ae2d705132d514950ffc02c396c448e20a2e0ebeae7980d1ebe54d70fdb10236","observation_id":"d5d62d6f-50d2-42aa-8eb2-48a0a816329f","resolution":{"observed_at":"2026-08-03T22:36:33.234772Z","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":"2026-08-03T22:36:33.309418Z","title":"Eye-tracking in ar/vr: A technological review and future directions.IEEE Open Journal on Immersive Displays, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.309418Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:78e943a1e7a9173251e0793d8af77fa45125aaa68d06f099ca63e0206bc1832a","observation_id":"cb51d5ec-1626-42b5-b37e-377f90c45d42","resolution":{"observed_at":"2026-08-03T22:36:33.309418Z","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":"2026-08-03T22:36:33.374146Z","title":"Gazebase, a large-scale, multi-stimulus, longitudinal eye movement dataset.Scientific Data, 8(1):184, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.374146Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:49eaecea093602980adeb9ce77ce8f3363a59e3ffa2f5d1b493d80344974601e","observation_id":"a93d4b80-78b0-45d0-b87f-6122db922cbe","resolution":{"observed_at":"2026-08-03T22:36:33.374146Z","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":"2026-08-03T22:36:33.432889Z","title":"The promise of eye-tracking methodology in organizational research: A taxonomy, review, and future avenues.Organizational Research Methods, 22(2):590–617, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.432889Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:adb53475376d1a54107cc04327c3cda7ac7dc25383354142d785d434a64c6f2c","observation_id":"563c4d6e-99c3-452f-9bbe-3d98cab4b009","resolution":{"observed_at":"2026-08-03T22:36:33.432889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09819","last_updated":"2022-12-02T13:55:29Z","snapshot_observed_at":"2026-07-06T14:07:11.750709Z","submitted_at":"2022-10-18T12:57:30Z","title":"Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.09819","snapshot_observed_at":"2026-08-03T22:36:33.450155Z","title":"Eye-tracking based classification of mandarin chinese readers with and without dyslexia using neural sequence models.arXiv preprint arXiv:2210.09819, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.450155Z"},"links":{"cited_paper":"/paper/2210.09819","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:3e023f1cb56ee16c7d3d03108315b938dd9eb054fbdb084397cbe3728c9cda16","observation_id":"74284fa7-6faa-4c9f-bf38-6fc2b652c18c","resolution":{"observed_at":"2026-08-03T22:36:33.450155Z","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":"2026-08-03T22:36:33.455691Z","title":"Eye-tracking based autism spectrum disorder diagnosis using chaotic butterfly optimization with deep learning model.Computers, Materials & Continua, 76(2), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.455691Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:0c54e3ec9886111df34b87f278b92fcef81ae4aa2c88378e4381845408f819ff","observation_id":"88ec8dae-3578-45fb-a4f0-89d3817cddc8","resolution":{"observed_at":"2026-08-03T22:36:33.455691Z","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":"2026-08-03T22:36:33.461719Z","title":"Eye know you too: Toward viable end-to-end eye movement biometrics for user authentication.IEEE Transactions on Information Forensics and Security, 17:3151–3164, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.461719Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:3b5fe41c7196fcc28271e3640663d7eeb53d3c56d30471b5aec839dda6b9e103","observation_id":"cf7d485d-609e-45a2-81c2-7a2e05279a79","resolution":{"observed_at":"2026-08-03T22:36:33.461719Z","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":"2026-08-03T22:36:33.466653Z","title":"Gaze authentication: Factors influencing authentication performance.arXiv preprint arXiv:2509.10969, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.466653Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:0c8bf61613c72f1f63832a574270b094686dc6704ce5add9391a118e63e8752a","observation_id":"d71c0f01-154a-4e3a-923d-8a9182c147fa","resolution":{"observed_at":"2026-08-03T22:36:33.466653Z","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":"2026-08-03T22:36:33.473224Z","title":"Person identification using ocular biometrics with liveness detection, July 14 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.473224Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:3a1f7d964eaea8e8be09757e1824223ee019f622ba4934d30832da1ec6604621","observation_id":"4d6b3cf0-767a-467d-812f-a182f54978b7","resolution":{"observed_at":"2026-08-03T22:36:33.473224Z","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":"2026-08-03T22:36:33.478293Z","title":"Iris print attack detection using eye movement signals","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.478293Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:9c9c998c70007d19264ae0b1f0fdffcf22e7a930ccd183ab9313ad21da568163","observation_id":"1cdc6c97-759c-4793-b215-275bd397a949","resolution":{"observed_at":"2026-08-03T22:36:33.478293Z","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":"2026-08-03T22:36:33.484423Z","title":"Towards foveated rendering for gaze-tracked virtual reality.ACM Transactions On Graphics (TOG), 35 (6):1–12, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.484423Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:24746a961d915d2a4ad52772ce23caa21bb289029a0d06ca430e9e875229d8b6","observation_id":"f3925c24-9701-418e-9aa5-19798c2ee1a5","resolution":{"observed_at":"2026-08-03T22:36:33.484423Z","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":"2026-08-03T22:36:33.489211Z","title":"Improving user experience of eye tracking-based interaction: Introspecting and adapting interfaces.ACM Transactions on Computer-Human Interaction (TOCHI), 26(6):1–46, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.489211Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:69cb86e3b3a91a0c7434156d88a609328b375394f3ada2545e675043a51462ab","observation_id":"b612b30b-45a5-46b9-b8e2-b182fe48a02a","resolution":{"observed_at":"2026-08-03T22:36:33.489211Z","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":"2026-08-03T22:36:33.494908Z","title":"Brockmole, and Sidney K","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.494908Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:404e8697d0034b137358bec30376a3fb76ac5530faf70470f51f4ec6a6e5546c","observation_id":"0e3603ee-ad45-4f4d-ad53-acb61881ac18","resolution":{"observed_at":"2026-08-03T22:36:33.494908Z","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":"2026-08-03T22:36:33.500638Z","title":"A new comprehensive eye-tracking test battery concurrently evaluating the pupil labs glasses and the eyelink 1000.PeerJ, 7:e7086, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.500638Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:b966016160d1f555dd414b21c5e8bb12703cbe1b4e420403c18b44a1181d930c","observation_id":"c2a55cc3-7554-4586-a81e-6a3340c8fd55","resolution":{"observed_at":"2026-08-03T22:36:33.500638Z","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":"2026-08-03T22:36:33.507219Z","title":"Biometric verification via complex eye movements: The effects of environment and stimulus","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.507219Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:a44aacca99eda8ac59ff71113af031f22b17863cff9d8a630753e24850673e1f","observation_id":"632579fc-d4ae-4f61-a99c-23a2caf9c93e","resolution":{"observed_at":"2026-08-03T22:36:33.507219Z","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":"2026-08-03T22:36:33.512129Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.512129Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:bcc20b0da1528c056b7753be15e566c8d9c5dc79404e808e4d09a9a64246a1ca","observation_id":"99eab054-5422-4c9c-bf66-fd2de688b592","resolution":{"observed_at":"2026-08-03T22:36:33.512129Z","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":"2026-08-03T22:36:33.516510Z","title":"Supreyes: Super resolutin for eyes using implicit neural representation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.516510Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:d8f53cfa83aa3113084b9863634e2d8aec652c68602341d36940098d1d21b691","observation_id":"22f4f55b-dfa0-4fba-bdbc-00fea0347f15","resolution":{"observed_at":"2026-08-03T22:36:33.516510Z","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":"2026-08-03T22:36:33.521489Z","title":"A survey of advances in vision-based vehicle re-identification.Computer Vision and Image Understanding, 182:50–63, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.521489Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:32ac88d2ba633678c5da2645d9f1ea2fbb32a3d85b8fd7ac67daa5959c7e4236","observation_id":"2d651dd6-2fbd-4609-bc29-2c6bc186e891","resolution":{"observed_at":"2026-08-03T22:36:33.521489Z","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":"2026-08-03T22:36:33.526185Z","title":"Privacy-aware eye tracking using differential privacy","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.526185Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:5b5294313f829798724d19712bc04e465159d31a4817603465a188d07013950b","observation_id":"673e5727-9b6e-48cf-958a-957c098f3058","resolution":{"observed_at":"2026-08-03T22:36:33.526185Z","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":"2026-08-03T22:36:33.530689Z","title":"Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.Computer Methods and Programs in Biomedicine, 260: 108571, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.530689Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:390cd16e44382e77b1ee57b0b05219b5961298bbe3a657242cdfe7fe0a0cfc66","observation_id":"42502c30-7f3f-4bd1-82a9-afa9f0ff81b4","resolution":{"observed_at":"2026-08-03T22:36:33.530689Z","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":"2026-08-03T22:36:33.535956Z","title":"Eyes alive","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.535956Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:cd94f613495e4181f7b7bf6dd9b479c5c2744d371fb364a88cf2555a2ca90e29","observation_id":"303cac78-3bc8-4e97-b8a6-4b12f8c3e393","resolution":{"observed_at":"2026-08-03T22:36:33.535956Z","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":"2026-08-03T22:36:33.540695Z","title":"Eyesyn: Psychology-inspired eye movement synthesis for gaze-based activity recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.540695Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:b92635561ab2936e30efe1062e073b492e4aca65ed56e77c3f1801c2f6a4341b","observation_id":"aa3775c8-8c5e-4f11-b734-711327d77182","resolution":{"observed_at":"2026-08-03T22:36:33.540695Z","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":"2026-08-03T22:36:33.546977Z","title":"Generative adversarial networks.Communications of the ACM, 63(11):139–144, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.546977Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:60c2a9ec658f2fae02d7278f4df79c556685070540b451c10221acb29e0996bb","observation_id":"ce3d0786-fb15-415d-916e-792494f0e38e","resolution":{"observed_at":"2026-08-03T22:36:33.546977Z","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":"2026-08-03T22:36:33.551655Z","title":"Sp-eyegan: Generating synthetic eye movement data with generative adversarial networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.551655Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:54fd1ee48a1290c36ef92d45943ef62b3ba80d5be7641a0a0bb701a1b95ce5fe","observation_id":"4d21baec-158f-4679-b2fd-e3a53d86275c","resolution":{"observed_at":"2026-08-03T22:36:33.551655Z","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":"2026-08-03T22:36:33.556663Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.556663Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:c6852b0f82b76cf8e99c1a84cbef5333c1ae3d6e15d085f48d228704476ae6d4","observation_id":"5c51f420-52e6-4610-bee0-5b1ed9c104eb","resolution":{"observed_at":"2026-08-03T22:36:33.556663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.01240","last_updated":"2025-05-20T11:01:20Z","snapshot_observed_at":"2026-07-06T19:09:14.628371Z","submitted_at":"2024-09-02T13:14:21Z","title":"DiffEyeSyn: Diffusion-based User-specific Eye Movement Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.01240","snapshot_observed_at":"2026-08-03T22:36:33.560771Z","title":"Diffeyesyn: Diffusion-based user-specific eye movement synthesis.arXiv preprint arXiv:2409.01240, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.560771Z"},"links":{"cited_paper":"/paper/2409.01240","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:c20dac19203650be85857dbde467583a5070eb5cf0e62d99b5226ec3a832d6ff","observation_id":"42c2e42f-05ef-426f-9582-d6a477373c2e","resolution":{"observed_at":"2026-08-03T22:36:33.560771Z","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":"2026-08-03T22:36:33.565177Z","title":"Determining which sine wave frequencies correspond to signal and which correspond to noise in eye-tracking time-series.Journal of Eye Movement Research, 14(3):16, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.565177Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:76b5d816b42430939004916665656e7aa6a5e1b05451cffc171a2e741df3dfc5","observation_id":"8e163dc2-e6bb-45e3-be8a-9111059917ed","resolution":{"observed_at":"2026-08-03T22:36:33.565177Z","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":"2026-08-03T22:36:33.569245Z","title":"Evaluation of eye tracking signal quality for virtual reality applications: A case study in the meta quest pro","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.569245Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:22ee6b2a726f0fad17735e625f6b3f70f4c67ef80dc5e0d15ad7d854a279e167","observation_id":"605d0c0e-d8ce-458a-82af-7df3900127f9","resolution":{"observed_at":"2026-08-03T22:36:33.569245Z","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":"2026-08-03T22:36:33.573692Z","title":"Modeling physiologically plausible eye rotations","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.573692Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:eab4d92b5e7ffef34537299a7c57b01ba886afe2f7c4c7adee106e7262906258","observation_id":"447c76db-57d8-4d1e-9527-d901e46f959b","resolution":{"observed_at":"2026-08-03T22:36:33.573692Z","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":"2026-08-03T22:36:33.578044Z","title":"Eye movement synthesis","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.578044Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:5ef72cec5e9395e8bdd88c9bf400398c34a2200c8ad66ff63d019c606603612f","observation_id":"12fab32c-2365-4a21-a956-ed47bd68a65e","resolution":{"observed_at":"2026-08-03T22:36:33.578044Z","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":"2026-08-03T22:36:33.582131Z","title":"Natural eye motion synthesis by modeling gaze-head coupling","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.582131Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:8620463296b0eff71c576fe8563b63185a58dde60380b237304788cd0f73ec89","observation_id":"bb0530a7-9e2c-44da-bbbd-075146ae430b","resolution":{"observed_at":"2026-08-03T22:36:33.582131Z","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":"2026-08-03T22:36:33.586365Z","title":"Rendering of eyes for eye-shape registration and gaze estimation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.586365Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:993d19278404737da482f925f4229e4ebef203a8595a7646339d815c0c4882e2","observation_id":"fe3c7d21-8790-49ee-ba8a-3824826a0428","resolution":{"observed_at":"2026-08-03T22:36:33.586365Z","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":"2026-08-03T22:36:33.590685Z","title":"Live speech driven head-and-eye motion generators.IEEE transactions on visualization and computer graphics, 18(11):1902–1914, 2012","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.590685Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:987d6ddc8b647433bc524d2515d3c2d9e1864b90068d728aada99129a3aa2119","observation_id":"9496aaed-dd69-463b-951a-e6572bcaf04f","resolution":{"observed_at":"2026-08-03T22:36:33.590685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.09296","last_updated":"2018-09-10T06:59:06Z","snapshot_observed_at":"2026-07-06T06:57:51.694921Z","submitted_at":"2018-08-27T06:14:22Z","title":"Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.09296","snapshot_observed_at":"2026-08-03T22:36:33.598450Z","title":"Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools.arXiv preprint arXiv:1808.09296, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.598450Z"},"links":{"cited_paper":"/paper/1808.09296","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:92259a01628485d2976707f45fa90f4558562c2b2be3b37f41e85f72b8362c81","observation_id":"fca4d0ef-e93b-4ca0-a8a9-2df97a2d9428","resolution":{"observed_at":"2026-08-03T22:36:33.598450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.00970","last_updated":"2018-03-28T06:48:37Z","snapshot_observed_at":"2026-08-01T15:48:57.433633Z","submitted_at":"2018-03-28T06:48:37Z","title":"Eye movement simulation and detector creation to reduce laborious parameter adjustments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.00970","snapshot_observed_at":"2026-08-03T22:36:33.604140Z","title":"Eye movement simulation and detector creation to reduce laborious parameter adjustments.arXiv preprint arXiv:1804.00970, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.604140Z"},"links":{"cited_paper":"/paper/1804.00970","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:33bf0f5b01de429bc5aab18f0e422d208b6bfc439f48982aaedb89f862563e40","observation_id":"bb977b19-6b5a-4438-8a55-15b896425fd0","resolution":{"observed_at":"2026-08-03T22:36:33.604140Z","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":"2026-08-03T22:36:33.608946Z","title":"Eyecatch: Simulating visuomotor coordination for object interception.ACM Transactions on Graphics (TOG), 31(4):1–10, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.608946Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:2d55dac9f333b419d7f024f5c423b35cead53b5a01f230da5100efaa5b300a06","observation_id":"1b20af68-7363-4743-abf4-acbd4d63d50b","resolution":{"observed_at":"2026-08-03T22:36:33.608946Z","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":"2026-08-03T22:36:33.613767Z","title":"An introduction to the kalman filter","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.613767Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:e71cc57e30b9014fb2cca4ec403b401859a161380b5057238d763d403dda4e70","observation_id":"b6d76c1a-6717-47c8-a9e1-d43d6e74251c","resolution":{"observed_at":"2026-08-03T22:36:33.613767Z","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":"2026-08-03T22:36:33.618047Z","title":"Automatic scanpath generation with deep recurrent neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.618047Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:fd4c48a8a4fb8a5a169907a4c27dbcdbf0b88e2ddf299517c3e053860465fc15","observation_id":"9523959a-2d10-44f1-8f7e-7530a1f57349","resolution":{"observed_at":"2026-08-03T22:36:33.618047Z","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":"2026-08-03T22:36:33.622065Z","title":"Pathgan: Visual scanpath prediction with generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.622065Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:1218e9173f675477b0316671f983f79f913457829f9c96d584a0e777000d3470","observation_id":"46f5566b-322c-4d7a-986a-9f365988561d","resolution":{"observed_at":"2026-08-03T22:36:33.622065Z","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":"2026-08-03T22:36:33.626083Z","title":"Eyegan: Gaze-preserving, mask-mediated eye image synthesis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.626083Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:ae2edbea79d84caf7d3708c35aa73e9ffd9f05d5a0c0c0045804b96dc57549ec","observation_id":"daf8f368-584d-4a73-9139-b4fd477922a0","resolution":{"observed_at":"2026-08-03T22:36:33.626083Z","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":"2026-08-03T22:36:33.630410Z","title":"Fully convolutional neural networks for raw eye tracking data segmentation, generation, and reconstruction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.630410Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:6ebe2440cc8b69956db9eeced5bac5738d7b55dd65a71bb05ffa8fdfd7052a49","observation_id":"cd097d62-fe03-46b2-aecb-65eaa636e8a7","resolution":{"observed_at":"2026-08-03T22:36:33.630410Z","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":"2026-08-03T22:36:33.634492Z","title":"Next-generation deep learning based on simulators and synthetic data.Trends in cognitive sciences, 26(2):174–187, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.634492Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:2925be9604428e74d59a86028ec7afa88ff8bf6599d57f42c7a88ed31d7e6474","observation_id":"aee8b480-a1a9-473d-b11e-142f284f81d0","resolution":{"observed_at":"2026-08-03T22:36:33.634492Z","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":"2026-08-03T22:36:33.638480Z","title":"Hpcgen: Hierarchical k-means clustering and level based principal components for scan path genaration","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.638480Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:991c16bde952a9262e7c9d52dca553c778095a3f939ddc17afdecef111012330","observation_id":"360f7655-0a54-45a8-a49b-e4eeb452a1c1","resolution":{"observed_at":"2026-08-03T22:36:33.638480Z","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":"2026-08-03T22:36:33.642868Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.642868Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:9ad33ec1d75870639bfc7ec5cf82c246575c00169b08240f0b11e43468f62ab4","observation_id":"a70d49ee-9cd7-4d1f-9a96-2b2d642d6c97","resolution":{"observed_at":"2026-08-03T22:36:33.642868Z","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":"2026-08-03T22:36:33.648169Z","title":"Diffgaze: A diffusion model for modelling fine-grained human gaze behaviour on 360° images.ACM Transactions on Interactive Intelligent Systems, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.648169Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:d4c2f1e66f99dec48eba35b97c96bd4a3ed0b3fbb00d4e80a453667df5130e8f","observation_id":"a451adfd-c081-4778-948a-fda323c93115","resolution":{"observed_at":"2026-08-03T22:36:33.648169Z","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":"2026-08-03T22:36:33.652291Z","title":"Adding conditional control to text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.652291Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:a77056b2b90ef367369fc3bec204e898ad1d73157f77b03932a8765d45fa09d3","observation_id":"73bda3f9-d978-40e4-932e-be8075e2652e","resolution":{"observed_at":"2026-08-03T22:36:33.652291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.09761","last_updated":"2021-03-30T19:48:38Z","snapshot_observed_at":"2026-07-06T09:57:19.117228Z","submitted_at":"2020-09-21T11:20:38Z","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.09761","snapshot_observed_at":"2026-08-03T22:36:33.656438Z","title":"Diffwave: A versatile diffusion model for audio synthesis.arXiv preprint arXiv:2009.09761, 2020","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.656438Z"},"links":{"cited_paper":"/paper/2009.09761","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:afbfbaff76d436637319288a95b7ebcc4b3bdfdabb142c95cfa6d3bbeac90f80","observation_id":"5a49db44-da4e-4fc9-af21-9e6b5d516c5c","resolution":{"observed_at":"2026-08-03T22:36:33.656438Z","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":"2026-08-03T22:36:33.661417Z","title":"Smoothing and differentiation of data by simplified least squares procedures.Analytical chemistry, 36(8):1627–1639, 1964","venue":null,"work_id":null,"year":1964},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.661417Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:9b50531832145a7d073db57539a6794d80056cff5b214f058ab10ba81562178d","observation_id":"0a1b4bbe-7d63-4f5c-a6e5-33716d5319a8","resolution":{"observed_at":"2026-08-03T22:36:33.661417Z","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":"2026-08-03T22:36:33.665996Z","title":"Identifying fixations and saccades in eye-tracking protocols","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.665996Z"},"links":{"citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:d8513bf30e2ad4ce62dfe1720959893c854381770575b65b7a7ba2d353cfb86a","observation_id":"8f083c65-9033-475d-b753-805b5d1c0f5c","resolution":{"observed_at":"2026-08-03T22:36:33.665996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-03T22:36:33.670187Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.670187Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:325a6489fb2ade8c464c152cbcaebe2ae66559e02b9792fded4a59097e0efe5e","observation_id":"6d13e648-c7c3-4622-8519-87c0f1acbb7d","resolution":{"observed_at":"2026-08-03T22:36:33.670187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02083","last_updated":"2019-12-04T16:22:56Z","snapshot_observed_at":"2026-08-05T00:50:08.116091Z","submitted_at":"2019-12-04T16:22:56Z","title":"Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02083","snapshot_observed_at":"2026-08-03T22:36:33.675765Z","title":"Evaluating the data quality of eye tracking signals from a virtual reality system: Case study using smi’s eye-tracking htc vive.arXiv preprint arXiv:1912.02083, 2019","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T22:36:33.675765Z"},"links":{"cited_paper":"/paper/1912.02083","citing_paper":"/paper/2511.09867"},"observation_digest":"sha256:8d04dac15a3b709aac1f06a2ab96300cb68f456b58a0a66bd71a316212e0be2d","observation_id":"71111d4a-1c71-4d07-81d5-32684c8a4e7d","resolution":{"observed_at":"2026-08-03T22:36:33.675765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.09867","last_updated":"2026-07-23T15:43:38Z","latest_version":2,"primary_category":"cs.HC","snapshot_observed_at":"2026-08-03T22:36:32.609682Z","submitted_at":"2025-11-13T01:59:01Z","title":"Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":51,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":51},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2511.09867."}