{"as_of":"2026-08-15T14:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fcb37360756110cdb7b865840b9fc11bd8cc02738599c40f16f087c777f2077c","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:18:59.938040Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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":"2026-08-06T20:12:08.314047Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T20:12:10.177960Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"cited_work":{"arxiv_id":"2506.00107","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.00107","snapshot_observed_at":"2026-08-06T20:12:10.177960Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","venue":"cs.IR","work_id":"71b75898-6d10-4019-a1dc-50d44a483ca9","year":2025},"citing_paper":{"arxiv_id":"2507.03559","last_updated":"2025-07-04T13:11:50Z","snapshot_observed_at":"2026-08-07T23:53:43.051542Z","submitted_at":"2025-07-04T13:11:50Z","title":"Predicting Asphalt Pavement Friction Using Texture-Based Image Indicator","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:12:08.314047Z"},"links":{"cited_paper":"/paper/2506.00107","citing_paper":"/paper/2507.03559"},"observation_digest":"sha256:1726d3c348ea8fb88551b1d2d730cabbf4be3937ccf06204e2667a6d5c43867a","observation_id":"7d8c9464-f881-4be8-ab54-93dd130cb6f6","resolution":{"observed_at":"2026-08-06T20:12:10.330681Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.00107/citation-record","integrity":"/paper/2506.00107/integrity","json":"/paper/2506.00107/citation-record.json","paper":"/paper/2506.00107"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.962704Z","title":"Jy61 imu sensor external validity: A framework for advanced pe- dometer algorithm personalisation","venue":null,"work_id":"8611dd9b-b6a9-47c9-985e-60fc23fb8f1c","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:56.820080Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:cc3120ba542ef06b5efe980c7961cc5a54be420e9c2b5faafcb322fc4f775ae0","observation_id":"ff958bee-5fc2-44de-b18d-141c20406855","resolution":{"observed_at":"2026-08-07T12:19:06.057023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20733","last_updated":"2024-12-30T06:14:48Z","snapshot_observed_at":"2026-08-12T18:24:54.830151Z","submitted_at":"2024-12-30T06:14:48Z","title":"Towards nation-wide analytical healthcare infrastructures: A privacy-preserving augmented knee rehabilitation case study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.20733","snapshot_observed_at":"2026-08-07T12:18:56.972312Z","title":"Towards nation-wide analytical healthcare infrastructures: A privacy-preserving aug- mented knee rehabilitation case study","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:56.972312Z"},"links":{"cited_paper":"/paper/2412.20733","citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:e71fb73067cb0431702ca5a34646cdf5059fa50b1cf609e4b6fae54f88b4bf60","observation_id":"e068d4f3-831e-409a-9641-9c8c9d6fd351","resolution":{"observed_at":"2026-08-07T12:18:56.972312Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.771105Z","title":"A gan-based method to tune lstm hyperparameters for ﬁnancial forecasting","venue":null,"work_id":"2407b8f5-e618-484e-b8be-bd1232d6d37d","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.084540Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:17a5588083340f90d99e1b5613b757b30e4c1e8ad64a9c818b1cf4e4319c6978","observation_id":"dd96d558-889f-4f43-a6b2-698da102dd7f","resolution":{"observed_at":"2026-08-07T12:19:05.856739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.599306Z","title":null,"venue":null,"work_id":"a625fbdf-3aab-44e5-a034-8710b5b04842","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.208784Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:c9da2a28dca41ffc4071b948382d7c914224f7ba2d14c323f8483980fb5ac9ee","observation_id":"d97f4db8-ac2c-4808-90c8-c54d1adb28ed","resolution":{"observed_at":"2026-08-07T12:19:05.690392Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.436512Z","title":"Vbpr: visual bayesian personalized ranking from implicit feedback","venue":null,"work_id":"35d84762-e58c-4ed2-9650-257e6b018919","year":2016},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.294835Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:fedb540e05ae3584575965136a86750f4f83093224382769adc0bf1b3ddfde0a","observation_id":"8d0762e0-6ff4-48b4-871a-b0ea1c6ac024","resolution":{"observed_at":"2026-08-07T12:19:05.520128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.245358Z","title":"Lightgcn: Simplifying and powering graph convolution network for recommen- dation","venue":null,"work_id":"f3bda89b-79bf-4693-b845-1aaac7f0f4ad","year":2020},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.376326Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:8133ad3912a3b5cbe61caccb1dd1049adc6908e4d4520a948c8fec314e1a7bcd","observation_id":"7d9a0864-0c1f-496b-920e-c3a5998cee75","resolution":{"observed_at":"2026-08-07T12:19:05.339812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:05.054518Z","title":"The impact of apple’s digital design on its success: An analysis of interaction and interface de- sign","venue":null,"work_id":"dccc0c85-9775-49b5-8d70-f5f8417f6594","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.460681Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:c5a358eb8234e463e8b44efd28a46bd75f9cd344c7e0e4be2ab29ab7d700b6a9","observation_id":"d25a4438-4ce3-4940-91ef-05bf9df22fe7","resolution":{"observed_at":"2026-08-07T12:19:05.138820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:04.842490Z","title":"Transforming logistics with innovative inte r- action design and digital ux solutions","venue":null,"work_id":"e88d9c57-5fcd-4bc1-b6f0-476cea4b6f85","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.571901Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:94e1558b916ce925587dc3722f2c5350ca07f6adcaa9737ebd33eeab330c0047","observation_id":"4ef61678-7fee-4c42-9805-e9b7eb969abb","resolution":{"observed_at":"2026-08-07T12:19:04.957204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:04.670761Z","title":"A con- trastive deep learning approach to cryptocurrency portfo- lio with us treasuries","venue":null,"work_id":"d26100a8-189d-48b6-9560-678b08aeb6f0","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.643742Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:e07b35aa7daa865c60deb5d72cf86aa680b4997c4008289a48671e821ad3a41e","observation_id":"90724e31-703e-4c41-bb5d-86cfbbd6ddbe","resolution":{"observed_at":"2026-08-07T12:19:04.748091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:04.491927Z","title":"Knowledge graph embedding and few-shot relational learning meth- ods for digital assets in usa","venue":null,"work_id":"66c0a677-3bbe-47ec-aef2-ac9500544cf4","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.725097Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:cfa538e5d780edf4d856940cff98a371d918d6e7d48f3f485c5cc6094da375a5","observation_id":"0335670e-bf85-41ab-85f7-7f6a5d037c2d","resolution":{"observed_at":"2026-08-07T12:19:04.575236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.19369","last_updated":"2025-05-25T23:39:34Z","snapshot_observed_at":"2026-08-09T01:23:53.519445Z","submitted_at":"2025-05-25T23:39:34Z","title":"SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.19369","snapshot_observed_at":"2026-08-07T12:18:57.827330Z","title":"Setransformer: A hybrid attention-based ar- chitecture for robust human activity recognition","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.827330Z"},"links":{"cited_paper":"/paper/2505.19369","citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:644e037f5b77180d69c5eb33fc9f25d9bd9d9fed0a2d4f50d96f196ae938cd2b","observation_id":"db3ea585-4125-4123-81d4-e45c372faed9","resolution":{"observed_at":"2026-08-07T12:18:57.827330Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:04.271449Z","title":"Cot: an efﬁcient and accurate method for detecting marker genes among many subtypes","venue":null,"work_id":"32ca956a-a82a-4eb3-a7be-66f8e54d7910","year":2022},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:57.918634Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:8725ec4b2426362fe2db7a0c2036138c0e0aebe69c90e7a5f03bcb4f7eb65a1c","observation_id":"4d4ebc2e-617f-4145-acc9-f964ad19741d","resolution":{"observed_at":"2026-08-07T12:19:04.381234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:04.070723Z","title":"Knowledge graph for query enrichment in retrieval augmented generation in domain speciﬁc ap- plication","venue":null,"work_id":"12393463-fcdf-40ae-a8df-9f1c5d995440","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.039342Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:043875eba8ae895789ab68a673c2f6099ffcfa12ad2d257245dfbd27062b8d85","observation_id":"5bc90592-e970-4138-af4f-0afc54a9f3d0","resolution":{"observed_at":"2026-08-07T12:19:04.144017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:03.856360Z","title":"Convex optimization of markov decision pro- cesses based on z transform: A theoretical framework for two-space decomposition and linear programming recon- struction","venue":null,"work_id":"a618629e-5357-4c5f-b927-9c18c83c79d8","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.118148Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:1532ee446823f175fdf859c4aa534d1064fee241b497cd1393416358f1de6d9d","observation_id":"8abdb679-d660-4d4e-8ef7-97f534f7bb19","resolution":{"observed_at":"2026-08-07T12:19:03.954364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1205.2618","last_updated":"2012-05-09T18:25:09Z","snapshot_observed_at":"2026-08-15T03:23:25.220336Z","submitted_at":"2012-05-09T18:25:09Z","title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1205.2618","snapshot_observed_at":"2026-08-07T12:18:58.225259Z","title":"Bpr: Bayesian person- alized ranking from implicit feedback","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.225259Z"},"links":{"cited_paper":"/paper/1205.2618","citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:2639b1c8ea90da2ababf4c1da11060f2d477cab59648264f62fd9c195b5ea6dd","observation_id":"a8f24ceb-40cf-457a-ab56-58d649bc9e29","resolution":{"observed_at":"2026-08-07T12:18:58.225259Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:03.712538Z","title":"Evaluat- ing supervised learning models for fraud detection: A comparative study of classical and deep architectures on imbalanced transaction data, 2025","venue":null,"work_id":"0da85d51-be73-4f79-a9df-f6795e76690c","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.304888Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:21166ed7bd46875d67e792896003f144269940ce179008e175f9ff58f7938f2e","observation_id":"86fc0150-d900-4412-9ce3-96eeb4ffd79d","resolution":{"observed_at":"2026-08-07T12:19:03.781133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.08111","last_updated":"2025-04-08T08:53:57Z","snapshot_observed_at":"2026-08-13T17:46:01.612948Z","submitted_at":"2025-03-11T07:23:11Z","title":"MaRI: Material Retrieval Integration across Domains","version":3},"cited_work":{"arxiv_id":"2503.08111","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.08111","snapshot_observed_at":"2026-08-07T12:19:00.188020Z","title":"MaRI: Material Retrieval Integration across Domains","venue":"cs.CV","work_id":"e2eb4659-f73f-4a1b-b1cb-0cfb63b8f705","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.414533Z"},"links":{"cited_paper":"/paper/2503.08111","citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:d3f4e70710a275218ba0b5a45481dac01641ed3fb5f0c5ffda5fd1bc411d4f29","observation_id":"a397435d-26d0-4ad8-a4bf-405d0d1cea1c","resolution":{"observed_at":"2026-08-07T12:19:00.267414Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:03.568569Z","title":"Dualgnn: Dual graph neural network for multimedia recommendation","venue":null,"work_id":"2d682720-c9bd-485c-b998-63ad7a09768e","year":2021},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.482161Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:44ee63a12fe246529d2bc436f555daf13a6985d31d404e01706f3e073ebd833c","observation_id":"57579919-0c3f-4e2f-b1ca-16ffdcc5b7e3","resolution":{"observed_at":"2026-08-07T12:19:03.603598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:03.358288Z","title":"A sys- tematic review of machine learning applications in in- fectious disease prediction, diagnosis, and outbreak fore - casting","venue":null,"work_id":"a312293b-20fe-4972-9ed0-2508505fd9c2","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.548735Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:0da447d4e85e65bb7a7c30b443ca1630aafbda98806000b5bd4dfaf92b3b49a5","observation_id":"5126d9c0-19a9-4475-b4c2-1d8a2bf03a48","resolution":{"observed_at":"2026-08-07T12:19:03.456054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:03.128078Z","title":"Study of artiﬁcial intelligence for visual defect inspection in industrial products","venue":null,"work_id":"df4c342a-f5a2-4089-97ae-0582795a6f9b","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.672627Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:1e7e7c1b6a600c4887809190aa0a80d85cdd6e6169a3ee7f1a6a6a1f1ec083c8","observation_id":"68ac92ae-1a17-45f9-87f0-41798859f23c","resolution":{"observed_at":"2026-08-07T12:19:03.235076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.970249Z","title":"Mmgcn: Multi- modal graph convolution network for personalized rec- ommendation of micro-video","venue":null,"work_id":"4b74d471-4f22-4617-8977-0ebaba12f1dd","year":2019},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.768387Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:5601359f9534f54fa0641704191fca3cf810b014977f21c22fab06fc0544f443","observation_id":"d3574816-26a4-458a-a88e-fa4e7d1039ef","resolution":{"observed_at":"2026-08-07T12:19:03.060444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.777404Z","title":"Warehouse robot task scheduling based on reinforcement learning to maximize operational efﬁciency","venue":null,"work_id":"4c90e0b4-78c9-4de5-936a-ea7249f3e092","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.850383Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:94121afeb211721cd95b815051330e50f6385d5ded08c0be797d7d1be362bbe7","observation_id":"79d5a4d1-8d51-4d80-912f-33240b6f7fe1","resolution":{"observed_at":"2026-08-07T12:19:02.889016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.608084Z","title":"Advancing unsupervised graph anomaly detection: A multi-level contrastive learning framework to mitigate local consistency decep- tion","venue":null,"work_id":"f67614de-42ca-4c0e-9058-fb75fc4a67bd","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:58.956196Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:c835ad3114208ac44fe6c86d7621e14eaf5eb9ec268f6c15fb1c98d9b831b273","observation_id":"1eb45a70-4a31-4377-ac03-2148cbe73eb6","resolution":{"observed_at":"2026-08-07T12:19:02.681959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.428196Z","title":"Multisage: Empowering gcn with contextualized multi- embeddings on web-scale multipartite networks","venue":null,"work_id":"e630017d-0dce-407b-9cbc-d505dc28b915","year":2020},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.035510Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:1240a740711a74a73039cbc1e172f9a8e86500c48e234e2b7a84a345d2372cda","observation_id":"f9784b28-5328-410a-9d64-63f611940ec0","resolution":{"observed_at":"2026-08-07T12:19:02.509809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.259920Z","title":"Interpretable credit default prediction with ensem- ble learning and shap, 2025","venue":null,"work_id":"d2f05833-3b71-485e-b930-ac7a64191571","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.155457Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:7f0f119c27838d1ed1f9cb8da1d1b32520a8c2655abcdf4f6c24e45f920b526c","observation_id":"44c562f1-fd14-4ee7-b7b1-11e1c85c9eac","resolution":{"observed_at":"2026-08-07T12:19:02.336641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:02.028059Z","title":"Machine learning optimizes the ef- ﬁciency of picking and packing in automated warehouse robot systems","venue":null,"work_id":"e5577db6-0852-4dfd-b68c-a02111aaa9e3","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.242860Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:a91be1099426948a33c50588773fa56b6e23f7fddcc683b15d8d37c5e9640e4a","observation_id":"b1cb6b53-ce77-4acc-ad1c-f7d9ea10acb2","resolution":{"observed_at":"2026-08-07T12:19:02.132793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:01.796201Z","title":"Multi-scale video super-resolution transformer with polynomial approximation","venue":null,"work_id":"49c3385b-787c-4a8a-9edb-d574cda26ae2","year":2023},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.345286Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:cc04ace7e5e9c10a7fb0f438b22621a003e6b4fd11c96c87b54160fc36d9709b","observation_id":"cff97373-656f-441b-8c36-d68621f104c3","resolution":{"observed_at":"2026-08-07T12:19:01.902780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:01.613543Z","title":"Optimization and application of cloud-based deep learning architecture for multi-source data prediction, 2024","venue":null,"work_id":"c4e4995c-b7db-4ae0-a6ba-aafbab03981e","year":2024},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.461648Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:af7d89ad7ad85b466cd000be6a593be1ec84962b983f0a9ee2cace6cc5405278","observation_id":"a4aba747-2ee9-45b2-b041-2d33d25d95bd","resolution":{"observed_at":"2026-08-07T12:19:01.678908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:01.361832Z","title":"The role of machine learning in reducing healthcare costs: The impact of medication adherence and preventive care on hospital- ization expenses, 2025","venue":null,"work_id":"04be5249-bdf9-4737-bbc8-7b7ef5551047","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.578261Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:ebad52157e4fa59f2b52803aee8d2ca7177591c97e4cc3aef069a877a89ea96a","observation_id":"d78ef1c9-b44a-4dfb-9ec1-0fd85a75a5eb","resolution":{"observed_at":"2026-08-07T12:19:01.473614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:01.143886Z","title":"Contex- tual bandits for unbounded context distributions, 2025","venue":null,"work_id":"49117095-393d-485d-8704-fbd0e229be95","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.685849Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:653d94886f4298576c816ae8e8af8561da80c82562d1d3545402464e9e6021f6","observation_id":"b6cbf592-78a7-4c98-88e7-466971986f77","resolution":{"observed_at":"2026-08-07T12:19:01.229123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:00.892737Z","title":"Mentor: Multi-level self-supervised learning for multimodal recommendation","venue":null,"work_id":"976546cf-0a7c-4561-9b22-ed10385bb0ee","year":2023},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.775879Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:edf0ac62a3bb750f6ff4e888a754d429c0602ff706995a9dcf99f0f709a97354","observation_id":"a6627a86-ac1d-44f5-91ad-8772570799be","resolution":{"observed_at":"2026-08-07T12:19:01.005335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:00.725651Z","title":"Enhancing thyroid disease prediction using machine learning: A compar- ative study of ensemble models and class balancing tech- niques","venue":null,"work_id":"bbce2f1b-f43b-46ea-a4ae-03d176a69c7d","year":2025},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.876735Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:7ab5f2f9b6288ecb58dd07c761f7acc57f9e840dd2886e8e5e65a2466758c72f","observation_id":"6c65f47b-4d59-486b-b130-c40ec2458e27","resolution":{"observed_at":"2026-08-07T12:19:00.800816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:19:00.470255Z","title":"Layer-reﬁned graph convolutional networks for recom- mendation","venue":null,"work_id":"375f2633-7784-40c7-94e8-7748bee7d570","year":2023},"citing_paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:59.938040Z"},"links":{"citing_paper":"/paper/2506.00107"},"observation_digest":"sha256:e7096612ce368c132d8dacf947d3575b0c7fae7d6a25c48c6084e7e44a5c289b","observation_id":"1357fb10-9b84-4a86-b9c2-323c0e88712c","resolution":{"observed_at":"2026-08-07T12:19:00.582985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.00107","last_updated":"2025-05-30T16:57:17Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-09T16:51:33.416404Z","submitted_at":"2025-05-30T16:57:17Z","title":"Gated Multimodal Graph Learning for Personalized Recommendation"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":33},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2506.00107."}