{"as_of":"2026-08-10T23:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cf4971e7e65a375808b6b3620b6f8460c920d0ddaf7db57d9bb99bd126d5f13e","coverage":[{"denominator":82,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":82,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T14:20:21.472989Z","state":"measured"},{"denominator":85,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":85,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T19:10:42.461337Z","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":"2026-07-03T04:37:37.134195Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"cited_work":{"arxiv_id":"2604.13453","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.13453","snapshot_observed_at":"2026-07-03T04:37:37.134195Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","venue":"cs.LG","work_id":"b9bfe463-9fec-4eca-b35b-d57566c4300b","year":2026},"citing_paper":{"arxiv_id":"2604.23902","last_updated":"2026-04-26T22:26:20Z","snapshot_observed_at":"2026-07-06T23:10:02.659384Z","submitted_at":"2026-04-26T22:26:20Z","title":"LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-08T05:59:22.177960Z"},"links":{"cited_paper":"/paper/2604.13453","citing_paper":"/paper/2604.23902"},"observation_digest":"sha256:5964a6dd5536041ac930182b88d9c6c4e0357cd797d795220bcd261396b3d7b7","observation_id":"aac75274-646a-43a9-8289-e57eed2cee30","resolution":{"observed_at":"2026-05-11T21:16:36.145050Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"cited_work":{"arxiv_id":"2604.13453","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.13453","snapshot_observed_at":"2026-07-03T04:37:37.134195Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","venue":"cs.LG","work_id":"b9bfe463-9fec-4eca-b35b-d57566c4300b","year":2026},"citing_paper":{"arxiv_id":"2606.00506","last_updated":"2026-05-30T03:39:15Z","snapshot_observed_at":"2026-08-05T10:30:25.165867Z","submitted_at":"2026-05-30T03:39:15Z","title":"EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T19:10:42.461337Z"},"links":{"cited_paper":"/paper/2604.13453","citing_paper":"/paper/2606.00506"},"observation_digest":"sha256:0e02e9e381ddeb8858cd1962eebdf8692e8258828189453681bbeda49f3fe984","observation_id":"584eae95-4d1b-46d1-a83f-0a3397482ae4","resolution":{"observed_at":"2026-06-28T19:12:34.546452Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"cited_work":{"arxiv_id":"2604.13453","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.13453","snapshot_observed_at":"2026-07-03T04:37:37.134195Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","venue":"cs.LG","work_id":"b9bfe463-9fec-4eca-b35b-d57566c4300b","year":2026},"citing_paper":{"arxiv_id":"2606.11257","last_updated":"2026-06-09T01:09:00Z","snapshot_observed_at":"2026-08-08T06:04:42.153459Z","submitted_at":"2026-06-09T01:09:00Z","title":"Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T13:46:46.866049Z"},"links":{"cited_paper":"/paper/2604.13453","citing_paper":"/paper/2606.11257"},"observation_digest":"sha256:154e3287feef1a454984eae5ab7ca90390279af6e52fdf43dcf9337d799f1cd8","observation_id":"09bc3db0-8462-4410-993e-2c9f2e1e58a9","resolution":{"observed_at":"2026-07-03T04:37:37.135826Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2604.13453/citation-record","integrity":"/paper/2604.13453/integrity","json":"/paper/2604.13453/citation-record.json","paper":"/paper/2604.13453"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bayesian critique-tune-based reinforcement learning with adaptive pressure for multi-intersection traffic signal control","venue":null,"work_id":"0f22822d-798b-4864-9c19-6368ddb5e96e","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:31313f35f721ecb37f583c3a9325c08ed1e2d3f2c2943539982c8b3dc0aef010","observation_id":"88067a12-89c1-4e75-912c-37b0bf634ba5","resolution":{"observed_at":"2026-05-18T14:42:43.135564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spatiotemporal multi-view continual dictionary learning with graph diffusion","venue":null,"work_id":"61b8aec2-568a-4753-9eea-03e5681badfd","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:32a3ba409c2fe5bae224bae1bf8c4b1a7d59ed2c480fe1be6b83960e6f3e2ec0","observation_id":"2b8b13eb-a3b4-41c3-86b4-1f8f95ed0585","resolution":{"observed_at":"2026-05-18T14:42:43.133473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-resolution context augmentation and dual channel attention for 3d lane detection","venue":null,"work_id":"1dce2f4b-1b4f-4815-9c7c-61ad4d991609","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:461ea3438cf6285bc688f046e52500085f563ecdfd04b2585740e55ac7e1c15a","observation_id":"c881dfa6-171d-4cb7-805e-254998034082","resolution":{"observed_at":"2026-05-18T14:42:43.126435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tur- boreg: Turboclique for robust and efficient point cloud registration","venue":null,"work_id":"cfd559b5-a6a2-4d46-bb8f-526c07dea0b6","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b7c128de4c6d084b7cbe5d3a93ead50faa290f5e712b93e97a6435c5307a5f56","observation_id":"03cc77ff-2117-488b-a7d4-63e7c4701a5d","resolution":{"observed_at":"2026-05-18T14:42:43.137486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spatiotemporal align- ment for remote sensing image recovery via terrain-aware diffusion","venue":null,"work_id":"ecd9b824-db96-4150-ac80-aa772f1f8535","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:811598a8d7b87b6c047f7dede2781b3715ce9050bbf77896b9ab82f273d6296a","observation_id":"d8c318bd-2261-4230-8725-adb2b40e0c63","resolution":{"observed_at":"2026-05-18T14:42:43.147383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Difflow3d: Toward robust uncertainty-aware scene flow estimation with iterative diffusion-based refinement","venue":null,"work_id":"9e0c0b95-ffa6-4e2a-b319-e8448c7bc00e","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f59439b3afe8e3ee5e4c3467a919f4d3c1352a26d2b9af00b3a06e5ac164653e","observation_id":"0cf54650-8be4-4007-9031-c48ac86eaf41","resolution":{"observed_at":"2026-05-18T14:42:43.117424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dvlo: Deep visual-lidar odometry with local-to-global feature fusion and bi- directional structure alignment","venue":null,"work_id":"03e97ae4-4603-462b-81cc-159e69797b11","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:7d8751dd8042394df54f96f582442d80743e8b69c988fa23b1668461cee93444","observation_id":"660311d9-8b62-44ae-a71c-d574ebddb4dc","resolution":{"observed_at":"2026-05-18T14:42:43.123595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Stg- avatar: Animatable human avatars via spacetime gaussian","venue":null,"work_id":"2a803af7-28bc-4a63-bb56-b588ce504d70","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f28be71abd9c7efff22c2ec2dd4c89dd33ab8713e216b880d43dcf08e201dabc","observation_id":"2dfbdbec-c5bd-4c2b-81e5-bb088c53919d","resolution":{"observed_at":"2026-05-18T14:42:43.114969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.00796","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T23:59:06.539151Z","title":"Adagar: Adaptive gabor representation for dynamic scene reconstruction.arXiv preprint arXiv:2601.00796","venue":null,"work_id":"26e8e8ca-775c-4efb-a665-43b1287838a7","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:fd989c4ff834b2b413dc20dc45374d0ece91f7d16ac3f151e3913a22ef5b7af6","observation_id":"bae4a027-b9e9-43e3-9d80-c21f3ede6ae1","resolution":{"observed_at":"2026-05-10T14:20:29.485652Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2412.17629","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learn from global correlations: Enhancing evolutionary algorithm via spectral gnn.arXiv preprint arXiv:2412.17629","venue":null,"work_id":"f35eeb24-bf12-42fc-b662-bee18f032180","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:a34ccf7a67c6cbbdd630fa1114315e891bf77361f72e59a8c456a8c46542c89e","observation_id":"fe8be797-8c22-4bf6-9ddd-d0f786fa48c0","resolution":{"observed_at":"2026-05-10T14:20:29.469820Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A stable technical feature with gru-cnn-ga fusion","venue":null,"work_id":"23e0dcd5-22ec-483f-81c4-71ca19e531df","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:4cae5c724f9c5f706bac04ce7d1544ea2144135509ab30fe8a41387430f5f271","observation_id":"7e3dd087-ae28-4c58-adc3-557cc9d35ce1","resolution":{"observed_at":"2026-05-18T14:42:43.121002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Research and practice of advertisement recommendation algorithm based on graph neural network","venue":null,"work_id":"fc5dd8df-824b-4a9e-9950-6d35911f7607","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:d8ab0726fc5910f43752784862d7ee94453ddd5323f483e45225590ee193eb75","observation_id":"88f9592e-70ac-4129-bd01-d79a307e8331","resolution":{"observed_at":"2026-05-18T14:42:43.144735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient cold-start recommendation via bpe token-level embedding initialization with llm","venue":null,"work_id":"2f93794d-70a3-445d-9945-0908b5eb7a6e","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:19d418e45d5cba0a804c10cfcea49aa5ca801d109dcbef345da6f4a1e1d46cff","observation_id":"39e0024a-8213-4868-84bb-e2f9ac323075","resolution":{"observed_at":"2026-05-18T14:42:43.164748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Priordrive: Enhancing online hd mapping with unified vector priors","venue":null,"work_id":"96c5865d-829b-4638-ba96-595915bc5861","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:a9e362a8e3664850af4cbd835913020b232223bbfa219786d69b0e4c5b50b011","observation_id":"bc157984-8681-499f-be43-eb103e5f73f1","resolution":{"observed_at":"2026-05-18T14:42:43.084894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:6380527db72f4e330f37d6fbc839fafff625ccebf6d49f7a80776eed778d9ba7","observation_id":"b96b758b-4b95-45c5-accd-62a8083728cf","resolution":{"observed_at":"2026-05-10T14:20:29.474708Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Decentralized graph-based multi-agent reinforcement learning using reward machines","venue":null,"work_id":"2d9fac31-c538-49d8-adb9-8891d56c1b79","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:0cca471e40700b368480a71d5cb153713117aeaebc37e5605a37445b57126f35","observation_id":"2b3bf689-4a4f-435c-be2f-5ce800df66e6","resolution":{"observed_at":"2026-05-18T14:42:43.071543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.09241","last_updated":"2025-08-12T15:12:42Z","snapshot_observed_at":"2026-08-05T21:14:43.328891Z","submitted_at":"2025-08-12T15:12:42Z","title":"FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents","version":1},"cited_work":{"arxiv_id":"2508.09241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.09241","snapshot_observed_at":"2026-07-01T09:25:40.553161Z","title":"arXiv preprint","venue":null,"work_id":"f653cf4e-dd0f-4ea8-87b5-afcdf0891a5d","year":2019},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2508.09241","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:574ffc3a6a2a02ef3429cd0dd75a1d59455cae2c9a86908ac34c7667c7b71adf","observation_id":"14ed10a4-c256-4815-9e65-1271d8fa921b","resolution":{"observed_at":"2026-05-10T14:20:29.497042Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T10:24:51.303285Z","title":"Intent: Invariance and discrimination-aware noise mitigation for robust composed image retrieval","venue":null,"work_id":"7a591302-1a63-4044-bfc8-cd5672c687e7","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:23f278d5de701462e4bddcf7cb077b2a1863cca1212aa3d07e7ac0968c9a1518","observation_id":"ad6099d9-a095-417c-a3c6-16ede5f127e5","resolution":{"observed_at":"2026-05-18T14:42:43.095150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hud: Hierar- chical uncertainty-aware disambiguation network for composed video retrieval","venue":null,"work_id":"df658809-f9fc-47d3-aa78-1025816aee91","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f7c520cf1b55ea9b8b44e5d71234117f73c1b1ad9060d83277c3349e283682f4","observation_id":"91960a04-bbb1-49d4-9ca3-b458c020e66a","resolution":{"observed_at":"2026-05-18T14:42:43.104455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Refine: Composed video retrieval via shared and differential semantics enhance- ment","venue":null,"work_id":"86983393-60b1-47b5-a961-db9e8dcd0347","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:879494f5e57be034b54acd5dabba8d2a45eb1aa6feedfe92ba424502e6368083","observation_id":"14764666-a23e-4c33-aadd-da69f69f9eff","resolution":{"observed_at":"2026-05-18T14:42:43.106519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tri- subspaces disentanglement for multimodal sentiment analysis","venue":null,"work_id":"dc8e81aa-001a-46c3-86ac-a9f157a0b3e2","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:be123151b2bd8ab43f4ff15a678d2c290a53b27efb9b4d39ab186e9af6ba6334","observation_id":"aa53be21-4c3c-4769-8882-67ec8652fe6e","resolution":{"observed_at":"2026-05-18T14:42:43.160293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cotextor: Training- free modular multilingual text editing via layered disentanglement and depth-aware fusion","venue":null,"work_id":"ea9d081b-8441-4ac3-88d5-1190e7866733","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f8b3a1d7984a358f69f73f69603dda8622271ef3a93c01454ca256a8b97bad3f","observation_id":"cfc44d55-1c65-4aa6-85bf-415f2c5975c3","resolution":{"observed_at":"2026-05-18T14:42:43.149945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DynamicNER: A dynamic, multilingual, and fine-grained dataset for LLM-based named entity recognition","venue":null,"work_id":"c819dedc-bc5a-4ca7-a085-a7d0693353dc","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:03515d26696702c576c4428c3863c26824e35f2bbce9e851dfc24ac2b09914a3","observation_id":"61c9e3cf-d717-4f03-be74-191f18df0cd2","resolution":{"observed_at":"2026-05-18T14:42:43.128550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.1152","doi":"10.20944/preprints202603.1152.v1","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Codes: A context-efficient framework for enhancing small language models via domain-specific adaptation and model ensembling","venue":"Preprints.org","work_id":"091ea5ac-3bb6-4fc9-b1b7-84ff327882ce","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:ccf7d1fabb71554a97987718ca5eb4beefa8166b2498154690583b249e202068","observation_id":"1fb18fd6-ca75-48b0-abbb-7b72eaf80286","resolution":{"observed_at":"2026-05-10T14:20:28.642573Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-07-11T16:49:35.126366+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T16:49:35.126366+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-06-05T21:23:00.469572Z","title":"Parameter-efficient and student-friendly knowledge distillation","venue":null,"work_id":"ede1fc49-0a42-4a19-8874-3ef80ff36581","year":2023},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:1c2f4c9da921e8ac1bc37b32c030a1082255acf5d779658712ea3e2a764805b0","observation_id":"1c3ec4ab-64c8-42c0-a1e4-22d2d2ff1218","resolution":{"observed_at":"2026-05-18T14:42:43.130511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21203/rs.3.rs-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T14:31:09.322172Z","title":"The Accessibility and Inaccessibility of Urban Public Charging Stations","venue":null,"work_id":"b1a1e72b-069c-4f6c-ba11-c1c3527be63f","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:a71196cc1a09804b831382246d5e354d4ed09b38c5a61d9fb0f468a1f2afc60e","observation_id":"caac9975-1e7b-4180-a596-b0ceb5f93188","resolution":{"observed_at":"2026-05-10T14:20:28.635153Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reg- former: an efficient projection-aware transformer network for large-scale point cloud registration","venue":null,"work_id":"ddcdc19f-0855-44b4-a856-975bf43b89e5","year":2023},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:3821fe809b6cd816d02f152f72efd2370fa12c2d88c19596d327c444d91c201e","observation_id":"bd50ec8f-794e-425d-af0d-7bd70293dea7","resolution":{"observed_at":"2026-05-18T14:42:43.139659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2512.02924","last_updated":"2026-07-20T02:01:50Z","snapshot_observed_at":"2026-08-06T15:38:52.220151Z","submitted_at":"2025-12-02T16:45:25Z","title":"AutoNeural: Co-Designing Vision-Language Models for NPU Inference","version":3},"cited_work":{"arxiv_id":"2512.02924","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2512.02924","snapshot_observed_at":"2026-07-21T02:20:32.885266Z","title":"AutoNeural: Co-Designing Vision-Language Models for NPU Inference","venue":null,"work_id":"f4cd1c86-d38c-4cac-a1a4-502fbdcd8103","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2512.02924","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:d39097f41e421c70944719a0a3beeb522aa4586a7b7d25e5173cd25dd6e7d7c1","observation_id":"842d82f3-b450-4671-aea3-64053c5a86f1","resolution":{"observed_at":"2026-07-21T02:20:32.885266Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"REA-RL: Reflection-aware online reinforcement learning for efficient reasoning","venue":null,"work_id":"ada156f7-34f9-4471-aabb-4e7d111d4109","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b937f17323543f3011b23140d265b787e10a8ed3c26859e203788d90e6de55e7","observation_id":"64a68c60-cefe-4914-bca4-99c61a38a28d","resolution":{"observed_at":"2026-05-18T14:42:43.152663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.19320","last_updated":"2026-05-20T06:06:23Z","snapshot_observed_at":"2026-07-06T22:46:40.118923Z","submitted_at":"2026-02-22T19:50:01Z","title":"Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations","version":2},"cited_work":{"arxiv_id":"2602.19320","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.19320","snapshot_observed_at":"2026-07-03T04:47:38.778462Z","title":"Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations","venue":"cs.CL","work_id":"f37a7758-0a40-478c-a725-53c57e1b9b75","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2602.19320","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:ce6e34185994f968c96a1da5889c073e31faa0d3cd9d7a98584807589cc24292","observation_id":"b86aeade-4266-4805-a3fb-50f9402c8bcc","resolution":{"observed_at":"2026-05-21T02:04:11.502809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient partitioning vision transformer on edge devices for distributed inference","venue":null,"work_id":"81bd977b-c8bf-4448-9976-c18380061100","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b53f6aa8428dc8f742535e473978955b089072a01e2e5362fb417e2d9d881705","observation_id":"b45e23e3-5123-4458-8d83-a764179b0d49","resolution":{"observed_at":"2026-05-18T14:42:43.142066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fedlpa: One- shot federated learning with layer-wise posterior aggregation","venue":null,"work_id":"d1098525-fe27-4fe2-b2b0-991e96f221f9","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:c5cd547383b701746eb037fce20280ee94122b3785bdebaee0ae8a3598e17bc9","observation_id":"ad1d0e61-bf37-44f8-b6dd-2a8f13ebe526","resolution":{"observed_at":"2026-05-18T14:42:43.169523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09104","last_updated":"2025-02-13T09:26:44Z","snapshot_observed_at":"2026-08-08T06:42:46.503708Z","submitted_at":"2025-02-13T09:26:44Z","title":"One-shot Federated Learning Methods: A Practical Guide","version":1},"cited_work":{"arxiv_id":"2502.09104","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.09104","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"One-shot federated learning methods: A practical guide","venue":null,"work_id":"33dd5fc3-7e2c-4b04-840f-1c1dcab76abd","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2502.09104","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:992ea85b9e130b3f5833abfcd4ad25cd38fd03ceb4c0af1c6835c16a64822098","observation_id":"86343c57-7e41-418c-9ceb-f5538e11fea9","resolution":{"observed_at":"2026-05-10T14:20:29.465694Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fastpillars: A deployment-friendly pillar-based 3d detector","venue":null,"work_id":"2889a6b1-e7a6-49b4-a8c0-d2b648d4c194","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b7c5e014dea6644961724db4888381fa659bb45703e8d23071b3949dc211e302","observation_id":"1d4a0c21-acbb-4e6b-ab37-616da83e2110","resolution":{"observed_at":"2026-05-18T14:42:43.082214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GSQ- tuning: Group-shared exponents integer in fully quantized training for LLMs on-device fine-tuning","venue":null,"work_id":"5b0b9efb-d7d1-4cda-94e0-419487e35c59","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:ec7c798f1cd90942c3244323a31d9f9cd56d0b5ab6e544db3db4aa285bccf9cc","observation_id":"aa611084-d7b2-4555-bd79-41c11e5aacdd","resolution":{"observed_at":"2026-05-18T14:42:43.108472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Yolov8- dds: A lightweight model based on pruning and distillation for early detection of root mold in barley seedling","venue":null,"work_id":"8b755063-2c64-48ac-ab92-27973b0fe00f","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:60abcc289577fb2fae3ce18f5362ea5951e97f14792a8854ef28bdfeff68a1e8","observation_id":"0b2ea701-104d-47e4-8dab-674a58a51ce0","resolution":{"observed_at":"2026-05-18T14:42:43.075081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Filter-and-refine: A MLLM based cascade system for industrial-scale video content moderation","venue":null,"work_id":"2c27d19e-34b9-4b29-b9b3-0e4a895d921b","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:a86d0f287b5973f9ee76daf3c081d1d55d922e1697d30413d79c71c61d9686c4","observation_id":"36e9fe23-f0d4-40c3-ae88-eee028ff8f5d","resolution":{"observed_at":"2026-05-18T14:42:43.065717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.02192","last_updated":"2026-05-26T02:43:34Z","snapshot_observed_at":"2026-08-09T04:10:49.998392Z","submitted_at":"2026-02-02T14:57:53Z","title":"ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2602.02192","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.02192","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Echo-2: A large scale distributed rollout framework for cost-efficient reinforcement learning","venue":null,"work_id":"cdba8fbd-d320-4a1d-82d6-ee8489858cb5","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2602.02192","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:8d7ec3f1b20f56df017da085173f1819d28a253845c53071a8e8087f1d6cb72f","observation_id":"f336f62a-0335-4939-a84d-3dd93dec191b","resolution":{"observed_at":"2026-05-27T02:04:33.896984Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.11554","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An efficient solution method for solving convex separable quadratic optimization problems","venue":null,"work_id":"a6910e51-0de0-4bb1-a1ef-72c6071c231a","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:2e2c6a923c1e7ad0ad676992f64bd2c4039006563e8a296b0995508695831b62","observation_id":"366ec035-2d42-48bf-b370-35f3e6ff350c","resolution":{"observed_at":"2026-05-10T14:20:29.507919Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A semidefinite relaxation based global algorithm for two-level graph partition problem","venue":null,"work_id":"ef849664-5c7e-4206-a0c0-1b39f18e722f","year":2023},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f51932239dcabff60265d59dd774aa3f05994a413c90b3ed0e26064a9c8730f5","observation_id":"a1f71477-c989-44dd-9836-3fca8c566e47","resolution":{"observed_at":"2026-05-18T14:42:43.062934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Comptrack: Information bottleneck-guided low-rank dynamic token compression for point cloud tracking","venue":null,"work_id":"2496147c-003e-46fb-bb24-cf8e1d06518e","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:59ea118e4ab7e69a1b6f1ea8a0768643dd6279f28b8deaf8b178cc52b9f979bb","observation_id":"7a86b7bc-dcc2-4d86-9825-20b437821a2c","resolution":{"observed_at":"2026-05-18T14:42:43.068511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.6321958","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regime-dependent volatility dynamics: Evidence from time-series analysis","venue":"SSRN Electronic Journal","work_id":"369f7006-3bfd-4a4e-8db5-0354d98a700f","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:ce94820e1ac1050524fd85560eae2089d2e75ae8e0f851a99eecc180c85edb2d","observation_id":"d938b5a7-05d0-42dc-92e5-3b11c0c51186","resolution":{"observed_at":"2026-05-10T14:20:28.627048Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.10788","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T21:08:58.534933Z","title":"& Wang, Z","venue":null,"work_id":"791419cb-7c57-4b79-83f0-2ffa39a73977","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:3ee72d47b5ee7df0cbdf84864c1d750eb9e6330a8531b1e99703618e5fcef54b","observation_id":"f38ff298-6024-4043-aa52-31bbdfa30613","resolution":{"observed_at":"2026-05-10T14:20:29.502459Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.16176","last_updated":"2026-04-17T06:58:39Z","snapshot_observed_at":"2026-08-01T14:56:36.642825Z","submitted_at":"2025-05-22T03:27:05Z","title":"Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning","version":2},"cited_work":{"arxiv_id":"2505.16176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.16176","snapshot_observed_at":"2026-07-04T08:59:42.991906Z","title":"Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning","venue":"cs.AI","work_id":"dce1aec1-aaba-4ce7-b717-929f9691ea96","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2505.16176","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f8aad85e4bd1b4b16424ebcc5709feef8f99575b6de357ba879d24bbafa611af","observation_id":"db3a6bf6-75af-4cce-b297-cff15944b162","resolution":{"observed_at":"2026-05-10T14:20:29.513034Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels","venue":null,"work_id":"92d5417f-5c74-4cf4-9bb8-cf119ef411db","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:f840e8d6a0998ef60706a4259bfabd8283140d14352f79fef14e19816a36f48c","observation_id":"78c2dfc2-62cf-4b97-aef2-801171f8000b","resolution":{"observed_at":"2026-05-18T14:42:43.079021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"PROSAC: Provably safe certification for machine learning models under adversarial attacks","venue":null,"work_id":"ad2d54b5-14b4-4f0f-9599-a0adb8cc63ed","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:82c0d126d62225f50da0c7ef8a501a92f5824f19a9a0146ae3ea09d598a93ca5","observation_id":"fb48fd7b-4526-416e-8e8a-11be3c99097d","resolution":{"observed_at":"2026-05-18T14:42:43.087356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Noisy but valid: Robust statistical evaluation of LLMs with imperfect judges","venue":null,"work_id":"444a569c-5f03-4db2-9b40-90cbd6357693","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:41cdf3e8cab218d27321c8610a304ac56bb305e9903baad4f2d8e674cb4cab6c","observation_id":"b1d7658d-df6f-4ba5-b1c2-411179be5bc3","resolution":{"observed_at":"2026-05-18T14:42:43.092761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic neural fortresses: An adaptive shield for model extraction defense","venue":null,"work_id":"639cbd38-b16b-4eb2-935c-293a40cef9a0","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:763a1bfd3b22b4d5f0ccf8933a63907de9271a88f041d45abf7ac411405c51ec","observation_id":"d43a9a17-d442-4ed2-9af9-c2efcc22a326","resolution":{"observed_at":"2026-05-18T14:42:43.110632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Agentauditor: Human-level safety and security evaluation for llm agents","venue":null,"work_id":"c76c12c5-37cf-45e0-8c4b-e5fd41765417","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:8bda1e30345e1874be4fd7bb4956f17d06fd6f407b509307c1b8d8948d23da19","observation_id":"69f74a2f-324d-433a-b290-7d9d73686a47","resolution":{"observed_at":"2026-05-18T14:42:43.060458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"From voice to safety: Language ai powered pilot-atc communication understanding for airport surface movement collision risk assessment","venue":null,"work_id":"e0cf8ca2-7ba2-4e15-8139-cd34f3a7bddc","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b16e497727d896568697684cb57e405445de1a4027f15efefbd5158d07d06cfe","observation_id":"14138235-9343-46bc-82e2-135de25075b1","resolution":{"observed_at":"2026-05-18T14:42:43.051139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning driven integrated detection and mitigation of uav gps spoofing attacks","venue":null,"work_id":"50271516-834e-4657-be19-5788e486aff5","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:876142c8024692fb73c2c332040a4a0fdea68ea48b7416b6ae93829aa9413b55","observation_id":"4dec599e-f1cb-46f4-86d9-1dbf059fbbb4","resolution":{"observed_at":"2026-05-18T14:42:43.049060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A fully data-driven approach for realistic traffic signal control using offline reinforcement learning","venue":null,"work_id":"0d6b8832-34a0-466a-962b-0024f954eac5","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:96e5a723de3065e7645441dddcfdbbfeb8c94148072cc076b51c0d8019d48649","observation_id":"11564863-9fca-410e-83a6-9248bce83895","resolution":{"observed_at":"2026-05-18T14:42:43.055954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ccma: A framework for cascading cooperative multi-agent in autonomous driving merging using large language models","venue":null,"work_id":"1311e843-d9fb-4f52-a972-45c386c9d053","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b64cc90c24b6d73d7da02641078e2ead883452b56ae2e90afb7e2c9fcced870c","observation_id":"88b2927a-de10-4997-928d-01627a75ca60","resolution":{"observed_at":"2026-05-18T14:42:43.042468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards cleaner heating production in rural areas: Identifying optimal regional renewable systems with a case in ningxia, china","venue":null,"work_id":"7b4f6c39-0baf-4d5c-a1fc-a4f0ccc00665","year":2021},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:aa5e8d37a01a343bdb2c293c8f566ad09acc6922652cab13394022536424655b","observation_id":"6d5a8678-099a-4c7b-9abb-51cad1e536b3","resolution":{"observed_at":"2026-05-18T14:42:43.044942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reasoning-enhanced domain-adaptive pretraining of mul- timodal large language models for short video content governance","venue":null,"work_id":"8e89801c-eabb-4822-b0dc-b888f0de94fd","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:d15de90678a694e3a00b320997210d16fd1778d4620d69acbfe16c976bff880c","observation_id":"6f18922e-ccc4-4666-a018-5f853b25136a","resolution":{"observed_at":"2026-05-18T14:42:43.047076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"When rules fall short: Agent-driven discovery of emerging content issues in short video platforms","venue":null,"work_id":"61d4adb8-30ac-4b0d-bba5-70612bfa77fe","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:3c60ce0e6018b2324d366b097ea74a31f79ee12d98ea00c6eafcdaea67654264","observation_id":"d7ee01fc-c2ff-490d-ac11-852fc5131ad4","resolution":{"observed_at":"2026-05-18T14:42:43.053683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.17685","last_updated":"2025-11-11T01:31:25Z","snapshot_observed_at":"2026-08-02T20:06:32.255780Z","submitted_at":"2025-05-23T09:55:32Z","title":"FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving","version":3},"cited_work":{"arxiv_id":"2505.17685","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.17685","snapshot_observed_at":"2026-07-08T05:34:32.413426Z","title":"FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving","venue":"cs.CV","work_id":"6ad88a41-f4d4-4df5-b676-2d30afb525ce","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2505.17685","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:83a577a8fa97db7796bf3c1d534534480eb1b1626d9b1431c0cc8a7856955a97","observation_id":"9f1b03ef-9fc7-4c66-a2d4-6f1fa30bd2a6","resolution":{"observed_at":"2026-05-15T19:19:43.280111Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.22548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T02:27:48.931160Z","title":"Janusvln: Decoupling semantics and spatiality with dual implicit memory for vision-language navigation","venue":null,"work_id":"5c872c6e-2d14-466d-8bf5-dcae48cf1d63","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:69140fdf9032428fbcc89793c8183b40b413651ce8b8107de1d9fa7b340947b1","observation_id":"7a8524df-b5ca-4bc4-a850-7e138f2af6d4","resolution":{"observed_at":"2026-05-10T14:20:29.480221Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Forecasting freeway traffic flow for intelligent transporta- tion systems application","venue":null,"work_id":"389689b4-25cc-4ed2-b933-ca1b741d05de","year":1995},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:238219853416e3c9858f7f9a1fec9d7362831efc163dcd27fb1036cac59594d4","observation_id":"a831e9dd-4446-4eec-9cbc-d339d7f9bd79","resolution":{"observed_at":"2026-05-18T14:42:43.097657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Short-term traffic flow prediction using seasonal ARIMA model with limited input data","venue":null,"work_id":"61e0ebb5-ddac-4d97-b95c-2ff86c2863b2","year":2015},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:dcc693a0ce84f365d45e67a3c1596fb7922c01df8b25d4d8e69a032e6a80f3a5","observation_id":"41a51b3e-714e-44da-a4f3-41d14be2881f","resolution":{"observed_at":"2026-05-18T14:42:43.100028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Predicting short-term traffic flow in urban based on multivariate linear regression model","venue":null,"work_id":"78588a09-8588-4d82-b0d8-899abbff1eb6","year":2020},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:908a10323235d0c40bca689e71d60433877c6e9de8ef63d03fa27499e4e6b447","observation_id":"9370203b-998b-4bf8-8c9a-97f27a81f4f6","resolution":{"observed_at":"2026-05-18T14:42:43.028974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Travel-time prediction with support vector regression","venue":null,"work_id":"6d126540-472c-4318-8cab-dd1b2ed7e607","year":2004},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:de79b60e52923e2d9241635b09f34881ce18cf3efdf05682c29f20b6cc47fd73","observation_id":"8477d2a5-47e5-4696-b678-936fd6090031","resolution":{"observed_at":"2026-05-18T14:42:43.031551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","venue":null,"work_id":"21b83dba-7e90-4f8a-a3d7-89339ab7a82c","year":2018},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:4211d316ea0eb720631d4465444b21a942bf5f368e812b2584b360fc93f4d7f5","observation_id":"985a9bfe-3a9d-442f-bd76-549d2be5c372","resolution":{"observed_at":"2026-05-18T14:42:43.026642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","venue":null,"work_id":"8c37b0de-5d4f-4e4b-bc6c-0a859a2f6a3c","year":2017},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:06b31b1df06863ffc2e5222b132500d39a8be00636261a0f5ab0d9c4117c29f4","observation_id":"957d31da-f5b2-4992-8b50-3ec3a3b36215","resolution":{"observed_at":"2026-05-18T14:42:43.024398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph wavenet for deep spatial-temporal graph modeling","venue":null,"work_id":"775e9504-b3ee-4561-8f10-b5de6902c51d","year":2019},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:13a59d31f12b4b9c6c15d0d1757f9b49dcf4a799237e5609978c8605b4b47a63","observation_id":"5e8a28e2-add1-47cf-8473-66c4c5713a06","resolution":{"observed_at":"2026-05-18T14:42:43.038468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spatial-temporal fusion graph neural networks for traffic flow forecasting","venue":null,"work_id":"7bb4260e-dd97-4932-84e2-fd728570ba04","year":2021},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:0edb3d09083971a555343321214fdda1adc8b5f5c9a51f36e6ed1af83d222511","observation_id":"30794a34-60f3-4ada-afcb-a579ecbb620f","resolution":{"observed_at":"2026-05-18T14:42:43.022325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adaptive graph convolutional recurrent network for traffic forecasting","venue":null,"work_id":"6ec17c06-9892-449a-8e90-c20b27515347","year":2020},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:972108d3aac9e3abdd369fe9d4d87cb32aed866466461f9f7662858a64b55c52","observation_id":"40f5f358-06a8-4578-90cc-c3d64dc4f3e1","resolution":{"observed_at":"2026-05-18T14:42:43.036113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Attention is all you need","venue":null,"work_id":"eabac93d-d1c7-422c-aad0-1c41707b4d5b","year":2017},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:401cf0663feb9c46816c9595b8e6c281362cc182d2bfbae3ba655d72dc4fe575","observation_id":"32e3f686-5626-4599-baf2-9152312c3628","resolution":{"observed_at":"2026-05-18T14:42:43.040436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gman: A graph multi-attention network for traffic prediction","venue":null,"work_id":"5518d00c-7bc3-434c-9639-0a3198d8c810","year":2020},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:377c2db0e3c25c67e21aa24d6cf068b80d4d66147fcb294acc912ecf8e6d6e05","observation_id":"93ca8d50-d582-4e0a-89eb-614aec6bade2","resolution":{"observed_at":"2026-05-18T14:42:43.020105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards spatio- temporal aware traffic time series forecasting","venue":null,"work_id":"7d17ee06-8e91-4ee4-b8e9-255cf27c2e37","year":2022},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:65da37515fd323b24296e4b003d0f0d932df0c1ff51456208fb463de459aabbd","observation_id":"0adb340e-6d63-46b4-96b8-5d404bcf43e8","resolution":{"observed_at":"2026-05-18T14:42:43.102114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":"3a660477-d9d0-4dee-a534-6e42501dda63","year":2022},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:bf68fa2338367722606a1feeaca008388290ac277e3fbbf592fda98d6b823f2c","observation_id":"6fff7743-145e-43d2-93dd-fd686a260834","resolution":{"observed_at":"2026-05-18T14:42:43.058068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"itrans- former: Inverted transformers are effective for time series forecasting","venue":null,"work_id":"7966cad8-1cee-4e8b-992f-a6dca540e76e","year":2023},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:c3dcf056d735adcbbf528ea60ad9ba8b04060c3e7e82ff8e6be6688075369f08","observation_id":"b75b0c8a-e929-4b64-bf46-0f47c7eaf967","resolution":{"observed_at":"2026-05-18T14:42:43.112786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adaptive context length optimization with low-frequency truncation for multi-agent reinforcement learning","venue":null,"work_id":"f6166d2c-76f4-42cb-b5da-711cb73f7d16","year":null},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:73ff5bbef630ca2f9b60ab213e5b33ecbef1b51687aa030a61584aaf7035e047","observation_id":"71851621-7cae-4662-898d-0f73a3a3773a","resolution":{"observed_at":"2026-05-18T14:42:43.155253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hippo: Recurrent memory with optimal polynomial projections","venue":null,"work_id":"d309f0dc-8055-4b61-bfb5-b6c84648096f","year":2020},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:c4fb0606dc6809e42523f57a2d6e26452def89da6ccf3f6ef83877c263e4fc0e","observation_id":"25cafe3c-0b9a-4904-ba78-593e907b4092","resolution":{"observed_at":"2026-05-18T14:42:43.011017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":"3726b1b6-078b-49ab-aa39-808de5b5adee","year":2021},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:0fdda2866aa354c503cb4dfb2624ab5c64d38c000b9d09c81a70812d28cb0fe8","observation_id":"8b99366b-f5e3-4a40-8a61-2315671f5a6d","resolution":{"observed_at":"2026-05-18T14:42:43.013719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":"5efffdbb-8368-4402-871d-55796e8fa62e","year":2023},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:3fdac6fd3b0c22170fe86fb502c6d163b5785a20d33dd09d1780897e5e136afc","observation_id":"4f1997f9-cc01-49e9-8a6a-c00eac615a7e","resolution":{"observed_at":"2026-05-18T14:42:43.015880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"U-mamba: Enhancing long-range depen- dency for biomedical image segmentation","venue":null,"work_id":"c869977a-45f5-4591-81b3-26f3bda84397","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:b96e7508c7f8b5b45c1f9e7f204da970e4001d88bb1ace0c66d80b2608029db4","observation_id":"cffaf44d-b40e-4bb8-b565-b90dcc442d42","resolution":{"observed_at":"2026-05-18T14:42:43.018130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":"0f7b4cec-cc95-4760-a387-474fd3122ebc","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:815e45d483678bc9a598161196b633a7c93c65d6b39f70ed214d1b802f1f7a8d","observation_id":"79ad143b-5532-44a6-b892-d693003093bd","resolution":{"observed_at":"2026-05-18T14:42:43.033879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Videomamba: State space model for efficient video understanding","venue":null,"work_id":"f3a77e3c-13ea-4c9c-96d0-88d11560b116","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:20d71381498f1e3dc0aa5dbe38a780bd27410419fdb96cc04e2898bff482ab9f","observation_id":"48ab970d-2265-4283-a215-e159d085dbf5","resolution":{"observed_at":"2026-05-18T14:42:43.157944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graph neural controlled differential equations for traffic forecasting","venue":null,"work_id":"e03d0b2d-5fca-4471-9da2-7ef28bef8331","year":2022},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:3aaf6a4bee0b71559f17dc5f6de5d58d616effe5bb18857303c9885a254a49a6","observation_id":"76e4af41-de7f-45c7-a35b-ee8a74f5220d","resolution":{"observed_at":"2026-05-18T14:42:43.162437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mcst-mamba: Multivariate mamba-based model for traffic prediction","venue":null,"work_id":"4b59512e-4e52-4188-bca4-78e3e43d5869","year":2025},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:e578ff2ed8a8c2f47c84dd2c841e802f5653aea2ee2759690467132eb025dc47","observation_id":"4f36aaf9-e03c-43ba-8a56-39e9badae0fe","resolution":{"observed_at":"2026-05-18T14:42:43.166821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A mamba foundation model for time series forecasting","venue":null,"work_id":"ce3b045f-b230-4c6d-b713-97f94dfcbf65","year":2024},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:ab597c58d0ec78fc8ce051a371e9e73d28b03939997b3bcd4e4978af10cae413","observation_id":"c74ee859-7f76-4467-8e80-6538f2615179","resolution":{"observed_at":"2026-05-18T14:42:43.089630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction"},"reference_resolution":{"displayed":82,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":0,"verified_exact":13,"verified_fuzzy":66},"total_outbound_references":82},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 3 inbound Pith citation observations for arXiv:2604.13453."}