{"as_of":"2026-08-10T05:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fbc35a550eec22154f84359fa1b6367e6da3f8035746ced18dcfe58c4313b10f","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:01:34.789342Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.20073/citation-record","integrity":"/paper/2506.20073/integrity","json":"/paper/2506.20073/citation-record.json","paper":"/paper/2506.20073"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.03784","last_updated":"2024-02-07T02:10:11Z","snapshot_observed_at":"2026-08-03T14:14:07.685811Z","submitted_at":"2024-02-06T07:55:54Z","title":"AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03784","snapshot_observed_at":"2026-08-06T23:01:34.525478Z","title":"Airphynet: Harnessing physics- guided neural networks for air quality prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.525478Z"},"links":{"cited_paper":"/paper/2402.03784","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:027b43443db4ae3a4b05727e9e3ce6ddfcf8a36ddf3d9ea86f02a9ba030ba0ef","observation_id":"b3b812fb-fb1f-4682-b591-b6878c591095","resolution":{"observed_at":"2026-08-06T23:01:34.525478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.753584Z","title":"Airformer: Predicting nationwide air quality in china with transformers,","venue":null,"work_id":"8c294ca9-dd00-4ed1-9127-fae9c7ba7375","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.531182Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:cd62e0a116dccd1a5cf0e43335eaa00c09896f78cd71ec05c55475d7ee25ce45","observation_id":"3b7ce511-5c99-4306-b366-f8a0c5382fae","resolution":{"observed_at":"2026-08-06T23:01:35.758498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.737926Z","title":"Urbangpt: Spatio-temporal large language models,","venue":null,"work_id":"acbb2388-3bc3-4fbe-8e6c-fad0aa3897ef","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.536053Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:ca45c04bc4347669e8ce74e389357ed52784f87176f0417bdaf169d57df4109a","observation_id":"1fe9333c-332c-4360-b436-6abcd7bb63f3","resolution":{"observed_at":"2026-08-06T23:01:35.742796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.717499Z","title":"Stden: Towards physics-guided neural networks for traffic flow prediction,","venue":null,"work_id":"057259f5-a7ce-4e4a-b316-4bf349380606","year":2022},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.540986Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:bfcbe4792b389f5b162507b0823e714cef83147a8b1dadb67fe9f68ad3cdb81e","observation_id":"578ccdc1-b546-46b4-be6e-ecde7012ccfb","resolution":{"observed_at":"2026-08-06T23:01:35.725620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.700069Z","title":"Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,","venue":null,"work_id":"782ef801-1096-4a04-ae20-f0bb38fe4e3c","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.546053Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:78ec16e232bc74d9eeedbad31ba11d135c2dbac7efaeb0d5bb52f9203385be28","observation_id":"8dba7ea5-12c1-4ad5-96fd-96f15b99d387","resolution":{"observed_at":"2026-08-06T23:01:35.705729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.683313Z","title":"Urban flow prediction from spatiotemporal data using machine learning: A survey,","venue":null,"work_id":"26ce965b-0e8e-49aa-8255-46d73e9582ed","year":2020},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.551145Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:584366312819c4cbb306762ff934914e76764003f5277d6efa29efec9600a435","observation_id":"e0527bd6-f2cd-4489-91c3-84e3e54f3ce8","resolution":{"observed_at":"2026-08-06T23:01:35.689409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.667115Z","title":"Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,","venue":null,"work_id":"9e79526e-1781-4006-8f99-d07b43ea79a7","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.557175Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:1bbf2b843ac7c19a17f5b25c21f2ef1cd65bd315634106c02d8d238489550d88","observation_id":"5f195cd9-2833-4a75-b422-c79913b7bc09","resolution":{"observed_at":"2026-08-06T23:01:35.672421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.651212Z","title":"Deep learning for spatio-temporal data mining: A survey,","venue":null,"work_id":"7861c290-bbf7-4f74-bb52-cb50bc2f3ed6","year":2020},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.562376Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:e06ad90adbfc9a1223214f6616bc14c7fc1c03d85184a9c3dd4d525103c59c2d","observation_id":"1fe48ea6-37be-4977-b84c-3f9680b1a060","resolution":{"observed_at":"2026-08-06T23:01:35.655987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09592","last_updated":"2024-05-15T12:07:43Z","snapshot_observed_at":"2026-07-06T18:14:54.506051Z","submitted_at":"2024-05-15T12:07:43Z","title":"A Survey of Generative Techniques for Spatial-Temporal Data Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09592","snapshot_observed_at":"2026-08-06T23:01:34.567225Z","title":"A survey of generative techniques for spatial-temporal data mining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.567225Z"},"links":{"cited_paper":"/paper/2405.09592","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:e3e282c5c9fec23c79c4d6ec7e1089a94508c685ac5a5c39bc4a114fd528e49e","observation_id":"5f2cff8d-712e-4364-8b68-7ecc2b406e17","resolution":{"observed_at":"2026-08-06T23:01:34.567225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.635912Z","title":"Foundation models for time series analysis: A tutorial and survey,","venue":null,"work_id":"f5a6e072-23bc-4c3a-bf10-d201d895e057","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.572497Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:c4290d79921799f8c525517aa39ceba54f56a73456c398ff5172f8ce684085c2","observation_id":"60f95b61-9465-46d0-a915-c2de23f2e205","resolution":{"observed_at":"2026-08-06T23:01:35.640852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13502","last_updated":"2025-03-12T09:42:18Z","snapshot_observed_at":"2026-08-07T17:10:38.634505Z","submitted_at":"2025-03-12T09:42:18Z","title":"Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.13502","snapshot_observed_at":"2026-08-06T23:01:34.577356Z","title":"Foundation models for spatio-temporal data science: A tutorial and survey,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.577356Z"},"links":{"cited_paper":"/paper/2503.13502","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:8bca8db05f90cf472dca10dd70e6c5cd9ba0509ea187d210a30c3c9d45c517f4","observation_id":"6be8a144-ebf7-424b-bab4-a262aef07469","resolution":{"observed_at":"2026-08-06T23:01:34.577356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.619354Z","title":"TEMPO: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":"ba5d46d7-3c2f-4b58-bca3-cbeaea8bd014","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.582644Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:eea98258bb1ccc519ffd31c02f79c6ae60a60286a3664cdc84a6ca3fb5fbbab2","observation_id":"721f8dad-d404-45ea-91ca-5f50081a1803","resolution":{"observed_at":"2026-08-06T23:01:35.625039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.602696Z","title":"One Fits All: Power general time series analysis by pretrained lm,","venue":null,"work_id":"0e752042-b811-455a-a1f8-bf122a159a0a","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.587485Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:ce870c574000630bbb8fdd6ea34b66c6cf656f1d3ae358fa1c2099b5010b74a7","observation_id":"7cc66a85-a6cf-4d3e-a49f-de7e85d9f969","resolution":{"observed_at":"2026-08-06T23:01:35.608408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.583351Z","title":"Can large language models be anomaly detectors for time series?,","venue":null,"work_id":"468e4a50-ef7b-4cdd-b526-98059c307780","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.592182Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:054c9a47a61d6087508a08898b24d7030ae1a108b1ab871faf346aea38419155","observation_id":"bf560d79-f596-4a32-bb6a-aae4152bfec4","resolution":{"observed_at":"2026-08-06T23:01:35.590158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.564607Z","title":"GATGPT: A pre-trained large language model with graph attention network for spatiotemporal imputation,","venue":null,"work_id":"1f94a9c7-bcf8-450b-827f-a06bef56cbfb","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.596786Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:224b22080b9a8565e4d8c957005e52e42c523e97d69afe2a0aa6e73b321cef34","observation_id":"6347046c-c905-4822-9386-46190a297bad","resolution":{"observed_at":"2026-08-06T23:01:35.570750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.548206Z","title":"Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction,","venue":null,"work_id":"17d161b8-4272-4a3d-9832-a2b8d1156cba","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.601413Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:94c3952be2427c2be463e5b6560d660260286620f63b3e2cb816c46c2fee8a20","observation_id":"8692700b-8d94-400b-95e1-b46a5855ed58","resolution":{"observed_at":"2026-08-06T23:01:35.553286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.531982Z","title":"Unist: A prompt-empowered universal model for urban spatio-temporal prediction,","venue":null,"work_id":"b6402d41-cccd-43e2-b87d-83642401ebe1","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.605924Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:51946067b4aefb6268e5029df26a11cbca0c8be2728fabe8557b90f6e8dcd15d","observation_id":"83b1b8e9-65a4-491e-88b6-ee1b0906cb79","resolution":{"observed_at":"2026-08-06T23:01:35.537549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.514465Z","title":"Are language models actually useful for time series forecasting?,","venue":null,"work_id":"350f9040-7700-44fe-b167-8905d90ca825","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.610452Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:378895d0c5ff8b6c0e2edf1efa73961da41bd5158de7e5164d652ac03c9d3aec","observation_id":"530b595d-5599-4452-aa33-c4fffa00bb77","resolution":{"observed_at":"2026-08-06T23:01:35.520186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.496596Z","title":"Position: Llms can’t plan, but can help planning in llm-modulo frameworks,","venue":null,"work_id":"76c4f4a4-a24c-4244-98c2-b3621e544d53","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.615617Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:383918e140ff54b24f4a8f84d405d0dd58b5537d64eb3c94fd2f1131375e23a9","observation_id":"a6f28582-24db-4a96-867e-4ebb0151455d","resolution":{"observed_at":"2026-08-06T23:01:35.502505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.477946Z","title":"Spatial-temporal large language model for traffic prediction,","venue":null,"work_id":"e8e78419-d5e0-4137-aff4-870f4fa52a08","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.620199Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:ac40f814350b68146cb9f144b3334734ba9c091fae634076356564ff483e646d","observation_id":"ffec43a5-cd2c-4321-aa8d-26e703e32bf6","resolution":{"observed_at":"2026-08-06T23:01:35.484494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.461291Z","title":"TimeCMA: Towards llm-empowered multivariate time series forecasting via cross-modality alignment,","venue":null,"work_id":"25ed3573-9a24-42a2-a777-bc60c44d7724","year":2025},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.624970Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:fa52754ff9588bf1adee228cadd9ebd7bf5cc0608d46ea1973740fd76ace2a62","observation_id":"770dc12b-e8a6-498d-9dfd-2f025793526b","resolution":{"observed_at":"2026-08-06T23:01:35.466498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.442800Z","title":"Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,","venue":null,"work_id":"735da4d6-f617-4e14-959a-2f5d02f03bb9","year":2025},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.629413Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:bb603942b018821c6f9f839fac424d9c423775dbd6af7fa1999f367139c854b8","observation_id":"4608e87a-5a73-4b24-bb55-90ac6be1d09a","resolution":{"observed_at":"2026-08-06T23:01:35.448416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.424040Z","title":"Genfollower: Enhancing car-following prediction with large language models,","venue":null,"work_id":"e66b6550-e0af-438c-9cdb-e617abaaa74a","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.633830Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:94c182cfcabb6bc6cbf56805c0a36659c264b922d021962825a88ade808c6eda","observation_id":"4a84e75f-562e-4845-89f3-127be25b6cd6","resolution":{"observed_at":"2026-08-06T23:01:35.429137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.408029Z","title":"Towards explainable traffic flow prediction with large language models,","venue":null,"work_id":"7d20e110-2ac8-46c9-8ce0-faab40ed5ed4","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.639224Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:b9053fb73c3ae8acce5490d15409c1782c64fb7abcebaaedfcdd138b00ec828f","observation_id":"d46eec2e-ebed-4263-91f0-d232d90bfeb5","resolution":{"observed_at":"2026-08-06T23:01:35.413181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12360","last_updated":"2024-06-18T07:41:42Z","snapshot_observed_at":"2026-08-07T14:30:57.020633Z","submitted_at":"2024-06-18T07:41:42Z","title":"UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12360","snapshot_observed_at":"2026-08-06T23:01:34.643798Z","title":"Urbanllm: Autonomous urban activity planning and management with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.643798Z"},"links":{"cited_paper":"/paper/2406.12360","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:2aa63aa782a1b6045e0673a9c37e436cd0ad5af98a575e280f189e50ea337929","observation_id":"bdab236e-f711-4916-b66f-185082898928","resolution":{"observed_at":"2026-08-06T23:01:34.643798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04047","last_updated":"2026-04-10T06:16:18Z","snapshot_observed_at":"2026-07-06T19:28:18.775189Z","submitted_at":"2024-10-05T06:04:19Z","title":"TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04047","snapshot_observed_at":"2026-08-06T23:01:34.648950Z","title":"Beyond forecasting: Composi- tional time series reasoning for end-to-end task execution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.648950Z"},"links":{"cited_paper":"/paper/2410.04047","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:13c7d9cfa6e0cbfd4cb58bdb7bfd91571a4ed86710e950ba4b09200a31ef6574","observation_id":"1e429b58-bfca-41eb-a0d2-5f78b346f4ee","resolution":{"observed_at":"2026-08-06T23:01:34.648950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.390072Z","title":"ELI5: Long form question answering,","venue":null,"work_id":"1e0f17b2-2358-49bf-841c-7cb106b93069","year":2019},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.654245Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:94ba29b3e9f57c1ad94b300c456bbc589b8a98340e6df42a45ac320784bbe6c5","observation_id":"6c91d945-18f5-43f6-9a41-3c1f652f282c","resolution":{"observed_at":"2026-08-06T23:01:35.395426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.372692Z","title":"GeoLLM: Extracting geospatial knowledge from large language models,","venue":null,"work_id":"15d10b88-e62e-499e-ace3-65420f48ba56","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.658921Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:2fc908a41e08e157718be1e02488aa1a0d4fe072f2cdabca4def9b04326261f9","observation_id":"9aa4a006-b000-47c0-ae7d-b45aaaba1985","resolution":{"observed_at":"2026-08-06T23:01:35.378740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.356375Z","title":"Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,","venue":null,"work_id":"2c397edb-45d4-438d-9cee-cb81e392cd42","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.664079Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:eb22323a11cf0ca7cca776ed3e4b61cf85eff1246c9ef90e57459bd9441e59f8","observation_id":"3a53f122-16bc-457b-95be-7e3f41f7f578","resolution":{"observed_at":"2026-08-06T23:01:35.361385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.339037Z","title":"Visual programming: Compositional visual reasoning without train- ing,","venue":null,"work_id":"f740e23e-9e24-4004-be88-bd5c93bfeec8","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.668916Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:06f1e2bada8d09db9ab97f272cb5ac3e4403213ce7f9de4ed91881b2a8890991","observation_id":"6b622cdb-299d-431b-8e49-73495ec5bb4f","resolution":{"observed_at":"2026-08-06T23:01:35.344683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10196","last_updated":"2026-06-08T01:28:46Z","snapshot_observed_at":"2026-07-06T16:33:34.184498Z","submitted_at":"2023-10-16T09:06:00Z","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10196","snapshot_observed_at":"2026-08-06T23:01:34.673834Z","title":"Large models for time series and spatio-temporal data: A survey and outlook,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.673834Z"},"links":{"cited_paper":"/paper/2310.10196","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:793fbbbeaaf44fea0438991fd4f04c1803d505e2a6b897e5315107327eb30a9a","observation_id":"2b968c0e-ba51-4d82-93de-41e9d5a11494","resolution":{"observed_at":"2026-08-06T23:01:34.673834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.321505Z","title":"Unitime: A language- empowered unified model for cross-domain time series forecasting,","venue":null,"work_id":"3bf099fc-d1df-4aa0-bbb6-671dfe74b737","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.679156Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:6e4816cfa75a05a362ac0fc2a25f93e5f4a2e3901394dd8bc72d893c4dce8bcd","observation_id":"196cfb5d-fcb4-4e2a-a122-fbd069f07145","resolution":{"observed_at":"2026-08-06T23:01:35.327520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.303830Z","title":"Large language models are zero-shot time series forecasters,","venue":null,"work_id":"d07a0ee6-0999-4409-9a7e-cfec1f5d0850","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.683984Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:3f19dc38de211a05f479da9a79b8b64f0423cde735fd1ff6d4d9558fea3ea33a","observation_id":"e0bd0b46-9bcc-4489-b996-a72b3e25fba3","resolution":{"observed_at":"2026-08-06T23:01:35.310201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.15197","last_updated":"2024-01-09T14:08:03Z","snapshot_observed_at":"2026-07-06T16:11:43.387948Z","submitted_at":"2023-08-29T10:24:23Z","title":"Where Would I Go Next? Large Language Models as Human Mobility Predictors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.15197","snapshot_observed_at":"2026-08-06T23:01:34.688526Z","title":"Where would i go next? large language models as human mobility predictors,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.688526Z"},"links":{"cited_paper":"/paper/2308.15197","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:6017df8ee69c8341e703e056ca7fd345225ff078135454fc1cdc2d7cc6c90df8","observation_id":"1c4f74d8-e91e-4779-8cf7-b680882f344d","resolution":{"observed_at":"2026-08-06T23:01:34.688526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-06T23:01:34.693129Z","title":"Gpt-4o system card,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.693129Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:1f97e529331e96900e2db6b65e53c0b2747c27df51ca8092160c6d6994a493e4","observation_id":"42c6bc60-a614-4722-b492-3b2e8c246f39","resolution":{"observed_at":"2026-08-06T23:01:34.693129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T23:01:34.697609Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.697609Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:c2a4743f6e753fb6f870307165d239a18cd8e8c49b54f17919aaf07909462e3d","observation_id":"4b23e09c-87cd-4991-991c-e155e6242b85","resolution":{"observed_at":"2026-08-06T23:01:34.697609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:34.702442Z","title":"Language models are few-shot learners,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.702442Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:d7228ffb110b0e0c8afd96fe6bae277025aa4ab6a35756b9ea7d047d85982c17","observation_id":"f6de8e3c-ba47-44a2-a890-86746a1dc6b0","resolution":{"observed_at":"2026-08-06T23:01:34.702442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:34.706944Z","title":"Tree of thoughts: Deliberate problem solving with large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.706944Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:60bad6d6a1b6320e0bb73b4419fc60b199375ef642bf6be7e2c0f2e4c1ffcf5e","observation_id":"6e5e2c60-6245-43e3-a804-859af4a63860","resolution":{"observed_at":"2026-08-06T23:01:34.706944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.264383Z","title":"Self-consistency improves chain of thought reasoning in language models,","venue":null,"work_id":"ff78a90f-0107-4700-aee2-50fcdd050e97","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.711392Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:b4ee1323d94049d6a549c9ace3a5778c29fbd721dd774d16d7f2e5bbc897551e","observation_id":"67d4947a-e321-4a93-ae94-ae1b1d53baf4","resolution":{"observed_at":"2026-08-06T23:01:35.269798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.247300Z","title":"Towards revealing the mystery behind chain of thought: a theoretical perspective,","venue":null,"work_id":"8e7639ef-5e5e-459d-ae43-c9cd70da3d7b","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.716024Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:543d62d768ac15798c1b4fc625965df05007d63a1b01231861f6be5611f60557","observation_id":"8e7a1f23-e620-4a4b-8acf-43e13ae95047","resolution":{"observed_at":"2026-08-06T23:01:35.253251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.229681Z","title":"Chain-of-table: Evolving tables in the reasoning chain for table understanding,","venue":null,"work_id":"a96651a2-d45c-4e06-99c7-f0ca7a5dc803","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.720577Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:f0b0afeb2e902dfe178b022842b0b50a99fb58f3d35bc0e233bbf46036e479f3","observation_id":"577d5e2c-560c-4866-a444-3a60c16f1283","resolution":{"observed_at":"2026-08-06T23:01:35.235534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19065","last_updated":"2024-06-27T10:34:02Z","snapshot_observed_at":"2026-07-06T18:37:51.919811Z","submitted_at":"2024-06-27T10:34:02Z","title":"STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19065","snapshot_observed_at":"2026-08-06T23:01:34.724997Z","title":"Stbench: Assessing the ability of large language models in spatio-temporal analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.724997Z"},"links":{"cited_paper":"/paper/2406.19065","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:21515232333f2a34dc9d771a54a1b6ff2439e7b87d0ca0c068dfb47ede8ae165","observation_id":"95649d76-a71e-4434-b0fc-10ffd2a0598b","resolution":{"observed_at":"2026-08-06T23:01:34.724997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.210435Z","title":"Situatedgen: Incorporating geographical and temporal contexts into generative commonsense reasoning,","venue":null,"work_id":"4e28ac32-678a-4ced-8ee3-10be6b50e72b","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.729939Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:0da7e457a55b5fe1175beaf8851617ed9d91be478c64c37fe0f6dac345196b4f","observation_id":"e2835b45-21e1-4f68-99ea-42e2e6884614","resolution":{"observed_at":"2026-08-06T23:01:35.216656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.193339Z","title":"AutoGPT","venue":null,"work_id":"525b893a-7e50-49e2-ae32-dbf7c1a41671","year":null},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.734858Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:d3cb52ab00c4d90a78c90da7dab6895c6746d7f4c35b12266924025145ca385b","observation_id":"3e4c8e85-c33e-4017-968d-0608bf41304d","resolution":{"observed_at":"2026-08-06T23:01:35.199053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07930","last_updated":"2023-07-16T03:03:59Z","snapshot_observed_at":"2026-08-10T02:41:02.370751Z","submitted_at":"2023-07-16T03:03:59Z","title":"GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07930","snapshot_observed_at":"2026-08-06T23:01:34.739761Z","title":"Geogpt: Understanding and processing geospatial tasks through an autonomous gpt,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.739761Z"},"links":{"cited_paper":"/paper/2307.07930","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:891f727f6e36a28efc887b16605a7edb6e963a78a77fb422fa8fc1e26675ae0a","observation_id":"b2f811a6-0b23-41c7-abf1-3ec5b75c3333","resolution":{"observed_at":"2026-08-06T23:01:34.739761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.174357Z","title":"Large language models as urban residents: An llm agent framework for personal mobility generation,","venue":null,"work_id":"cdc51cb7-3d0b-43f6-b495-0ae84584f3af","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.744592Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:dc76b9d1f172c51da0b94ce030686bf90941d384c61b60fcccc2c7d92edf1889","observation_id":"71591e0f-e411-4de5-aa07-7320fbe859a7","resolution":{"observed_at":"2026-08-06T23:01:35.180642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.156610Z","title":"Large language models empowered agent-based modeling and simulation: A survey and perspectives,","venue":null,"work_id":"d295a971-cec9-43fe-b9a9-d22b1c92bc8d","year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.749516Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:5a5fc9ac4286829d932ee6ff129444604911f01a15b4f8f244889c1dd494a4a0","observation_id":"66d13565-347d-478b-8246-bc378ef7f8c0","resolution":{"observed_at":"2026-08-06T23:01:35.161915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.01926","last_updated":"2018-02-22T19:52:51Z","snapshot_observed_at":"2026-08-01T21:20:06.448984Z","submitted_at":"2017-07-06T18:20:59Z","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.01926","snapshot_observed_at":"2026-08-06T23:01:34.754035Z","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.754035Z"},"links":{"cited_paper":"/paper/1707.01926","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:e3666607c577d66c1995b09fc1a78f8638175edc990ee344f2003040ded8de96","observation_id":"fcfd69d6-7c0c-4350-bbae-8b37aa160ed5","resolution":{"observed_at":"2026-08-06T23:01:34.754035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10420","last_updated":"2023-12-23T12:53:02Z","snapshot_observed_at":"2026-08-07T23:33:44.705669Z","submitted_at":"2023-03-18T14:02:04Z","title":"A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10420","snapshot_observed_at":"2026-08-06T23:01:34.759049Z","title":"A comprehensive capability analysis of gpt-3 and gpt-3.5 series models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.759049Z"},"links":{"cited_paper":"/paper/2303.10420","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:99c06ca68300b85861670c88a0435e1df541a1a651ac5916dbcee8ba8604e874","observation_id":"3704ba2b-470f-43e9-9698-6d07446414fc","resolution":{"observed_at":"2026-08-06T23:01:34.759049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-06T23:01:34.763933Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.763933Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:ee0e71a07176039ca53cfbc37df306e5b12af3e3d11c619b1c5590829856a15d","observation_id":"279da6df-5bbf-4af8-9df4-1938a05f8ab3","resolution":{"observed_at":"2026-08-06T23:01:34.763933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-06T23:01:34.768706Z","title":"Deepseek-v3 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.768706Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:b7449d8e72d4236f41442e41673ea362da832fdd4922eb1516afc163d3c34d14","observation_id":"ed85fb28-3215-492b-b3ae-37f95eb7ef7f","resolution":{"observed_at":"2026-08-06T23:01:34.768706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.139915Z","title":"A survey of reasoning with foundation models: Concepts, methodologies, and outlook,","venue":null,"work_id":"21c2c850-82de-49ec-92a3-5df072563aa7","year":2023},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.773544Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:4af5165e2489a27d8795b5644a81a49c047c3ee5ef0c8a86ec71d19e56dfea36","observation_id":"7e4c7147-af7f-463d-bc20-d57b461385a7","resolution":{"observed_at":"2026-08-06T23:01:35.145541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.122658Z","title":"• General understanding of spatio-temporal tasks such as analysis, anomaly detection, and forecasting","venue":null,"work_id":"5158bf94-274a-481d-8539-1bcc1c40429c","year":null},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.778969Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:15d042d41f3ec4d6b715f64af7fababe5427c59448b3ea680cb7ef30fbf1ee3c","observation_id":"820c980a-199e-4f1d-bf7c-ec4a608cf766","resolution":{"observed_at":"2026-08-06T23:01:35.128235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.105806Z","title":"Each query was paired with two answers, one from STReason and one from a randomly selected baseline ensuring each baseline appeared an equal number of times","venue":null,"work_id":"188c1bb3-2b76-48a8-8850-f161a714ea2c","year":null},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.783979Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:ee02af490230430354b24ec00ad4d90ff539278e2380e894a32fc624e3ef5aea","observation_id":"38700d6b-9135-4b98-a2a4-cd437d929705","resolution":{"observed_at":"2026-08-06T23:01:35.111187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:01:35.084657Z","title":"They were also encouraged to provide open-ended feedback explaining their choices","venue":null,"work_id":"2276261e-f00a-41d2-befc-8f77bc26589c","year":null},"citing_paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T23:01:34.789342Z"},"links":{"citing_paper":"/paper/2506.20073"},"observation_digest":"sha256:8875b7b1732f75f7806d0c4ec3e8bef2adc05f664b1c15b9f6afd069912105a0","observation_id":"029fcae5-48e8-4f29-aed0-d5d4e24f2489","resolution":{"observed_at":"2026-08-06T23:01:35.092757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.20073","last_updated":"2025-06-25T00:55:34Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T12:29:16.033445Z","submitted_at":"2025-06-25T00:55:34Z","title":"A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":55},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2506.20073."}