{"as_of":"2026-08-20T23:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:128eda2cb38eb0bab23ba53661deb2bcab57a56dadf3e50565ad95730b48f3e8","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T22:22:53.215257Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2605.26320/citation-record","integrity":"/paper/2605.26320/integrity","json":"/paper/2605.26320/citation-record.json","paper":"/paper/2605.26320"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:22:53.215257Z","title":"Generalist multimodal ai: A review of architectures, challenges and opportunities,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:55e4bfe1838985d2f049aef88ebb49d11bda6bddaaedc12a3aafa717870fde5b","observation_id":"93042950-5784-44f0-9e0c-21175669d0ae","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Seisbenchâ ˘AˇTa toolbox for machine learning in seismology,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:73f7c6993c0f34e174f4a79b1e5d8bc4e3ba50359f73e138a912f80de8392479","observation_id":"00729652-a29d-41e6-a935-58e39a069270","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Stanford earthquake dataset (stead): A global data set of seismic signals for ai,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:46d51d2003dec7914b490f0cb01ac3d885c68c1d54ffd99618409d351a8fc7d7","observation_id":"05b6a612-5de2-4194-ad6d-87f67ec5fbab","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19960","last_updated":"2025-07-09T07:08:00Z","snapshot_observed_at":"2026-08-16T12:54:42.095537Z","submitted_at":"2025-02-27T10:35:53Z","title":"SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model","version":2},"cited_work":{"arxiv_id":"2502.19960","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.19960","snapshot_observed_at":"2026-07-01T13:35:46.020308Z","title":"Seismollm: Advancing seismic monitoring via cross-modal transfer with pre-trained large language model","venue":null,"work_id":"081a27bf-40c1-4f6c-ae12-78566c463be0","year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2502.19960","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:b19b9d335d081256706aefa8de72b11fd99a58f2737fe9187eb7d47688ecbf0a","observation_id":"0147e8c1-354e-4eff-bd3a-aad1c6bbdc6b","resolution":{"observed_at":"2026-06-29T22:23:59.930737Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:22:53.215257Z","title":"ShakeMap: Near real-time maps of earthquake shaking,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:28c5df5d952e680574301ff4799ca2e56847db29f21d58b4de90441647ce68e3","observation_id":"b2a76fec-2891-4ddc-8a0f-6b193a3f357e","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Unified-io 2: Scaling au- toregressive multimodal models with vision language audio and action,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:46028ad5bda8155ecc7abeb1c6af7cd42754386455f50216447c092e592ed7e2","observation_id":"f7befa66-71f5-4084-9c8a-375f5e5370c3","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01743","last_updated":"2025-03-07T09:05:58Z","snapshot_observed_at":"2026-08-15T22:57:45.773661Z","submitted_at":"2025-03-03T17:05:52Z","title":"Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs","version":2},"cited_work":{"arxiv_id":"2503.01743","doi":"10.18653/v1/2023.wmt-1.23","metadata_source":"pith","pith_arxiv_id":"2503.01743","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs","venue":"cs.CL","work_id":"83956045-536a-41ff-af02-b80e2a614eab","year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2503.01743","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:3460f17c254d1c1d96ee2c42cca3faec67d297e122f27332f04c51fde4e0d2a7","observation_id":"043e5342-89fe-4824-b2f5-cef53576b92e","resolution":{"observed_at":"2026-06-29T22:23:59.913079Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:22:53.215257Z","title":"Instance–the italian seismic dataset for machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:ae609142a4e14ff59ae003ca83b88ac5c492254d2a3161582899d0c284969de6","observation_id":"edf9adb9-7abf-4bc6-97fa-2ef1b76f95c2","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"National earthquake information center systems overview and integration,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:fdf3e625840feb70f1a6cdc0ff9b422e41f3dabcb21ce6921d21b6b0b5823111","observation_id":"e55f20cc-43a2-41eb-a02d-512c744e532e","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Which picker fits my data? a quanti- tative evaluation of deep learning based seismic pickers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:658f786fc9240c2b424cf7082f51c1a50150dc0d787092be4bcc23f1452238ec","observation_id":"aebe4009-bc7e-414d-b982-b9031a570945","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Loc- flow: An end-to-end machine learning-based high-precision earthquake location workflow,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:58c188cef38531ccef6ff9cb677ea0c68d57d743f8f530c6b5bfbac6b0c89883","observation_id":"9cb36969-74e8-4765-84cb-ccc29425df48","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Seismic arrival-time picking on distributed acoustic sens- ing data using semi-supervised learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:e70ecbdacb0b1498a029a76c907b27a34b25951b0ad695a849d0a4fa79308e8c","observation_id":"6db09567-2b45-48c8-a72a-15634ce2aef9","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Gemini and physical world: large language models can estimate the intensity of earthquake shaking from multimodal social media posts,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:18be54b489897ae966778d3fb8de87e16d942cb15fe4cc1172066368889c273b","observation_id":"a6f017fb-e52f-4309-827b-84c15886a8f4","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Onellm: one framework to align all modalities with language. arxiv,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:31427aec4a8e462c5ca0018ad57f54b0e4bfec7744affd163f7d16a22b8eb6c2","observation_id":"8c8a29c9-80c9-4e71-b929-9ff946ba23e9","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Next-gpt: Any-to-any multimodal llm,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:dd9599dca6302343b7f5f89e0f66d804bc43aa33decb56f3e08504a294fe8459","observation_id":"9313d589-39f4-4174-aeab-ec31b18f29df","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"M3SciQA: A multi-modal multi-document scientific QA benchmark for evaluating foundation models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:7e94cf43a4dad4d66e9dc5e468605eb7f1bf9aaf4ac0e6005db57e1bb3e51da6","observation_id":"660506ff-1a66-4398-ac24-addc0ceacb76","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Learn to explain: Multimodal reasoning via thought chains for sci- ence question answering,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:44f40eff5ad43ddebe9b408c937101a85efea9e8f39b8d70674157891610312c","observation_id":"0642d0bd-1aae-4b04-aead-4cf2672e43c3","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21051","last_updated":"2025-04-29T03:07:38Z","snapshot_observed_at":"2026-08-16T05:28:14.985658Z","submitted_at":"2025-04-29T03:07:38Z","title":"Multimodal Large Language Models for Medicine: A Comprehensive Survey","version":1},"cited_work":{"arxiv_id":"2504.21051","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21051","snapshot_observed_at":"2026-06-29T22:23:59.931932Z","title":"& Tang, H","venue":null,"work_id":"4288ede7-ce69-442f-a90c-61310090d490","year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2504.21051","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:a11660e9a6e64b0a9b570be87c1e5644e9f2edd39f98c1ef5a1be9afbe2c0340","observation_id":"59cde944-f041-4a73-aaef-15a8e88ef5d9","resolution":{"observed_at":"2026-06-29T22:23:59.933375Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02527","last_updated":"2024-12-03T16:21:17Z","snapshot_observed_at":"2026-08-18T10:30:23.195608Z","submitted_at":"2024-12-03T16:21:17Z","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","version":1},"cited_work":{"arxiv_id":"2412.02527","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02527","snapshot_observed_at":"2026-06-29T22:23:59.927409Z","title":"The multimodal universe: enabling large-scale machine learning with 100tb of astronomical scientific data,","venue":null,"work_id":"b2956be1-0f16-4fd7-a182-9037da4a6281","year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2412.02527","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:ff95c227920aa7119fbb626f39f651fb3d3ed5eb2d54839c59f0f7ef7f2ecf0a","observation_id":"c41e6977-cc42-47c3-a1e4-99e6663221a4","resolution":{"observed_at":"2026-06-29T22:23:59.929114Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-16T11:06:17.042878Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":"2310.01728","doi":"10.48550/arxiv.2310.01728","metadata_source":"pith","pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-07-11T01:17:44.658229Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","venue":"cs.LG","work_id":"e2b7aab4-6ed4-457b-9074-6fa4816dfdc2","year":2023},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:82630aaeb4f10f4ff8af48bb5e47db062333876bcb2568cb080630a6eed56f2e","observation_id":"06e84cec-9652-4cd4-81c4-1eeb125fde2e","resolution":{"observed_at":"2026-06-29T22:23:59.919463Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04395","last_updated":"2025-05-26T14:45:18Z","snapshot_observed_at":"2026-08-20T02:53:57.037944Z","submitted_at":"2025-02-06T05:59:45Z","title":"Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting","version":2},"cited_work":{"arxiv_id":"2502.04395","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04395","snapshot_observed_at":"2026-07-01T19:56:10.514464Z","title":"arXiv preprint arXiv:2502.04395 (2025)","venue":null,"work_id":"6e4e8853-9655-48ab-9417-5bdb78a9313c","year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2502.04395","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:0a84ec002c43b64e0829e6656e2b64c62234d7b02373dbf8ef22ede95a8c38de","observation_id":"1d140e72-5d1a-4e0f-bd63-a8064a95bd69","resolution":{"observed_at":"2026-06-29T22:23:59.922789Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02637","last_updated":"2024-11-28T16:01:47Z","snapshot_observed_at":"2026-08-17T08:14:04.502730Z","submitted_at":"2024-10-03T16:23:13Z","title":"Plots Unlock Time-Series Understanding in Multimodal Models","version":2},"cited_work":{"arxiv_id":"2410.02637","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02637","snapshot_observed_at":"2026-06-29T22:23:59.923481Z","title":"Plots unlock time-series un- derstanding in multimodal models,","venue":null,"work_id":"fb5ba1b8-8d16-4a25-b152-02ae26bbd0b7","year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"cited_paper":"/paper/2410.02637","citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:7a1b9a1436f656d9b12cf666d8b153e2ea67879118456488a2bc6a51832c87b6","observation_id":"b214624b-b274-4040-9ca3-886afd846ebb","resolution":{"observed_at":"2026-06-29T22:23:59.924994Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:22:53.215257Z","title":"Wilber3: Interactive event search and data retrieval,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:2a5ab430e8d17ae6b24533708dca0bda30aa40ce5bd5813713579ffe4354bc55","observation_id":"68909d04-d2c3-4ede-a1d7-b7ee87507d81","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Chronos: Learning the language of time series,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:f895b8154cfb9040249690b79655c9a2de9c82d2ec3a6e54dfe74de95b9b232e","observation_id":"3a47260a-305b-4ada-8840-0c160c270bcc","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"2026-06-29T22:22:53.215257Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:680cbf95e13b14629cbdc2b52ae8ea4b9bab84aa92299e9cb8e516d214017ef8","observation_id":"93f4d874-98f0-4fa0-aba3-d90033bf0184","resolution":{"observed_at":"2026-06-29T22:22:53.215257Z","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":"1190.1667","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T22:23:59.926674Z","title":"Imagebind: One embedding space to bind them all,","venue":null,"work_id":"140148c3-8201-4374-b3ee-f4f79ddacb60","year":2023},"citing_paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T22:22:53.215257Z"},"links":{"citing_paper":"/paper/2605.26320"},"observation_digest":"sha256:4840d13283cf0d27b71a82227dca3313d0b3d70530d51f7c1c6d2cc934f9b93a","observation_id":"f271e851-210d-4c79-8db2-e8fd7a011417","resolution":{"observed_at":"2026-06-29T22:23:59.928147Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.26320","last_updated":"2026-05-25T20:35:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T00:16:28.759414Z","submitted_at":"2026-05-25T20:35:48Z","title":"MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":18,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":26},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2605.26320."}