{"as_of":"2026-08-15T19:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a001121f973eeb21500a1900d080fcb0e1fbb054ee14fdafae06666dca23123","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T00:44:13.983638Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.19372/citation-record","integrity":"/paper/2412.19372/integrity","json":"/paper/2412.19372/citation-record.json","paper":"/paper/2412.19372"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16160","last_updated":"2024-12-27T00:43:39Z","snapshot_observed_at":"2026-08-15T18:12:35.716951Z","submitted_at":"2024-11-23T18:30:04Z","title":"Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks","version":2},"cited_work":{"arxiv_id":"2412.16160","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.16160","snapshot_observed_at":"2026-08-11T00:44:14.341639Z","title":"Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks","venue":"q-fin.ST","work_id":"1628cc84-64e1-4da5-9e95-0fe4677cdd1f","year":2024},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.770288Z"},"links":{"cited_paper":"/paper/2412.16160","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:595a454745c5dc738c9d0eb89a7b8a88d2d51259fcd5fc51b89adaab29c4ac0f","observation_id":"e86b8eb3-959e-42d0-bc1c-6b74d24356c6","resolution":{"observed_at":"2026-08-11T00:44:14.347505Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.944492Z","title":"Alshawarbeh, A","venue":null,"work_id":"1b09d6b7-2204-4dcb-b71c-a3a00472ef86","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.776613Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:b6ae989bbd255aa3b2fbd6d87893daa6298b24fae032a13d9824e627ebd2f88d","observation_id":"9d3986d4-34b2-4f4c-969d-54d0b23f70bd","resolution":{"observed_at":"2026-08-11T00:44:14.951222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.927227Z","title":null,"venue":null,"work_id":"0e7fbbff-c2b0-4d46-aa1f-dcaa30783231","year":2020},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.781945Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:98559e02aacbdee78672d78b024e147ff1a504bd85ceddd414a1bb1e52a26ab7","observation_id":"e26f7e50-9160-4010-b673-a663babd8e60","resolution":{"observed_at":"2026-08-11T00:44:14.932033Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.911382Z","title":null,"venue":null,"work_id":"4d550986-2236-4f9a-9232-aee7343e71cc","year":2020},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.787337Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:68904ca81f4861259d96c7ae28802759cf26314798fc05b1d568489e1ac3c985","observation_id":"351e6786-052f-45ac-a206-ac0151ebb9b0","resolution":{"observed_at":"2026-08-11T00:44:14.916320Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.895153Z","title":"Zhu, G.-Y","venue":null,"work_id":"b1ed5674-54e2-41fa-895f-a530289895b6","year":2024},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.792846Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:a3843990e046296c914128c76f76374adf33475ac1ffcd22f944049b7659a1f7","observation_id":"14b9937d-2959-42ce-b954-85b9589b67bd","resolution":{"observed_at":"2026-08-11T00:44:14.900338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.880275Z","title":null,"venue":null,"work_id":"1a4250fb-bdd0-4149-9291-822fb7198b37","year":2024},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.798718Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:c887d298fe280c373bf4d0c4eaa5420c0274138359fb8f71a811f5c3867e4737","observation_id":"6db1d041-eaa9-425c-a975-2f2b3df3d4a3","resolution":{"observed_at":"2026-08-11T00:44:14.885081Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:13.805705Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.805705Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:ba3fe9726eb32dc966f89226c6c72671b755a720718cc995f9974b2f965508a8","observation_id":"4f152b25-2aea-4fed-8f3d-cf7c217f0de5","resolution":{"observed_at":"2026-08-11T00:44:13.805705Z","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":"2026-08-11T00:44:14.865273Z","title":"Dingli, K","venue":null,"work_id":"0679f29e-1a3b-4c58-b40d-3b154cd642f6","year":2017},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.811509Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:7060be07f04209926ea821ebdddc4fab5632feb7c3aeb271c8619b500eb7561b","observation_id":"6f375658-8e14-4f10-9509-7289e6d35d2a","resolution":{"observed_at":"2026-08-11T00:44:14.870094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.850079Z","title":null,"venue":null,"work_id":"dd540324-f71b-4768-8708-91ee66e20f7c","year":2021},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.817403Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:fca72f54497b3e530b947adb5ecbc262153650c8309493b5f4f6ff2f5a0b8108","observation_id":"f3951425-7af2-4c4f-b0c5-eb2b40dd2082","resolution":{"observed_at":"2026-08-11T00:44:14.855262Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.830662Z","title":"Arifovic, X.-z","venue":null,"work_id":"3c246d1f-9d25-4488-9417-2f4c1776d406","year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.827872Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:f8febe2bb0016a72b5bcc1a2642ff33b18bf888eba3ea1912e05787f1b2dd80e","observation_id":"b99c63b3-6bd3-41f2-82c1-a5eb832e963c","resolution":{"observed_at":"2026-08-11T00:44:14.838098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.812712Z","title":null,"venue":null,"work_id":"a3dcefb3-4f32-4243-8ecc-4c0dafecd122","year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.833579Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:49daf6a9f234640b515e00e650e4ee87617d3670d39a5f323caa6c5912062c4b","observation_id":"3441359a-d20f-4632-ade2-30033c7072f9","resolution":{"observed_at":"2026-08-11T00:44:14.817715Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.796900Z","title":"Moews, G","venue":null,"work_id":"9c6e49b2-fc9b-49c3-8e78-21750344f1f9","year":2020},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.839554Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:7a625c6d3e8c4605c88d04be54847026fedc441323e780a85ae35e76188968ed","observation_id":"ac2bf42e-10dd-475d-9d2d-3fb40305b63c","resolution":{"observed_at":"2026-08-11T00:44:14.801835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.777824Z","title":null,"venue":null,"work_id":"b6ad4d56-f395-4032-ac0a-0732228247b8","year":2019},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.844595Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:059c7fb1b6c42eb0136502dd5d4848d318db55caf1c42d491c382d991cc8d831","observation_id":"bf1b922e-7fec-4638-b185-8b952d3cc1a0","resolution":{"observed_at":"2026-08-11T00:44:14.785557Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.759863Z","title":"Nousi, A","venue":null,"work_id":"833fa279-0a23-48cc-92cc-5b0acd0eca88","year":2019},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.849393Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:e213ea546d6b1b6f365a8446323d2c2a02867a7bc8bc2eeb182778cbedf19706","observation_id":"95b7c787-7f0c-4309-a38a-5e4b178fe3fa","resolution":{"observed_at":"2026-08-11T00:44:14.765075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.08101","last_updated":"2025-03-21T17:31:44Z","snapshot_observed_at":"2026-08-13T00:08:35.976951Z","submitted_at":"2024-05-13T18:28:39Z","title":"Data-driven measures of high-frequency trading","version":3},"cited_work":{"arxiv_id":"2405.08101","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.08101","snapshot_observed_at":"2026-08-11T00:44:14.121399Z","title":"Data-driven measures of high-frequency trading","venue":"q-fin.CP","work_id":"ffebd1c2-df30-40c2-bfcf-bba16ba22087","year":2024},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.854080Z"},"links":{"cited_paper":"/paper/2405.08101","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:9b92538020a2c97a8bec6ed96a5142064f3a5518d65eca9fec0e7a84fa9c3de3","observation_id":"8ca2da6d-2a5c-4bb7-9511-333e9d845ecb","resolution":{"observed_at":"2026-08-11T00:44:14.126829Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.742218Z","title":"M¨ akinen, J","venue":null,"work_id":"2a187b00-7d27-479f-b35e-395c5be48296","year":2019},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.859857Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:e34bcebb20bef413feb02f9861132a8b4d67efea9e679d10ba3829d4c3d482c7","observation_id":"93b89a94-e7e9-4df4-b197-3d5859835c5c","resolution":{"observed_at":"2026-08-11T00:44:14.748028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.721594Z","title":null,"venue":null,"work_id":"dcfc7f07-4079-43b2-8c65-bed8591953fa","year":2001},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.864533Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:2e7ad60a18d8c8012449ae710b3640b69965a5aaa6fa53629f508b7dc755ecce","observation_id":"0d2a4798-a431-484d-8e69-7dcc0b9c1372","resolution":{"observed_at":"2026-08-11T00:44:14.729302Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.700392Z","title":null,"venue":null,"work_id":"3a2c031e-e439-445f-9f45-3d114ffa2af2","year":2016},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.869166Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:6f5a207951d1b74490bf9c9cc49a93a198a305b3d261c51545eee7e83a7c7610","observation_id":"c8cf130e-58a9-49fa-8e37-16801ac7cb25","resolution":{"observed_at":"2026-08-11T00:44:14.707010Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08359","last_updated":"2023-09-10T19:20:43Z","snapshot_observed_at":"2026-08-13T22:06:46.654756Z","submitted_at":"2023-01-19T23:32:51Z","title":"Domain-adapted Learning and Interpretability: DRL for Gas Trading","version":3},"cited_work":{"arxiv_id":"2301.08359","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.08359","snapshot_observed_at":"2026-08-11T00:44:14.096564Z","title":"Domain-adapted Learning and Interpretability: DRL for Gas Trading","venue":"q-fin.TR","work_id":"568b3428-e825-41c3-985c-e394b56c2ca8","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.873561Z"},"links":{"cited_paper":"/paper/2301.08359","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:0af18f1e4a885246a5e0f9440e86dec7f6c5b1bd4655098cb0b14059bf8b5720","observation_id":"d55c5211-89e4-449c-ab72-02efac36cb3b","resolution":{"observed_at":"2026-08-11T00:44:14.101928Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.679343Z","title":null,"venue":null,"work_id":"3eec1a18-509a-4ba6-a244-cdb77c84785c","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.879868Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:5f4cbe7d6bcfa3d45b21de0b388cbe6f453e6bfc57d311e92c6c168c8f18af11","observation_id":"8cdf96ea-47b5-4c72-a08d-cae121fb30e2","resolution":{"observed_at":"2026-08-11T00:44:14.685067Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.662431Z","title":"Karpe, J","venue":null,"work_id":"6d3b3951-4f05-4bca-b181-c13ed860b570","year":2020},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.884496Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:6984d1c7d85a57d1098892b6de57aa2807618db6a723035d926bd7f0f69ec2ef","observation_id":"f151447d-9bb6-4ee9-88e8-fcb1615cd029","resolution":{"observed_at":"2026-08-11T00:44:14.667920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.637712Z","title":"Philip, Estimating permanent price impact via machine learning, Journal of Econometrics 215 (2) (2020) 414–449","venue":null,"work_id":"5aeb7db4-0f59-439f-a705-6bf1bbbb0016","year":2020},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.890382Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:9f0e3e0dde54553532f04523c3d7325a6cd1099333f2ec268f2271cfd4fb0a37","observation_id":"3b9bb109-f16c-4e88-aa2e-b1d4544c6ff1","resolution":{"observed_at":"2026-08-11T00:44:14.647061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.619454Z","title":null,"venue":null,"work_id":"2f9be035-4d8e-4af0-880e-24bd9d99eb55","year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.895904Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:30fdcfdeb3b4ab3bd1f4ad2b0c3e5f4c8d45862a2eea9ddbb7fa6c9f4f5a19fd","observation_id":"654c9f8f-50e8-4c2e-8d35-a8f1b8639a30","resolution":{"observed_at":"2026-08-11T00:44:14.624895Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.601638Z","title":null,"venue":null,"work_id":"6d603831-861d-49a0-88ef-9aa1f9b49e0b","year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.900643Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:7bee1f2462e81f5d4fb867c8510c95b4625f3207828e2f3c2bae83de78bb3aa7","observation_id":"6c2e25d2-ff09-47f6-9cef-a90823d24089","resolution":{"observed_at":"2026-08-11T00:44:14.607146Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.584730Z","title":"Tsantekidis, N","venue":null,"work_id":"7c24406d-c955-4bd5-a2d0-40c49decd594","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.906038Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:39bc646b342bbbec50401ebb8c92cf1100a22f5251c3422514bb9f2a81d85a1b","observation_id":"b106be03-591b-4d41-90e5-ba3940434e71","resolution":{"observed_at":"2026-08-11T00:44:14.590361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.567644Z","title":null,"venue":null,"work_id":"1e13cd0b-9db2-4649-bbc6-6d0c0272eeb7","year":2021},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.910973Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:fd1d597e950e66d65244024b92931cd4f264240670ab80f8a217b45ffb17650f","observation_id":"9c05c90f-30ef-4d5f-9cb7-1fb95dc9be2b","resolution":{"observed_at":"2026-08-11T00:44:14.573972Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.547332Z","title":null,"venue":null,"work_id":"0c77074b-c380-4dff-bac5-9bbec00f4b45","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.915933Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:7589e1ee7157b9d1152e79702695d4d57db0a192324c6ee073ee8993108a4d95","observation_id":"29f359fd-78f4-4484-b161-98e93a0aed19","resolution":{"observed_at":"2026-08-11T00:44:14.552453Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.527473Z","title":null,"venue":null,"work_id":"0f4d3bd1-abb4-420d-9d59-c946e8e87d2b","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.922206Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:ffa30ac11813d85f069d6aec31fef57ed2763b9599a1fc4e761663aa4ea16a7f","observation_id":"7ca7d325-6f4b-41ab-9ba0-9c9f7c703035","resolution":{"observed_at":"2026-08-11T00:44:14.532678Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.501046Z","title":"Fathinezhad, P","venue":null,"work_id":"8b983cfb-0723-40c0-8989-f17ca0842c58","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.927353Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:6d8658b1b5a85205d8bfed439157933aec91b4527a0409a1fe534ebad7d87dc3","observation_id":"b4534b27-c0d2-48d6-a97f-77a965970962","resolution":{"observed_at":"2026-08-11T00:44:14.511315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.482131Z","title":"Elbaz, A","venue":null,"work_id":"c71561a7-92d9-42c2-ae2d-40ba274be3e6","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.932275Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:3cd2628ffcc28e01026276e6d5815da426305c286f00b9440594d009987fdd86","observation_id":"8c6ad659-ce59-4c84-a55d-241930ca6e5a","resolution":{"observed_at":"2026-08-11T00:44:14.487671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.00260","last_updated":"2019-09-04T19:30:00Z","snapshot_observed_at":"2026-08-14T19:23:21.694743Z","submitted_at":"2018-11-01T07:02:45Z","title":"Horizon: Facebook's Open Source Applied Reinforcement Learning Platform","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.00260","snapshot_observed_at":"2026-08-11T00:44:13.937694Z","title":"Gauci, E","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.937694Z"},"links":{"cited_paper":"/paper/1811.00260","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:05ebac69de0dac8e40090b29fbd95bdd5fc6c2ec2c5b0a0e0eda02181128c56c","observation_id":"6b5b4348-cc1d-4e48-95ff-820d138ec647","resolution":{"observed_at":"2026-08-11T00:44:13.937694Z","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":"2026-08-11T00:44:14.465726Z","title":null,"venue":null,"work_id":"10564aa8-2713-4408-bd75-cbf7d2e47f71","year":2022},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.943046Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:5aa03bf260227d444fb1d67e650f49a61183ba9759423b79fe8ff37dcd790f1c","observation_id":"c6761a88-5c92-42e7-8620-e1d4ea29932a","resolution":{"observed_at":"2026-08-11T00:44:14.471209Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.448245Z","title":"Saadallah, K","venue":null,"work_id":"de059cd9-fe00-47d3-adce-c40932e3ac59","year":2021},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.948210Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:fc67a97e44dfdd19ae70c4f3bcd9c54b3d5ab6544b515f8d8442a48c0dc7a05b","observation_id":"5f1777bb-4a26-4104-a9ac-8a4cf12916fd","resolution":{"observed_at":"2026-08-11T00:44:14.454027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.429704Z","title":"Kuremoto, M","venue":null,"work_id":"6795cac4-f774-400f-b2c7-9b04b13f3a77","year":2007},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.954046Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:62f005de03c215c0305e7491c7cf81ab14de005f5a913d6ad66524b31838cddb","observation_id":"2c5e6084-92e7-4032-8f2d-d450493912a3","resolution":{"observed_at":"2026-08-11T00:44:14.435870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.412004Z","title":"Hirata, T","venue":null,"work_id":"48d3cc26-6858-460d-9afa-5456d904a77e","year":2018},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.959061Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:773c27d7b6c61995ed44d0e54f3738cd67c9abe65d98c329889a9e47f067e267","observation_id":"84eab95a-cf4f-44e3-96d0-6d80269d077c","resolution":{"observed_at":"2026-08-11T00:44:14.417479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.393191Z","title":"Zhuang, V","venue":null,"work_id":"81e46d72-abea-48be-86de-e64b696b1cb3","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.964078Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:76ffa6f59318a90441ded2b5f98bbcd4acf7dc2973c6117cad684252be56191b","observation_id":"a24791ed-6b05-4eb8-b328-6ef8ba1bcecc","resolution":{"observed_at":"2026-08-11T00:44:14.398911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.375200Z","title":null,"venue":null,"work_id":"11c29f6d-c87e-418d-b01e-e503f033eea5","year":2005},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.968876Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:b741c6684dfc76c86ad10edd134415c13d64eb66d89237e2015976294411349b","observation_id":"c6a4c6dc-de5c-40fb-90d3-bf0e539ac81d","resolution":{"observed_at":"2026-08-11T00:44:14.379974Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:44:14.359301Z","title":"Ntakaris, G","venue":null,"work_id":"a380a789-3542-46aa-a296-205af15b4d91","year":2019},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.973508Z"},"links":{"citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:f6807ebc7f4a1d332b0c7defacd839bd6d466d77dcc7a56d47a494847e05a324","observation_id":"c286d5b0-61cc-417d-a8be-270dacec4c61","resolution":{"observed_at":"2026-08-11T00:44:14.364265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09840","last_updated":"2023-05-15T17:01:15Z","snapshot_observed_at":"2026-08-13T12:00:53.835647Z","submitted_at":"2023-04-17T14:51:03Z","title":"Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting","version":3},"cited_work":{"arxiv_id":"2304.09840","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.09840","snapshot_observed_at":"2026-08-11T00:44:14.054589Z","title":"Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting","venue":"cs.LG","work_id":"1bb99d54-cd43-442d-ac3f-9fcce09717ab","year":2023},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.978512Z"},"links":{"cited_paper":"/paper/2304.09840","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:7f8db8b8508ffa1d29bc036087c2c4fb519669c49f2a66f620ddea2786bfa4ef","observation_id":"d1d040d0-f9b1-45ed-9d7a-b82dd0df2e0c","resolution":{"observed_at":"2026-08-11T00:44:14.062146Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T00:44:13.983638Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T00:44:13.983638Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.19372"},"observation_digest":"sha256:6e7bdfb8061fd4186d95c6d08fe953c0fac825ccad61ff30edce082ed5a786cf","observation_id":"ba97e26f-02d1-475a-855d-2654acd4e99b","resolution":{"observed_at":"2026-08-11T00:44:13.983638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.19372","last_updated":"2024-12-30T23:48:35Z","latest_version":2,"primary_category":"q-fin.ST","snapshot_observed_at":"2026-08-15T19:03:16.589904Z","submitted_at":"2024-12-26T22:49:53Z","title":"Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":4,"verified_fuzzy":17},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2412.19372."}