{"as_of":"2026-08-10T05:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44aec4edc9702aeba0c70ead39c71ae68cd0f2bd30d93d3acb66d19270476cda","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T21:57:15.401641Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"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/2502.09318/citation-record","integrity":"/paper/2502.09318/integrity","json":"/paper/2502.09318/citation-record.json","paper":"/paper/2502.09318"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T21:57:15.764662Z","title":null,"venue":null,"work_id":"581d0e2b-6d39-4720-a26f-14257dcaaac7","year":2000},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.307861Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:2f50b19bffd9a0db9d468098196e47381f9d7be1f2bef4ca5ffe3e203c2513e5","observation_id":"b4314190-415e-4cca-a6c8-a3d3515c51fd","resolution":{"observed_at":"2026-08-07T21:57:15.768363Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T21:57:15.312499Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.312499Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:7703ac08458ab4a6bb357c574d84cf12820a09c40c1c1811174ed76212ec553b","observation_id":"a54b6a8f-ab8b-4fc0-ab84-2f222088f5c1","resolution":{"observed_at":"2026-08-07T21:57:15.312499Z","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":"2026-08-07T21:57:15.746447Z","title":"Essai d’une recherche statistique sur le t exte du roman “Eugene Onegin","venue":null,"work_id":"403243ac-1949-4323-8e50-0a2d768c7c1b","year":1913},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.316424Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:f85e8c54b3b20a49c03f565aaf1f0c499600aec87c2ebb2e799bf4238495d600","observation_id":"b619cb70-5595-4c27-b382-2202adbcfde9","resolution":{"observed_at":"2026-08-07T21:57:15.750759Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T21:57:15.320697Z","title":"Deep learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.320697Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:e69798997e6aee93c792b3ac1fed7e769c1e6b746126564d719dd006474f921e","observation_id":"b86ad47d-3416-4d85-8ecf-fded59f1c5ef","resolution":{"observed_at":"2026-08-07T21:57:15.320697Z","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":"2026-08-07T21:57:15.326353Z","title":"Medsker and L","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.326353Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:4ea2a1e2174e270dcd1589d752e03611436ad64637dba51875c7382eb9049b92","observation_id":"9126719b-b525-4187-ae9e-17bb9a5a09ec","resolution":{"observed_at":"2026-08-07T21:57:15.326353Z","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":"2026-08-07T21:57:15.330888Z","title":"Time-series forecasting with deep learning: a survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.330888Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:c51342dc9c49297561fe86638546a4a8b54c960dc775243e0cf1ff8791388922","observation_id":"2efa4116-426f-4f2c-bb35-4b0974a9533b","resolution":{"observed_at":"2026-08-07T21:57:15.330888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07344","last_updated":"2025-07-14T14:09:59Z","snapshot_observed_at":"2026-07-06T18:13:16.171803Z","submitted_at":"2024-05-12T17:40:48Z","title":"TKAN: Temporal Kolmogorov-Arnold Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07344","snapshot_observed_at":"2026-08-07T21:57:15.335214Z","title":"Tkan: Temporal kolmogorov-a rnold net- works,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.335214Z"},"links":{"cited_paper":"/paper/2405.07344","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:3dd51b6079a9107e7720b2fcbade6efb914df107bcb38f5679aa2b57ab25c5af","observation_id":"531c15d7-e2d4-437b-8ac5-d44e4df496cc","resolution":{"observed_at":"2026-08-07T21:57:15.335214Z","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":"2026-08-07T21:57:15.715478Z","title":"Temporal fusi on transform- ers for interpretable multi-horizon time series forecasti ng,","venue":null,"work_id":"c6c4665f-c3be-45bc-aa8a-eb7776249c1b","year":2021},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.339441Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:1b14ff1e3c1e6aa8673e2d81070e0433c1265cfd0e661df2c148b3a50b4a6c4e","observation_id":"5cb3e872-c682-4179-8b46-71c0c66f6935","resolution":{"observed_at":"2026-08-07T21:57:15.719432Z","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":"2406.02486","last_updated":"2024-06-05T16:32:16Z","snapshot_observed_at":"2026-08-09T10:30:42.403433Z","submitted_at":"2024-06-04T16:55:42Z","title":"A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02486","snapshot_observed_at":"2026-08-07T21:57:15.342875Z","title":"A temporal kolmogorov-arnol d transformer for time series forecasting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.342875Z"},"links":{"cited_paper":"/paper/2406.02486","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:9f1d230607d4d93331112fcc6bf5f8c4311e77d008533d07862ae11ca5f1f2f8","observation_id":"c124a726-829f-416b-ac07-a95db21e25be","resolution":{"observed_at":"2026-08-07T21:57:15.342875Z","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":"2026-08-07T21:57:15.346614Z","title":"Long short-term mem ory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.346614Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:517ed9fef318983f968317ee87301a937b23b797fde6f8dd8416274d3a84d773","observation_id":"93efb85b-8082-4d48-90cb-44d0a84e2671","resolution":{"observed_at":"2026-08-07T21:57:15.346614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.1078","last_updated":"2014-09-03T00:25:02Z","snapshot_observed_at":"2026-07-06T03:45:28.546418Z","submitted_at":"2014-06-03T17:47:08Z","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.1078","snapshot_observed_at":"2026-08-07T21:57:15.350157Z","title":"Learning phrase representation s using rnn encoder-decoder for statistical machine translation,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.350157Z"},"links":{"cited_paper":"/paper/1406.1078","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:fe8fa24248e80c06d7cbd1444ffbba7be4809eb7cab23fe66ffe9048980e14c3","observation_id":"414c11ff-cb97-4733-9b48-e85ac815e0ef","resolution":{"observed_at":"2026-08-07T21:57:15.350157Z","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":"2026-08-07T21:57:15.696004Z","title":"Integration of paths–a faithful represen tation of paths by noncommutative formal power series,","venue":null,"work_id":"507395f7-e3cc-46ce-87d6-9afb65b1b8e5","year":1958},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.354174Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:8d004e49206714dd0c4bed7e9519787bea4b8a29bf86d3445c83b397cf0074b9","observation_id":"aa18f5ad-cf33-4c66-80c3-e9d75fbd7bfb","resolution":{"observed_at":"2026-08-07T21:57:15.700302Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T21:57:15.357637Z","title":"A primer on the signatu re method in machine learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.357637Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:27888290b13a96ee43539d803479b5f126d19d673c9d0f87f6ae9ccb111bf8fd","observation_id":"cb4bc40d-fcf3-48fd-aa30-1d3fa64b06fa","resolution":{"observed_at":"2026-08-07T21:57:15.357637Z","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":"2026-08-07T21:57:15.684185Z","title":"Embedding and learning with signatures ,","venue":null,"work_id":"09cdb884-b1c8-4e28-9b21-bc34706f9f87","year":2021},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.360973Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:79f5a45532a77dfdad4baade1ab05af5c566db8af53cc96d887ad8b270dae7b7","observation_id":"35d4b64b-d317-4017-bb19-943fb2a7f66d","resolution":{"observed_at":"2026-08-07T21:57:15.688143Z","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":"1307.7244","last_updated":"2014-07-15T09:44:41Z","snapshot_observed_at":"2026-07-06T03:19:17.715143Z","submitted_at":"2013-07-27T10:13:04Z","title":"Extracting information from the signature of a financial data stream","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1307.7244","snapshot_observed_at":"2026-08-07T21:57:15.364310Z","title":"Ext racting information from the signature of a ﬁnancial data stream,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.364310Z"},"links":{"cited_paper":"/paper/1307.7244","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:4cc2e8a8a12f36a4cce22482438eb61aa08597cbcb35b35fe43392ef8c26d45c","observation_id":"cf91baf5-bec0-4a18-96d8-a6a19bb43fd5","resolution":{"observed_at":"2026-08-07T21:57:15.364310Z","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":"2026-08-07T21:57:15.671973Z","title":"Deep signature s tatistics for likelihood-free time-series models,","venue":null,"work_id":"9b620fff-a5fa-4289-b9b6-1ec918ea6874","year":2021},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.368100Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:7cd1675182a9dab4d5d369b2fdf6e9a6619236f82ec0768e05f7cfcfe6d90c48","observation_id":"a5991eb7-d285-4eed-bb94-373bd48e6f67","resolution":{"observed_at":"2026-08-07T21:57:15.676602Z","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":"2406.17890","last_updated":"2024-12-09T16:37:12Z","snapshot_observed_at":"2026-08-09T23:58:32.021035Z","submitted_at":"2024-06-25T18:58:39Z","title":"SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17890","snapshot_observed_at":"2026-08-07T21:57:15.371556Z","title":"Sigkan: Signature-weighte d kolmogorov- arnold networks for time series,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.371556Z"},"links":{"cited_paper":"/paper/2406.17890","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:551be87c49c2c17c6efd8daa18e23ee5814f54329a28637d1027c877cdce0615","observation_id":"00a3f213-aeb3-493b-a2e1-6d8f40d80e73","resolution":{"observed_at":"2026-08-07T21:57:15.371556Z","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":"2026-08-07T21:57:15.660727Z","title":"Improving the gating mechanism of recurrent neural networks,","venue":null,"work_id":"21f3ec84-2888-49eb-84f7-5c6e2e9fe5f5","year":2020},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.375351Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:16d87b1c8caf6cbfd93322d6b1267b864aca64b68ab324a60258575632d67660","observation_id":"c38798b3-14c1-4482-ad8f-49d0d996bc85","resolution":{"observed_at":"2026-08-07T21:57:15.664518Z","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":"2026-08-07T21:57:15.649455Z","title":"Simpliﬁed gating in long short-te rm memory (lstm) recurrent neural networks,","venue":null,"work_id":"0c8bb5a2-f3e6-44d5-b8f3-53f8b6ee6df3","year":2017},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.378917Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:3ed77f3e41a4f075819dbd16dc0c94848c82bf99b6e4de518ae699d7d79d92fc","observation_id":"bbc4f072-7c06-4989-a0b0-eb3938422022","resolution":{"observed_at":"2026-08-07T21:57:15.653381Z","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":"2002.11338","last_updated":"2020-05-26T13:59:48Z","snapshot_observed_at":"2026-08-09T08:48:32.552731Z","submitted_at":"2020-02-26T07:51:38Z","title":"Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.11338","snapshot_observed_at":"2026-08-07T21:57:15.382362Z","title":"Reﬁned gate: A simple and effective gating mechanism for recurrent units,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.382362Z"},"links":{"cited_paper":"/paper/2002.11338","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:784c5685409ce679af5ecce71bf184b3e1ae288d1af1ce57ca8f00aac29012d6","observation_id":"ad306ab5-2e71-4043-9f86-3fc5eb869bcb","resolution":{"observed_at":"2026-08-07T21:57:15.382362Z","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":"2026-08-07T21:57:15.637233Z","title":"Differential equations driven by rough si gnals,","venue":null,"work_id":"847168a3-4b62-4853-ba42-5da8036d488e","year":1998},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.386323Z"},"links":{"citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:652e4e1bb188cc26ef243c83b20ee92ea0aa9b5399ad26874266ffafa83fad46","observation_id":"26b0b727-bf9a-44d1-b257-52549d56d9bc","resolution":{"observed_at":"2026-08-07T21:57:15.641316Z","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":"2011.10630","last_updated":"2020-11-20T20:42:13Z","snapshot_observed_at":"2026-08-09T18:50:03.236431Z","submitted_at":"2020-11-20T20:42:13Z","title":"Solving path dependent PDEs with LSTM networks and path signatures","version":1},"cited_work":{"arxiv_id":"2011.10630","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.10630","snapshot_observed_at":"2026-08-07T21:57:15.475498Z","title":"Solving path dependent PDEs with LSTM networks and path signatures","venue":"q-fin.CP","work_id":"4b95297b-4451-407a-a2d9-9a87bc033285","year":2020},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.389866Z"},"links":{"cited_paper":"/paper/2011.10630","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:ee1baa738d3eb4d17ec6d96babff5bbd69ad0e4b0daa1cfc6fbee7ec84d43c3e","observation_id":"f30bad8f-7d08-478b-8460-1eaf3a9a4f76","resolution":{"observed_at":"2026-08-07T21:57:15.479752Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2403.10288","last_updated":"2024-03-15T13:29:45Z","snapshot_observed_at":"2026-08-05T16:08:56.338804Z","submitted_at":"2024-03-15T13:29:45Z","title":"Rough Transformers for Continuous and Efficient Time-Series Modelling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.10288","snapshot_observed_at":"2026-08-07T21:57:15.393921Z","title":"Rough transformers for continuous and efﬁcient time-series mode lling,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.393921Z"},"links":{"cited_paper":"/paper/2403.10288","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:f90c43a0c0a0f9d105af55f57de4422b412ce3ee9f6b20d1bf22af76ab639f8b","observation_id":"bd009a60-b551-4882-a0bf-8a32022a66dc","resolution":{"observed_at":"2026-08-07T21:57:15.393921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23297","last_updated":"2024-10-15T17:17:02Z","snapshot_observed_at":"2026-07-06T19:42:25.742756Z","submitted_at":"2024-10-15T17:17:02Z","title":"Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction","version":1},"cited_work":{"arxiv_id":"2410.23297","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.23297","snapshot_observed_at":"2026-08-07T21:57:15.449165Z","title":"Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction","venue":"q-fin.PM","work_id":"9212da2f-924d-467a-82b3-e12fcf1092ec","year":2024},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.397688Z"},"links":{"cited_paper":"/paper/2410.23297","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:3f361444d7b5cc4645ab9468ee37a2c2a17c8d1e2a6b2f2967b230f02ccc5e31","observation_id":"35780616-e75f-459e-874f-16cda7f1559d","resolution":{"observed_at":"2026-08-07T21:57:15.453315Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2501.08455","last_updated":"2025-01-14T22:00:01Z","snapshot_observed_at":"2026-08-09T01:58:24.910810Z","submitted_at":"2025-01-14T22:00:01Z","title":"Keras Sig: Efficient Path Signature Computation on GPU in Keras 3","version":1},"cited_work":{"arxiv_id":"2501.08455","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.08455","snapshot_observed_at":"2026-08-07T21:57:15.430885Z","title":"Keras Sig: Efficient Path Signature Computation on GPU in Keras 3","venue":"cs.LG","work_id":"e05158da-cab0-432f-8189-e6a1e90ed19b","year":2025},"citing_paper":{"arxiv_id":"2502.09318","last_updated":"2025-02-13T13:33:35Z","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T21:57:15.401641Z"},"links":{"cited_paper":"/paper/2501.08455","citing_paper":"/paper/2502.09318"},"observation_digest":"sha256:4e88b9a14eae5a6fb616e9c3bfc3e0b222d616dbeb9fb16bf0ef6aa7edcd3760","observation_id":"7b0708b1-233f-466e-b631-4ead75568dc0","resolution":{"observed_at":"2026-08-07T21:57:15.437437Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2502.09318","last_updated":"2025-02-13T13:33:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T10:31:33.766515Z","submitted_at":"2025-02-13T13:33:35Z","title":"SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":3,"verified_fuzzy":8},"total_outbound_references":25},"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 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2502.09318."}