{"as_of":"2026-08-09T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b432d4c8077c1d7d962d414392e56872593011952af3cac2fd3c31cb9a57fa46","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T19:50:20.268930Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.00154/citation-record","integrity":"/paper/2607.00154/integrity","json":"/paper/2607.00154/citation-record.json","paper":"/paper/2607.00154"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.130581Z","title":"itransformer: Inverted transformers are effective for time series forecasting,","venue":null,"work_id":"66afb61e-d0c5-4cc6-956c-7fa9a073d05a","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:60718e2df58cb90313811dd8933c9313f50d893212ebe6cece885fd6f3a21424","observation_id":"77021c45-012d-4b3a-9bff-a82b9cda046d","resolution":{"observed_at":"2026-07-05T20:51:25.131993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T14:07:06.849417Z","title":"Attention is all you need","venue":null,"work_id":"4f585692-8ef8-4f20-8f75-5e0e32647d76","year":2017},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:57d396e6b2322a9937f0e914c49fe66b8e9a1621899288b1a4268ba3de833cbb","observation_id":"cb91e6b0-73b1-44a3-bf4e-2e3253d5f714","resolution":{"observed_at":"2026-07-05T20:51:25.137709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.141864Z","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting,","venue":null,"work_id":"7a4942c4-67df-48fc-89be-c2e582ba67e7","year":2021},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:815fc09c121feecb1257bcd3a391a0c3527fd8bd46f9d4c355e2814d7ecad435","observation_id":"7eef35e6-4372-48bc-aa23-f89b1eaf5d96","resolution":{"observed_at":"2026-07-05T20:51:25.143107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.137899Z","title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting,","venue":null,"work_id":"7f42dbe5-eb17-497f-96a0-d9cf3e49603b","year":2021},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:fd6590a3ff4ff09215b526f224e0e4e6056544e408fa6fd3c36d177ccea30190","observation_id":"1efcd500-81bb-424d-9e71-a06801cecdfd","resolution":{"observed_at":"2026-07-05T20:51:25.139240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.127197Z","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,","venue":null,"work_id":"97f3f8c9-8ce2-4a5e-bcb0-cdd88709773e","year":2021},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:c9ab07bd1862b2c8ce08f91f9c4f59d44b39a48c0260388f652cbbf14e54879a","observation_id":"57b7ad02-7ec3-41de-bc61-d3a8d32e6c2b","resolution":{"observed_at":"2026-07-05T20:51:25.128720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.169808Z","title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,","venue":null,"work_id":"fd1c238c-c15c-4868-a876-90d03300ad9a","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:f95740c90e2e7ccddd2fad715a5cae4c22e4e9798ac34fcb66fde2dc1ffe1fef","observation_id":"75d40202-a526-4ae3-af48-1049fe5585a4","resolution":{"observed_at":"2026-07-05T20:51:25.171492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.111724Z","title":"Large language models are zero-shot time series forecasters,","venue":null,"work_id":"2db58a44-f481-4f26-9515-5805e97827b9","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:696b90df2d69064c76a4f2f49b333eef7d79c8bd1fe6147b9eb33e7c553efa93","observation_id":"e645d206-01ff-49d4-82e1-281aad3c8d7f","resolution":{"observed_at":"2026-07-05T20:51:25.113076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T11:23:43.307820Z","title":"Promptcast: A new prompt-based learning paradigm for time series forecasting","venue":null,"work_id":"b4c80410-249b-40cf-ad91-207953d6cf70","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:11515f288fb954a04c74db2b8c028846800a7032aab1e93c2b05f8dc8c6ee580","observation_id":"d60882e7-ef1d-4414-b064-4fbcf58d0313","resolution":{"observed_at":"2026-07-05T20:51:25.149357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.149953Z","title":"One fits all: Power general time series analysis by pretrained lm","venue":null,"work_id":"2d4099bf-801e-43ce-8471-5c300c5a7e03","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:2c83b5890214e574e7724a81380a6ba4b92679c4669769c088ddd98b8d6f632e","observation_id":"115d8808-9d30-4822-a816-515639d57d65","resolution":{"observed_at":"2026-07-05T20:51:25.151987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-04T04:31:27.172482Z","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":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:ea8ce2c29d343480324a503e65a5c5518b6871eff72b60433ebe06aac84f8542","observation_id":"2af6aed2-6fff-40d7-9034-72be957122b4","resolution":{"observed_at":"2026-07-02T19:57:19.187186Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.165852Z","title":"Lag-llama: Towards foundation models for time series forecasting,","venue":null,"work_id":"3a7afb77-4540-4edc-a60e-48d3bdd4c0c4","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:b843632dc8052fa0d6d45b16ef79c3caaa817dd526644a86bb6619e366f12aee","observation_id":"043d1434-6f61-41ab-a9a7-f2e3c4e56436","resolution":{"observed_at":"2026-07-05T20:51:25.167307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03885","last_updated":"2024-10-10T15:37:45Z","snapshot_observed_at":"2026-08-07T22:30:00.741959Z","submitted_at":"2024-02-06T10:48:46Z","title":"MOMENT: A Family of Open Time-series Foundation Models","version":3},"cited_work":{"arxiv_id":"2402.03885","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03885","snapshot_observed_at":"2026-07-03T17:38:43.284760Z","title":"Moment: A family of open time-series foundation models","venue":null,"work_id":"26ab8b80-a36b-488c-876d-c5b25358a5b7","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"cited_paper":"/paper/2402.03885","citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:7a24b388609d1d25383c2be734de62c7c8ff309a0b830bda3b765e79c34b6608","observation_id":"d76ec23a-d820-4666-88fa-29e55b0b6872","resolution":{"observed_at":"2026-07-02T19:57:19.185496Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.145341Z","title":"A decoder-only foundation model for time-series forecasting,","venue":null,"work_id":"dd5b4b10-1289-41b0-910f-a695bc3e76fd","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:9751dce74bdfbae4b613be19564123a2298e690390054bab94745edc73da40c2","observation_id":"eada2218-fd46-4ad8-8bf6-28b33ed3c55c","resolution":{"observed_at":"2026-07-05T20:51:25.146833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02592","last_updated":"2024-05-22T11:49:59Z","snapshot_observed_at":"2026-08-05T03:13:48.347682Z","submitted_at":"2024-02-04T20:00:45Z","title":"Unified Training of Universal Time Series Forecasting Transformers","version":2},"cited_work":{"arxiv_id":"2402.02592","doi":"10.48550/arxiv.2402.02592","metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02592","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Unified training of universal time series forecasting transformers","venue":"arXiv (Cornell University)","work_id":"278b5549-7509-4fdf-8c12-bbd81a9b9070","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"cited_paper":"/paper/2402.02592","citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:d996b48d729efb74356e834e57b70689cfd419947f14142ef866b3683beccba9","observation_id":"a433b5f0-9a98-4d16-a4a9-57cef6cb949f","resolution":{"observed_at":"2026-07-02T19:57:19.191338Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.143446Z","title":"Chronos: Learning the language of time series,","venue":null,"work_id":"fd71f903-43bc-4521-bc1d-6b5b2c7e4a0f","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:fbb2eb0a0399746fb2ac9026b56e227ac888a1a10de7f7d21523ff37cd22a6d5","observation_id":"9025bc5c-e99c-4225-8ab6-d6c5d0311475","resolution":{"observed_at":"2026-07-05T20:51:25.145044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2510.15821","last_updated":"2025-10-17T17:00:53Z","snapshot_observed_at":"2026-08-09T00:10:13.880513Z","submitted_at":"2025-10-17T17:00:53Z","title":"Chronos-2: From Univariate to Universal Forecasting","version":1},"cited_work":{"arxiv_id":"2510.15821","doi":"10.48550/arxiv.2510.15821","metadata_source":"pith","pith_arxiv_id":"2510.15821","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Chronos-2: From Univariate to Universal Forecasting","venue":"cs.LG","work_id":"c00ac266-1edf-4787-a2b3-23692973b1b8","year":2025},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"cited_paper":"/paper/2510.15821","citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:88bc9b98f1a67bb10e2890ebb880d564d6a1f460326239cc2d1d28cf6d428ae9","observation_id":"009bec6d-10bc-447c-8aa8-711f2026b1d4","resolution":{"observed_at":"2026-07-02T19:57:19.188748Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.167969Z","title":"Forecastpfn: Synthetically-trained zero-shot forecasting,","venue":null,"work_id":"e27db642-bfc3-4f6c-86a4-83e4856cf64d","year":2023},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:46b4d1b9128616770eedfa1cc3e40499404c4de6ca50f9fc7bafc838389b3296","observation_id":"b72b4224-683d-41a6-9095-82a1b84ed064","resolution":{"observed_at":"2026-07-05T20:51:25.169462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.124886Z","title":"From tables to time: How tabpfn-v2 outperforms specialized time series forecasting models,","venue":null,"work_id":"7d0ead0a-9ef4-44f6-b98a-b942cc4a0a38","year":2025},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:627293a89588e4648cf2e34b043e8a2f384654c51a5d150e4e1148e6edb8ccfd","observation_id":"b90cdbcc-ee6a-4b55-96c4-3298663fc369","resolution":{"observed_at":"2026-07-05T20:51:25.126542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.129397Z","title":"Neural architecture search: A survey,","venue":null,"work_id":"ac1d8be3-215b-424d-9d8e-6b6431808706","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:2ed9f78b442979403566ab9a4c6258ff025e58dd58576ddd745572f314e32e67","observation_id":"38d90a04-44c8-4aaa-a31b-48ec5def3bb7","resolution":{"observed_at":"2026-07-05T20:51:25.130784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T12:26:17.332313Z","title":"Evolving neural networks through augmenting topologies","venue":null,"work_id":"5d6b4a7d-c84a-4147-b04d-1dd2a378514f","year":2002},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:bbd943ff993b7b6435df413d71c2e0af0a191552692d48acfbf891b02bb478ef","observation_id":"4717f6d2-ff1c-4e8f-84d9-0b91ee61e75c","resolution":{"observed_at":"2026-07-05T20:51:25.135224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.131323Z","title":"Investigating recurrent neural network memory structures using neuro- evolution,","venue":null,"work_id":"004faa7b-aed8-46be-833f-b00c32942bc1","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:07ebe899f6accfa5b117064224a00422a39e636e100e341759f6213b440c7d43","observation_id":"ffc0e3bd-53bf-4a86-9f6d-f6f04c607c83","resolution":{"observed_at":"2026-07-05T20:51:25.132895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.141253Z","title":"Exa-gp: Unifying graph-based genetic programming and neuroevo- lution for explainable time series forecasting,","venue":null,"work_id":"26591403-adf3-4dcb-8e10-b8a81a68f2ac","year":2024},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:3b00bc875e43ddd6051a3d6f47b36e1a3c226ae816499cb6a42f0bb2adfc2b52","observation_id":"da9871b4-f892-44f0-97a5-e7c969d3fc45","resolution":{"observed_at":"2026-07-05T20:51:25.142846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.117992Z","title":"Ant-based neural topology search (ants) for optimizing recurrent networks,","venue":null,"work_id":"c973d880-7c5d-45d7-a2d4-2f3abda4a13f","year":2020},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:65fc8ac57f2719dfeee608f3f2df390f0bc31d6720403d40b88d05a398f4ca4f","observation_id":"66b78bab-82e2-421a-b25f-7ad2926497da","resolution":{"observed_at":"2026-07-05T20:51:25.119737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.118403Z","title":"Continuous ant-based neural topology search,","venue":null,"work_id":"1f641796-aeff-4be0-b983-c84c9c48109c","year":2021},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:24a40848db6429150fd92d56d5112ede9ee929a95bba13dac9b9e14643f8f9c6","observation_id":"6a4c65ea-b0c8-43ab-a9d8-130df445a753","resolution":{"observed_at":"2026-07-05T20:51:25.119993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.139807Z","title":"Cg-cants-n: A versatile graph-based framework for scalable and adaptive problem solving across domains,","venue":null,"work_id":"7bf37a83-765d-4ce2-8bbe-a62f04f6bb8a","year":2025},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:a75629755b40f414ed3fe9bec1711774ee0976e5a1573e2f899e323e346a473a","observation_id":"9fd05ddd-f0a3-4f91-b207-c8f0c93104e2","resolution":{"observed_at":"2026-07-05T20:51:25.141056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.159993Z","title":"Nsga-net: neural architecture search using multi-objective genetic algorithm,","venue":null,"work_id":"1645dd64-99c3-4de4-ad69-99aa5bd23eac","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:9bdb6583778409bfe4df773d502cc4884a6b0583abcf2c78c7f8b0d506525263","observation_id":"13528d2c-1dd6-432a-a9ba-c549ae576997","resolution":{"observed_at":"2026-07-05T20:51:25.161457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.109701Z","title":"Regularized evolution for image classifier architecture search,","venue":null,"work_id":"11c3b708-96c0-46eb-b6a8-3e99e87295d1","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:eaa82f3fa96e1f28453e6556ae97e28a92de0b6457655a8b44d2f157499631a2","observation_id":"9a5b4fd8-d1c6-474f-b728-3714d75e596a","resolution":{"observed_at":"2026-07-05T20:51:25.110964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.113390Z","title":"Efficient neural architecture search via parameters sharing,","venue":null,"work_id":"0c6c27f6-2c71-4f9d-bd36-d9faa26c4d4c","year":2018},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:179406ff14f0a2af72b5917647552b7ed1d7b7254c7a1e2fd70001d69e1767e1","observation_id":"5b5b2364-c480-44db-9557-277b8baaf514","resolution":{"observed_at":"2026-07-05T20:51:25.114824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-01T17:05:53.501765Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":"1806.09055","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-07-10T21:27:35.708872Z","title":"DARTS: Differentiable Architecture Search","venue":"cs.LG","work_id":"2f7f6e44-42e7-498e-a68f-a01e092a8903","year":2018},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:b938c00ae38dd683c67f7e50779138775387a21daf08234bc7a9ef50f9e15bb9","observation_id":"ff337bd1-d86b-40d5-9bb5-cde138f6316b","resolution":{"observed_at":"2026-07-02T19:57:19.180029Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.162113Z","title":"The evolved transformer,","venue":null,"work_id":"aa18690c-edef-4bf5-88c6-1ca56938eeff","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:d5ed7b2644f8f549e7b506e50688880d2fd72e216ffbcd97326709121dbea8d1","observation_id":"482e0b84-654a-4f9b-a23e-90c7ab470214","resolution":{"observed_at":"2026-07-05T20:51:25.163420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.159485Z","title":"Searching the search space of vision transformer,","venue":null,"work_id":"1744f73a-19c4-4732-96fc-b3f291bd0fe7","year":2021},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:e8552ee9c292bb2a75c626fb74c16ab10aae5f673cc0baa8bcf23b76bd477eaa","observation_id":"7c60d52e-3a13-4688-b404-2e46c259d3b4","resolution":{"observed_at":"2026-07-05T20:51:25.161203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.161980Z","title":"Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training,","venue":null,"work_id":"b0968ea3-bedc-4125-966a-65b6d60adb61","year":2022},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:f0659beccde633cb23b43eff2f9c25e1150e4acef4eb08380a35d121cd5fd3a3","observation_id":"0791d2e8-e1e9-4ed7-ad53-ad0c07b0a588","resolution":{"observed_at":"2026-07-05T20:51:25.163578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.167620Z","title":null,"venue":null,"work_id":"9ec5e285-7068-4487-bf42-54363e789d2e","year":1999},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:6f1282f9b4340ea97d20766745e2736f052d162d622942f1133696462d85c737","observation_id":"a771876d-633f-44f4-9bb6-c7d2bd643359","resolution":{"observed_at":"2026-07-05T20:51:25.169190Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T20:51:25.169971Z","title":"Designing neural networks through neuroevolution,","venue":null,"work_id":"02711f4b-f960-460f-8ff7-cb55e147df90","year":2019},"citing_paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-02T19:50:20.268930Z"},"links":{"citing_paper":"/paper/2607.00154"},"observation_digest":"sha256:5f3df9753301c8b00d2ef5527c356e48320f7e0670afd5fac3f08f202194bb97","observation_id":"f1e0af2a-e117-4fb4-bb95-f01b6fce7779","resolution":{"observed_at":"2026-07-05T20:51:25.171325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.00154","last_updated":"2026-06-30T20:29:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T04:06:10.497083Z","submitted_at":"2026-06-30T20:29:34Z","title":"EVOTS: Evolutionary Transformer Search for Time Series Forecasting"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":5,"verified_fuzzy":28},"total_outbound_references":34},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.00154."}