{"as_of":"2026-08-08T12:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54d9a48ff38190e2956919edeb6827339f3b109e87bfc91d56208a396ec8b15d","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:19:42.053573Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:14:57.367920Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:29:10.676654Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15572","snapshot_observed_at":"2026-08-07T05:14:57.367920Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.09085","last_updated":"2025-06-10T08:10:16Z","snapshot_observed_at":"2026-08-07T05:05:12.126967Z","submitted_at":"2025-06-10T08:10:16Z","title":"LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T05:14:57.367920Z"},"links":{"cited_paper":"/paper/2505.15572","citing_paper":"/paper/2506.09085"},"observation_digest":"sha256:a9c93e5ce74118b548bd3f01e872064bd2b7824846294837ea546a87c5bcab4c","observation_id":"29e7564c-bfe2-4701-a592-88d4d32c04dc","resolution":{"observed_at":"2026-08-07T05:14:57.367920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"cited_work":{"arxiv_id":"2505.15572","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.15572","snapshot_observed_at":"2026-08-06T21:29:10.676654Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","venue":"cs.LG","work_id":"87c66d09-3c54-4808-91ec-cb3511b9a1cd","year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-07T22:30:29.169423Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":124,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:08.214803Z"},"links":{"cited_paper":"/paper/2505.15572","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:3d432ab6eba3f4cad38fc66be851bc7b521fd52b934be4c063c30e52d6c80039","observation_id":"5ef5456a-03e5-46f3-9537-d6d86acd025b","resolution":{"observed_at":"2026-08-06T21:29:10.777100Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2505.15572/citation-record","integrity":"/paper/2505.15572/integrity","json":"/paper/2505.15572/citation-record.json","paper":"/paper/2505.15572"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:50.420669Z","title":"Artificial intelligence in physical sci- ences: Symbolic regression trends and perspectives","venue":null,"work_id":"901d59e9-fecc-478e-ae3b-08901e031a8b","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:37.474831Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:b9a7f4a29d48d9e976dbf1985376ac574bc5f5d70192efd8b6dfdfc6878cbec0","observation_id":"6d540d20-0708-4e13-8ad5-a0b8610dec64","resolution":{"observed_at":"2026-08-07T15:19:50.532832Z","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":"2026-08-07T15:19:50.138595Z","title":"Multiple regression genetic programming","venue":null,"work_id":"a1eb1409-8b3a-4c6a-8a03-b50612a36fcd","year":2014},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:37.610745Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:de88b64004e68a13d562ae3250c6593fd17fa77aea434a68b645e67838dbad7c","observation_id":"19a99033-cbf2-4f1f-af07-a94b8ebd05aa","resolution":{"observed_at":"2026-08-07T15:19:50.297292Z","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":"2026-08-07T15:19:49.910779Z","title":"Gorec: a generative cold-start recommendation framework","venue":null,"work_id":"65410ce6-5708-423b-839d-704b5134be33","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:37.801282Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:127093a7abc6413e4b92461f36c37a6ba48bfe5df33ec0c0837d7001b239c6bb","observation_id":"f68fd1d8-ad59-489b-88ad-1882c7305709","resolution":{"observed_at":"2026-08-07T15:19:50.027271Z","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":"2026-08-07T15:19:49.701448Z","title":"Multimodality invariant learning for multimedia-based new item recommendation","venue":null,"work_id":"188fb525-8347-45ea-9300-aebb6a76b648","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:37.965622Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:17ab879bf9f82cc8aa33dbb15b4fbc8198541fe2542c5a9ff273378396f04b10","observation_id":"b9b87f5c-ef33-45a0-a458-3aa7448c04df","resolution":{"observed_at":"2026-08-07T15:19:49.791151Z","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":"2026-08-07T15:19:49.509903Z","title":"Neural symbolic regression that scales","venue":null,"work_id":"23348af8-98ea-47e5-8093-23961c53e666","year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.147485Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4d8b120ea0ecdcdbca4257d938af1113e7c7231a81d7763240ebfc77912b9fa8","observation_id":"f4563260-b921-4f68-b28c-e4bd48722a1a","resolution":{"observed_at":"2026-08-07T15:19:49.601396Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:38.324529Z","title":"Operon c++: an efficient genetic pro- gramming framework for symbolic regression","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.324529Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:c8c1a2446e170c0fb7eb7382d33709b591d88d2f80d2ac3b65aca56d00f651a5","observation_id":"1deeb593-ebb8-4b34-ab63-1c3451b48135","resolution":{"observed_at":"2026-08-07T15:19:38.324529Z","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":"2026-08-07T15:19:49.331312Z","title":"Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems","venue":null,"work_id":"e0a6e9d3-6468-4069-9193-a496e9a90ded","year":2011},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.435641Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f62e89cb9073a750a5224dd493247454ebef692bd24dc1fbd11a345e67bc85d3","observation_id":"ce133121-c472-4119-91d5-9c60a306d4f9","resolution":{"observed_at":"2026-08-07T15:19:49.398847Z","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":"2107.14351","last_updated":"2021-07-29T22:12:59Z","snapshot_observed_at":"2026-07-06T11:33:58.035943Z","submitted_at":"2021-07-29T22:12:59Z","title":"Contemporary Symbolic Regression Methods and their Relative Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.14351","snapshot_observed_at":"2026-08-07T15:19:38.598819Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.598819Z"},"links":{"cited_paper":"/paper/2107.14351","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:7e8384c5d462790cd7535c786a957c336d8feae315909335c9019d33e82b0741","observation_id":"a94de04e-2f10-49c1-a792-4af181a83b0f","resolution":{"observed_at":"2026-08-07T15:19:38.598819Z","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":"2026-08-07T15:19:38.715608Z","title":"Multi-model approach for stock price prediction and trading recommendations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.715608Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:6703830bfb9cdd8920a386f651fe86426b2124faffe295f170ffb02a81f9e767","observation_id":"c5e0d599-2086-480a-b13f-04c781fb0cb8","resolution":{"observed_at":"2026-08-07T15:19:38.715608Z","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":"2026-08-07T15:19:49.141874Z","title":"Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression","venue":null,"work_id":"5d0b75d0-d0e1-41ea-a397-c33827b3844b","year":2017},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.827677Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4ce55a09237b69d7ddfff95ef7985f376fe1b44d7f9b32afad314793e44de3b3","observation_id":"4823b385-b300-418f-a083-ef642c773dd1","resolution":{"observed_at":"2026-08-07T15:19:49.227680Z","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":"2026-08-07T15:19:48.964295Z","title":"Discovering symbolic models from deep learning with inductive biases","venue":null,"work_id":"077d366d-79c1-4c58-8149-69b23a55e908","year":2020},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:38.919452Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4dd668511778453c79785afdf70a53bfc22b8cd7cd039252f5705f862e8fe4ba","observation_id":"ea0c6d7e-3f90-45d9-85d6-5da0f1b75b60","resolution":{"observed_at":"2026-08-07T15:19:49.071262Z","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":"2026-08-07T15:19:48.775760Z","title":"Interaction–transformation evolutionary algorithm for symbolic regression","venue":null,"work_id":"d3e58dc3-9227-4230-a48e-cfd53b1e78c6","year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.025274Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:c2b156c4292334943d7aeb9d9ef19bdbb91a169159cbabe0af4da6ea5823d6b3","observation_id":"ddf83fdf-d003-4fa0-aae4-a933e5400b26","resolution":{"observed_at":"2026-08-07T15:19:48.850974Z","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":"2026-08-07T15:19:48.572014Z","title":"Deep symbolic regression for recurrence prediction","venue":null,"work_id":"eb4b5c00-d882-4032-b8ce-77a896f8f22a","year":2022},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.110955Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:3f29e9e8c845075732075d531df6123514b3d91bb8079188321e5b075763b8a5","observation_id":"f73bb99a-42e0-44e6-8c26-461ca486b57b","resolution":{"observed_at":"2026-08-07T15:19:48.665132Z","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":"2026-08-07T15:19:48.373393Z","title":"Evolutionary large language model for automated feature transformation","venue":null,"work_id":"28e9b607-5565-4f06-be38-da4be94bfd1f","year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.163341Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:ae13a063ef1cfa37bc936e2e64c8f84d4abb7d30466abb9ade07f2a708606841","observation_id":"a94ad057-5282-4609-ac1c-b594f42155f3","resolution":{"observed_at":"2026-08-07T15:19:48.462412Z","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":"2504.21304","last_updated":"2025-04-30T04:26:03Z","snapshot_observed_at":"2026-08-07T15:57:55.449740Z","submitted_at":"2025-04-30T04:26:03Z","title":"Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21304","snapshot_observed_at":"2026-08-07T15:19:39.221996Z","title":"Unsupervised feature transformation via in-context generation, generator-critic llm agents, and duet-play teaming","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.221996Z"},"links":{"cited_paper":"/paper/2504.21304","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:c4b337c32955980fbd4837a867bfcc4b366ac72e1ce3c0be889204890c4e5026","observation_id":"6f45f2b5-2c32-4251-a34f-387db9e7481c","resolution":{"observed_at":"2026-08-07T15:19:39.221996Z","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":"2026-08-07T15:19:48.184873Z","title":"Neuro-symbolic embedding for short and effective feature selection via autoregressive generation","venue":null,"work_id":"4f0bdcfa-c351-4a22-9298-056ee0584aa9","year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.363326Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:10b3adc7b37d43ad80f4a59bd97cc656dba5fcebf72b70543f470174d4de7bee","observation_id":"3997dbc0-7542-4580-ad5c-6343393b1800","resolution":{"observed_at":"2026-08-07T15:19:48.270162Z","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":"2005.15547","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:42.843766Z","title":"Gustafson, E.K","venue":null,"work_id":"5883bd54-c135-4963-b6ec-bbdecbf78038","year":2005},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.443910Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:5bcef8e09e0fce5c6997038788d81d052984d30b9c0ce2dbe111adc804ef11e3","observation_id":"32c5bf9a-1586-4af1-bc14-def11d384beb","resolution":{"observed_at":"2026-08-07T15:19:42.937810Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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":"2026-08-07T15:19:47.945039Z","title":"Shape- constrained multi-objective genetic programming for symbolic regression","venue":null,"work_id":"5ce3c1a1-352e-463a-82d5-b99fc9b84dcb","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.520432Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:6a12f4e26272f65cd3ee978fa4ef3b61501dcbc527eec63bbbaf7c0356f4fc47","observation_id":"28c95cf9-df58-4340-900c-7c3784f72d5e","resolution":{"observed_at":"2026-08-07T15:19:48.059864Z","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":"2026-08-07T15:19:47.749438Z","title":"Double correction framework for denoising recommendation","venue":null,"work_id":"901aec90-c386-4935-9fe5-8dc6aee59495","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.591329Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:51ebdaf9a648e3345352fa7a39485b82abfcdc66acaa0de8980ba5dddc505d5a","observation_id":"124cb244-e7ed-42ec-8759-7c7686fde104","resolution":{"observed_at":"2026-08-07T15:19:47.852970Z","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":"2401.00282","last_updated":"2023-12-30T17:05:31Z","snapshot_observed_at":"2026-08-05T16:26:52.781511Z","submitted_at":"2023-12-30T17:05:31Z","title":"Deep Generative Symbolic Regression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00282","snapshot_observed_at":"2026-08-07T15:19:39.659141Z","title":"Deep generative symbolic regression","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.659141Z"},"links":{"cited_paper":"/paper/2401.00282","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4932617bfcb7b30777f2c174863cff1a4b1bf4dc67bc0423f14d7fc353fb3260","observation_id":"b1ead65f-f192-4501-8073-896b80f88026","resolution":{"observed_at":"2026-08-07T15:19:39.659141Z","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":"2026-08-07T15:19:47.548122Z","title":"Reinforcement feature transformation for polymer property performance prediction","venue":null,"work_id":"cdeeaec5-c0e3-4318-bff3-c7a762caae96","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.809965Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:1224f482428180e7989c0a65b766495cf19d8c7a8f85b3f13c8e4e7c5d77aaa5","observation_id":"749b205c-698a-4e6e-9257-eff555115f94","resolution":{"observed_at":"2026-08-07T15:19:47.635220Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:39.941840Z","title":"Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:39.941840Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:77d3988691575690323caf2b7e7c16c127886b68d7dcdc90c17cc90cb07a105a","observation_id":"a926ab44-4030-4185-9498-ed2e2e9506ab","resolution":{"observed_at":"2026-08-07T15:19:39.941840Z","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":"2504.02275","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:42.595275Z","title":"Enhancing customer contact efficiency with graph neural networks in credit card fraud detection workflow","venue":null,"work_id":"f4246982-f809-4510-9051-090f72180409","year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.084719Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:db7b595750e54cedde187c648f31012819388db14352538488481340dff9ec7e","observation_id":"bec1315b-f74d-416f-8a89-104d82f8ec70","resolution":{"observed_at":"2026-08-07T15:19:42.619759Z","resolver_source":"raw_fallback","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":{"arxiv_id":"1910.08892","last_updated":"2020-01-16T02:11:21Z","snapshot_observed_at":"2026-07-06T08:30:47.148345Z","submitted_at":"2019-10-20T04:28:50Z","title":"Bayesian Symbolic Regression","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.08892","snapshot_observed_at":"2026-08-07T15:19:40.165170Z","title":"Bayesian symbolic regression.arXiv preprint arXiv:1910.08892, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.165170Z"},"links":{"cited_paper":"/paper/1910.08892","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:cb6990604d99cd6d32a00bb9f9845aa93a4cf0400d53bcecb3cbb3fc8b6d50ba","observation_id":"e6250186-6e50-4fd0-b05d-41792391466c","resolution":{"observed_at":"2026-08-07T15:19:40.165170Z","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":"2026-08-07T15:19:47.391899Z","title":"End-to- end symbolic regression with transformers","venue":null,"work_id":"8085d3c4-0fd6-45eb-9653-e305128db809","year":2022},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.222610Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:709cf0cf4f36fceae7e740a6178e2194b72235fe4a42867b0ab542649542478a","observation_id":"434b8c5d-1b9c-4eca-88fb-32b693954c9a","resolution":{"observed_at":"2026-08-07T15:19:47.469587Z","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":"2026-08-07T15:19:47.088985Z","title":"Integration of neural network-based symbolic regression in deep learning for scientific discovery","venue":null,"work_id":"6c3361cd-e17d-40aa-aff1-949ad082c7a0","year":2020},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.292635Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:17d80f5cf8d96f958b2596e43d51e3b48261fb6390c7d9643acadea2abede825","observation_id":"60674fce-b488-4641-841c-b413bb19015c","resolution":{"observed_at":"2026-08-07T15:19:47.248205Z","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":"2026-08-07T15:19:46.874443Z","title":"Inference of compact nonlinear dynamic models by epigenetic local search","venue":null,"work_id":"4a12d932-38c5-4d08-842b-658d7d44325a","year":2016},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.362500Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:24d8f7f1d7c9080879f448db06ba885c760d7198db8dd6d93aad01945ad17279","observation_id":"c180e107-25d9-4eea-be67-98070c96906a","resolution":{"observed_at":"2026-08-07T15:19:46.966254Z","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":"2026-08-07T15:19:46.695858Z","title":"Epsilon-lexicase selection for regression","venue":null,"work_id":"9bdcc0c7-1c0b-4848-9f9c-d6e8f138ba5e","year":2016},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.401241Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f0c652a008e1690543ab58b7322bc7db0690eba86598908aa6c95ce5398c4c6f","observation_id":"5d79dea4-7e32-4ce8-af7a-4e312dbf2764","resolution":{"observed_at":"2026-08-07T15:19:46.770359Z","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":"1807.00981","last_updated":"2019-03-25T16:07:28Z","snapshot_observed_at":"2026-08-02T11:50:07.183205Z","submitted_at":"2018-07-03T05:21:30Z","title":"Learning concise representations for regression by evolving networks of trees","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00981","snapshot_observed_at":"2026-08-07T15:19:40.468386Z","title":"Learning concise representations for regression by evolving networks of trees","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.468386Z"},"links":{"cited_paper":"/paper/1807.00981","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:bb15dc0abe676fb7ae2ea79d1437a025a9373bfe9523e8f9126a088a31e29712","observation_id":"d2e7e49b-3c94-4d96-ab15-cda64f357148","resolution":{"observed_at":"2026-08-07T15:19:40.468386Z","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":"2026-08-07T15:19:46.588932Z","title":"A flexible symbolic regression method for constructing interpretable clinical prediction models","venue":null,"work_id":"e670687a-927e-4453-8803-482145809615","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.518397Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4922a9e8246ea172583fa75bbb18ddbc1af3b0b4a67d9874cde798d9dc5ca276","observation_id":"c994cb42-6a7c-492d-a4c1-9d77f5001afe","resolution":{"observed_at":"2026-08-07T15:19:46.612930Z","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":"2026-08-07T15:19:46.500669Z","title":"Sehf: A summary-enhanced hierarchical framework for financial report sentiment analysis","venue":null,"work_id":"e93b92b1-899b-46a9-bb43-017087a778b0","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.568947Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:5e038d8d1ef3247461597dde9b04112f45d83d771f769f596b6b0be7c9612f86","observation_id":"4ddf88b7-3e9d-479b-a590-619c96a4f05d","resolution":{"observed_at":"2026-08-07T15:19:46.546727Z","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":"2026-08-07T15:19:46.266240Z","title":"Sade: A speaker-aware dual encoding model based on diagbert for medical triage and pre-diagnosis","venue":null,"work_id":"9ecc53c2-9840-4a2a-be1a-ab1baadea961","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.627450Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:5f04622188b328e38d6539c0496948f5a2edd7c3aab8af0da02cff8d2c3c2930","observation_id":"03d03d67-a101-4e11-b89c-9f2b289f1ff5","resolution":{"observed_at":"2026-08-07T15:19:46.390116Z","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":"2026-08-07T15:19:46.098047Z","title":"Pth and the regulation of mesenchymal cells within the bone marrow niche","venue":null,"work_id":"2cd1cd52-c44b-4f0c-9dc0-3f67638c4832","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.696315Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:163184b469494507a3d41a24019b3499ef211141b6021a03097344ea44188464","observation_id":"af70475b-aa75-4c26-9b90-80684ae816d8","resolution":{"observed_at":"2026-08-07T15:19:46.187454Z","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":"2026-08-07T15:19:45.871886Z","title":"Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression","venue":null,"work_id":"bcf9e158-1735-4bbd-b82e-0e25c937a61f","year":2019},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.725480Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:169337895b7f0d4fff683e70494191e144b0d062cb96a77be3a0b35e0f4b1cdf","observation_id":"6a0e911f-d808-47ea-a2ad-dc2d2b64eee7","resolution":{"observed_at":"2026-08-07T15:19:45.960105Z","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":"2026-08-07T15:19:45.672073Z","title":"Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling","venue":null,"work_id":"8051ebf8-d7f2-4955-bcde-9dc1f1a578ee","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.761901Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4798b623d567965be983b606a42fb7ad4f5385fd5fad8e206cc37edea453dcd0","observation_id":"b280dbc8-f3ae-4ccf-a152-4b5d19cadc3d","resolution":{"observed_at":"2026-08-07T15:19:45.760833Z","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":"2026-08-07T15:19:45.532747Z","title":"Ffx: Fast, scalable, deterministic symbolic regression technology","venue":null,"work_id":"2f874cdf-5877-4ac2-a759-0bd0127eb3ac","year":2011},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.805977Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:4bc6d7e4bb6f7825065bd7a03e528f4560c8ac0cc4d4bf6ba77117b889a0bd57","observation_id":"6a9982e9-bf24-4015-bf77-4d0d9aa039a0","resolution":{"observed_at":"2026-08-07T15:19:45.599621Z","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":"2111.00053","last_updated":"2021-11-17T22:33:49Z","snapshot_observed_at":"2026-08-05T01:45:41.400272Z","submitted_at":"2021-10-29T19:26:41Z","title":"Symbolic Regression via Neural-Guided Genetic Programming Population Seeding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00053","snapshot_observed_at":"2026-08-07T15:19:40.857956Z","title":"Symbolic regression via neural-guided genetic programming population seeding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.857956Z"},"links":{"cited_paper":"/paper/2111.00053","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f3e0bf40124f20f18675ff7fc123cf02e1d6770d020ad0b9f2411a4dff424f0a","observation_id":"ef3a3ee8-a4ac-4bca-a23b-0ed98f6b29a9","resolution":{"observed_at":"2026-08-07T15:19:40.857956Z","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":"2026-08-07T15:19:45.291538Z","title":"Symbolic regression via deep reinforcement learning enhanced genetic programming seeding","venue":null,"work_id":"257d11ae-7cd5-4070-9636-2cadce78096f","year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.892984Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:bd16fb4ffff31916f471f3eee2370f4ff2dfcd8f261baea14346bad9cdbe06f5","observation_id":"026804de-a253-4956-971c-69e159f28afe","resolution":{"observed_at":"2026-08-07T15:19:45.352693Z","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":"1703.00512","last_updated":"2017-03-01T21:20:11Z","snapshot_observed_at":"2026-08-04T12:18:42.416608Z","submitted_at":"2017-03-01T21:20:11Z","title":"PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison","version":1},"cited_work":{"arxiv_id":"1703.00512","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.00512","snapshot_observed_at":"2026-08-07T15:19:42.367200Z","title":"PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison","venue":"cs.LG","work_id":"6b403399-c978-433e-9434-ac21b5114b11","year":2017},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.907317Z"},"links":{"cited_paper":"/paper/1703.00512","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:e3e712de4e88024427a1a7dc63d68966f6d4d843e57f20af549e3f4ef3dbd992","observation_id":"ad30daf6-7902-46f9-836a-680ec8e29cef","resolution":{"observed_at":"2026-08-07T15:19:42.415935Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"1912.04871","last_updated":"2021-04-05T22:29:16Z","snapshot_observed_at":"2026-08-06T05:06:42.342874Z","submitted_at":"2019-12-10T18:25:48Z","title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.04871","snapshot_observed_at":"2026-08-07T15:19:40.931537Z","title":"Deep symbolic regression: Recovering mathematical expressions from data via risk- seeking policy gradients","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.931537Z"},"links":{"cited_paper":"/paper/1912.04871","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:bbf5a5c18c282260c8547365a593496b0c332cb103ea1671c84bb9108807b0c5","observation_id":"fc039243-5f78-4448-9ae0-94e277cc17b9","resolution":{"observed_at":"2026-08-07T15:19:40.931537Z","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":"2026-08-07T15:19:45.094354Z","title":"Age-fitness pareto optimization","venue":null,"work_id":"a9232c33-14c2-4201-b234-a22cfb329994","year":2010},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:40.981004Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:3ec28af495b8305f8885084d31cac8e5d0a5f23b59273f70a6cf0efc7909de34","observation_id":"986958c7-27ad-43be-815a-ec79db68e102","resolution":{"observed_at":"2026-08-07T15:19:45.203040Z","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":"2026-08-07T15:19:44.881513Z","title":"Transformer-based planning for symbolic regression","venue":null,"work_id":"a46b9d61-863a-4ff9-9012-8a5cf9f8dd94","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.039517Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:7b28175e08472cae8e6f2095d2af460aca386d28b4a600ed2b1db249ff9aa9f4","observation_id":"60431169-bddc-4739-a47c-c1f81d738b02","resolution":{"observed_at":"2026-08-07T15:19:44.966355Z","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":"2205.13134","last_updated":"2023-02-02T12:29:53Z","snapshot_observed_at":"2026-07-06T13:14:06.120131Z","submitted_at":"2022-05-26T03:50:52Z","title":"Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13134","snapshot_observed_at":"2026-08-07T15:19:41.072343Z","title":"Symbolic physics learner: Discovering governing equations via monte carlo tree search","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.072343Z"},"links":{"cited_paper":"/paper/2205.13134","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:9a5e3346bc26620433836e036ff6222174d2e45ff419637e4203d9d200cded70","observation_id":"f0054d39-e244-47ed-9815-bfd17da176c9","resolution":{"observed_at":"2026-08-07T15:19:41.072343Z","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":"2026-08-07T15:19:41.107387Z","title":"Ai feynman: A physics-inspired method for symbolic regression","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.107387Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:3418e3b87b2736d5eeacc488eec95ec4842965a845d5332e3054917550b02478","observation_id":"84f70a3b-1bf5-40c1-9a72-2f355fa25ea6","resolution":{"observed_at":"2026-08-07T15:19:41.107387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10782","last_updated":"2020-12-16T17:58:47Z","snapshot_observed_at":"2026-07-06T09:30:32.320227Z","submitted_at":"2020-06-18T18:01:19Z","title":"AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10782","snapshot_observed_at":"2026-08-07T15:19:41.153461Z","title":"Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.153461Z"},"links":{"cited_paper":"/paper/2006.10782","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f56fd0ad3b7b70c59cfb6f6e788727cdef9a4aa9406acaef3774a9a64e8501ec","observation_id":"2874f898-eee0-4478-8c16-699e67bea742","resolution":{"observed_at":"2026-08-07T15:19:41.153461Z","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":"2026-08-07T15:19:44.697617Z","title":"Semantically-based crossover in genetic programming: application to real-valued symbolic regression","venue":null,"work_id":"d8c74876-8bea-433e-b81c-beab97ebaf73","year":2011},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.199712Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:2d7dca45dde1e158ed3bdf5a1cf05804bb0b74bcf7eabd1f831aff1adbb65c97","observation_id":"b03b26e2-c793-4e7e-bee8-b3454f323a9f","resolution":{"observed_at":"2026-08-07T15:19:44.777505Z","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":"2106.14131","last_updated":"2021-06-27T03:26:35Z","snapshot_observed_at":"2026-08-07T12:08:37.241142Z","submitted_at":"2021-06-27T03:26:35Z","title":"SymbolicGPT: A Generative Transformer Model for Symbolic Regression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14131","snapshot_observed_at":"2026-08-07T15:19:41.254282Z","title":"Symbolicgpt: A generative transformer model for symbolic regression","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.254282Z"},"links":{"cited_paper":"/paper/2106.14131","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:6c90ed4b633232c6c77257170e885f015c541461531ec1dc062526f03784c2b0","observation_id":"deadf2a1-f285-47e2-bd18-9ad77f88dfc8","resolution":{"observed_at":"2026-08-07T15:19:41.254282Z","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":"2026-08-07T15:19:44.507013Z","title":"Scalable genetic pro- gramming by gene-pool optimal mixing and input-space entropy-based building-block learning","venue":null,"work_id":"3beaba9c-6829-4d96-8d06-1c1c2c3c835a","year":2017},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.304780Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:a892db81a2a7bbf37f31736ecb1ae6d5e7da6b69c78366a671a7d45fa94aaedf","observation_id":"a9797823-09f3-474d-b1eb-fa87ecb91048","resolution":{"observed_at":"2026-08-07T15:19:44.571900Z","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":"2026-08-07T15:19:44.356689Z","title":"Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression","venue":null,"work_id":"9da0abfa-b455-4542-aa01-67687742c715","year":2019},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.340141Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:27da09b6157185b5c9087552927c9abb40fde2a3563e36480de2d6a7ba90fecc","observation_id":"340b7b71-be49-4dbe-b350-3f05e3aa44b1","resolution":{"observed_at":"2026-08-07T15:19:44.422431Z","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":"2501.10555","last_updated":"2025-01-17T21:05:09Z","snapshot_observed_at":"2026-07-06T20:22:41.032443Z","submitted_at":"2025-01-17T21:05:09Z","title":"Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.10555","snapshot_observed_at":"2026-08-07T15:19:41.432848Z","title":"Towards data-centric ai: A comprehensive survey of traditional, reinforcement, and generative approaches for tabular data transformation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.432848Z"},"links":{"cited_paper":"/paper/2501.10555","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:1b58b5df8d17ae4cf52420ae311ec2cfe149bf95f8e5b44199802a24a219a792","observation_id":"6b186ec6-dba3-4ed6-94a6-891f00154eb2","resolution":{"observed_at":"2026-08-07T15:19:41.432848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03521","last_updated":"2025-02-25T02:17:05Z","snapshot_observed_at":"2026-07-06T19:27:47.617823Z","submitted_at":"2024-09-27T00:01:32Z","title":"Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03521","snapshot_observed_at":"2026-08-07T15:19:41.496755Z","title":"Lcmdc: Large-scale chinese medical dialogue corpora for automatic triage and medical consultation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.496755Z"},"links":{"cited_paper":"/paper/2410.03521","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:b55c68950c7c51bd5388dcef58de5b9b40fc02d864aa1bb0906ba52d478c8d83","observation_id":"6fd9a633-1f0f-41d5-8cae-7d6944274224","resolution":{"observed_at":"2026-08-07T15:19:41.496755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04015","last_updated":"2024-03-06T19:58:19Z","snapshot_observed_at":"2026-08-03T07:21:30.180572Z","submitted_at":"2024-03-06T19:58:19Z","title":"Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04015","snapshot_observed_at":"2026-08-07T15:19:41.583613Z","title":"Knockoff-guided feature selection via a single pre-trained reinforced agent","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.583613Z"},"links":{"cited_paper":"/paper/2403.04015","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:2dbcdcd9ec3d7bbfde2ec58f266710eb328ca94a5de4a1d2c91fbd352a7acd0c","observation_id":"f328dfa5-a1df-4e25-9e70-552d05bc5d92","resolution":{"observed_at":"2026-08-07T15:19:41.583613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09617","last_updated":"2025-07-17T07:51:28Z","snapshot_observed_at":"2026-07-06T17:30:21.100876Z","submitted_at":"2024-02-14T23:12:09Z","title":"LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09617","snapshot_observed_at":"2026-08-07T15:19:41.657729Z","title":"Llm-enhanced user-item interactions: Leveraging edge information for optimized recommendations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.657729Z"},"links":{"cited_paper":"/paper/2402.09617","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:e13cd0e2cacaa75be9678d1a598d9539377f7249ec2f6777de26904013e3ac7d","observation_id":"25899c0c-c65a-45d0-a120-4842bf8ba3ab","resolution":{"observed_at":"2026-08-07T15:19:41.657729Z","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":"2026-08-07T15:19:41.694888Z","title":"A successful hybrid deep learning model aiming at promoter identification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.694888Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:8c4298bf922fddfdbdf4009542939f2904a4cbcca3cd69356ef03f709394009c","observation_id":"9ee983f7-21d8-4817-9a54-74dd08dd6b6c","resolution":{"observed_at":"2026-08-07T15:19:41.694888Z","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":"2026-08-07T15:19:44.168700Z","title":"Symbolic regression in materials science.MRS Communications, 9(3):793–805, 2019","venue":null,"work_id":"8a5c62fc-280e-4390-a640-45364f279640","year":2019},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.717066Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:b86e9f2d404f2a6ae53d0db3c29d74e5ed074e4f4ba5fbc87033721d02f03b5c","observation_id":"7a6d3922-2356-4339-ad17-952a3104e3fe","resolution":{"observed_at":"2026-08-07T15:19:44.226917Z","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":"2026-08-07T15:19:43.950414Z","title":"Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing","venue":null,"work_id":"e5de2097-395e-4872-9d52-bd43a10e26bf","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.749583Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:37d435820634705c1d0a9baed11fb900ace704603fb7837d2cdcb641132f09e5","observation_id":"5aa6c0e1-5e8d-4293-b9f8-c176af2663cb","resolution":{"observed_at":"2026-08-07T15:19:44.056245Z","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":"2411.05742","last_updated":"2024-11-08T18:01:05Z","snapshot_observed_at":"2026-07-06T19:47:34.320753Z","submitted_at":"2024-11-08T18:01:05Z","title":"Topology-aware Reinforcement Feature Space Reconstruction for Graph Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05742","snapshot_observed_at":"2026-08-07T15:19:41.800742Z","title":"Topology-aware reinforcement feature space reconstruction for graph data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.800742Z"},"links":{"cited_paper":"/paper/2411.05742","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:abe6d62bfcb03d2467b4f34bd494cb586061bf4f5fb023ee16e6a0d176aff3f0","observation_id":"443bca91-424d-423d-a1d7-e21dc2b009e7","resolution":{"observed_at":"2026-08-07T15:19:41.800742Z","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":"2026-08-07T15:19:43.770199Z","title":"Feature selection as deep sequential generative learning","venue":null,"work_id":"f602effb-b8af-460b-997c-7333592170d9","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.846815Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:02ffb467803dcc1d816b910d55d2588fdf37ebce84f64bcefa4909d66891fefa","observation_id":"a2f7bd7d-ad70-4cf2-91a9-3c9b5be86581","resolution":{"observed_at":"2026-08-07T15:19:43.852225Z","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":"2026-08-07T15:19:43.596548Z","title":"Revolutionizing biomarker discovery: Leveraging generative ai for bio-knowledge-embedded continuous space exploration","venue":null,"work_id":"1d4fd490-8931-48fa-80de-9422cf68651d","year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.883546Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:751bb9cffbabe8dce4a9bb44274cc3d94c8efbf784dbd6c02689f5f15b32fc99","observation_id":"ae9de55b-8dc9-406e-b0ee-858c620c8a30","resolution":{"observed_at":"2026-08-07T15:19:43.675849Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:19:41.922971Z","title":"Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine-tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.922971Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f5086386215e6053c09543810703157413327ae3665b783a59186012c7e6a223","observation_id":"c2661aed-b642-48d3-9a7e-f9ab6fc370bd","resolution":{"observed_at":"2026-08-07T15:19:41.922971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08828","last_updated":"2025-02-16T16:41:47Z","snapshot_observed_at":"2026-08-07T23:30:50.064504Z","submitted_at":"2025-02-12T22:34:50Z","title":"A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08828","snapshot_observed_at":"2026-08-07T15:19:41.963199Z","title":"A survey on data-centric ai: Tabular learning from reinforcement learning and generative ai perspective","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:41.963199Z"},"links":{"cited_paper":"/paper/2502.08828","citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:f5e43db1133d0eddb75ac81d263f46593a7649c8a738f906cdb1e38686c0966e","observation_id":"721bf089-eab2-4bcc-b062-a8f256d138bc","resolution":{"observed_at":"2026-08-07T15:19:41.963199Z","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":"2026-08-07T15:19:43.438981Z","title":"Deep learning and symbolic regression for discovering parametric equations","venue":null,"work_id":"7822cc99-a794-41fb-94ba-ad04edaf4d35","year":2023},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:42.000717Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:935fdefeeddf71e69bd11980252e01fff0a3fcd37879b7bac16139d6c1b4f911","observation_id":"6b58a863-5e61-4e4a-8fdc-3368d0006cd4","resolution":{"observed_at":"2026-08-07T15:19:43.515932Z","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":"2026-08-07T15:19:43.231319Z","title":"To simplify the notations, we replace the constant with ’C’ in the equations","venue":null,"work_id":"9e6efd28-f25d-4584-9d92-4521c6c28593","year":null},"citing_paper":{"arxiv_id":"2505.15572","last_updated":"2025-05-21T14:25:41Z","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:42.053573Z"},"links":{"citing_paper":"/paper/2505.15572"},"observation_digest":"sha256:04e192f2adc3d5c10fa5678b6a8ad519c8c47a17faf038da0668e1161adc4568","observation_id":"edbb3fd1-d590-4632-86e3-38ee2398fe01","resolution":{"observed_at":"2026-08-07T15:19:43.318152Z","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":"2505.15572","last_updated":"2025-05-21T14:25:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:12:33.360178Z","submitted_at":"2025-05-21T14:25:41Z","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":22,"verified_exact":1,"verified_fuzzy":38},"total_outbound_references":63},"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 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2505.15572."}