{"as_of":"2026-08-20T05:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:97983409d078c4cf596e4ffc328e0c7509969784e535edf0efd67fa23d5e1d6e","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:40:16.402454Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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":"2026-08-16T11:06:08.233759Z","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":"2026-08-06T20:25:38.576348Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01085","snapshot_observed_at":"2026-08-16T11:06:08.233759Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.16516","last_updated":"2025-04-24T19:36:09Z","snapshot_observed_at":"2026-08-18T14:44:46.063293Z","submitted_at":"2025-04-23T08:41:27Z","title":"Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T11:06:08.233759Z"},"links":{"cited_paper":"/paper/2501.01085","citing_paper":"/paper/2504.16516"},"observation_digest":"sha256:b730a0533bd6b1642d83f6e42883456d7fb8546fe617c562d614c162d5285984","observation_id":"37a1bb22-d106-4120-a957-9c4eda0dc0a0","resolution":{"observed_at":"2026-08-16T11:06:08.233759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2501.01085","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.01085","snapshot_observed_at":"2026-08-06T20:25:38.576348Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","venue":"cs.LG","work_id":"98631c2a-b52e-4435-9164-beb76558ff8a","year":2025},"citing_paper":{"arxiv_id":"2507.03110","last_updated":"2025-08-18T20:02:13Z","snapshot_observed_at":"2026-08-19T21:30:21.204726Z","submitted_at":"2025-07-03T18:32:03Z","title":"SymMatika: Structure-Aware Symbolic Discovery","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:25:38.240909Z"},"links":{"cited_paper":"/paper/2501.01085","citing_paper":"/paper/2507.03110"},"observation_digest":"sha256:f63d63359aaa9b3090779f88858c16240d4e1f8f73a5dbeca6ef0a772aa53519","observation_id":"840eb33f-f18b-49aa-a2e1-60eea75d66ff","resolution":{"observed_at":"2026-08-06T20:25:38.618720Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.01085/citation-record","integrity":"/paper/2501.01085/integrity","json":"/paper/2501.01085/citation-record.json","paper":"/paper/2501.01085"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:40:16.173253Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.173253Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:4c180c771b5605c8e620c89589774bfebdd00d6db00d28e305144cfe614050ca","observation_id":"dea0bbb3-1bf6-47ec-8e85-824e5fefde9e","resolution":{"observed_at":"2026-08-10T22:40:16.173253Z","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":"2026-08-10T22:40:16.178532Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.178532Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:775dfde63739b0ac6ab6026cff46c8bcc4c9aa3d78557306793858ee2d0f6890","observation_id":"60c915ee-f8e0-46eb-ae33-fa8cdff58d59","resolution":{"observed_at":"2026-08-10T22:40:16.178532Z","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":"2026-08-10T22:40:17.110683Z","title":null,"venue":null,"work_id":"0c2da18c-eedf-4345-82fe-28444bae6e80","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.183695Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:2bf5a62d5d3696e5115e3c72c9a446ed56d9c8e720eea118a1e801249f1a419c","observation_id":"29412bab-90e1-4204-926e-92b98b36bfaf","resolution":{"observed_at":"2026-08-10T22:40:17.115066Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1205.2653","last_updated":"2012-05-09T15:01:22Z","snapshot_observed_at":"2026-08-15T04:10:22.921871Z","submitted_at":"2012-05-09T15:01:22Z","title":"L2 Regularization for Learning Kernels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1205.2653","snapshot_observed_at":"2026-08-10T22:40:16.189281Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.189281Z"},"links":{"cited_paper":"/paper/1205.2653","citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:b94d0c2edd7a6b1c8209a9ffff8d38b871a97df01674fba8206b68fae97ecd15","observation_id":"85b42f5d-6b2e-468d-9973-b887e6808ef9","resolution":{"observed_at":"2026-08-10T22:40:16.189281Z","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":"2026-08-10T22:40:17.096983Z","title":null,"venue":null,"work_id":"fd6705c9-7b6b-4171-b5b9-9bb93ceee7b3","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.193809Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:d0a7ac4b88fb753a1c4fbb048fbda02bee1acfb833ea771678e0f2abcd785883","observation_id":"24a04bde-8c2b-4404-8dc2-099a5e6f1b55","resolution":{"observed_at":"2026-08-10T22:40:17.101442Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:17.083646Z","title":null,"venue":null,"work_id":"9bd0e74c-3948-4915-a284-c141ed4c82d9","year":2014},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.198274Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:7d3d6aed7e9444c99b3905c01294a597a9bd76eb24a15e11ebe50d12c5d50df9","observation_id":"c9ea40c0-ddff-4c87-b584-f5feb415c387","resolution":{"observed_at":"2026-08-10T22:40:17.087859Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:17.070449Z","title":null,"venue":null,"work_id":"82c8f259-bf12-4101-b1d7-a7075c277ece","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.203073Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ccbdcd998d4bbd23076e00302898e6bef860e53b6f29fd4689796a10495ac95a","observation_id":"818f16a1-32bd-447b-b496-da6ef248b133","resolution":{"observed_at":"2026-08-10T22:40:17.074597Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.10995","last_updated":"2021-02-26T21:21:11Z","snapshot_observed_at":"2026-08-19T07:13:31.428401Z","submitted_at":"2019-01-30T18:40:37Z","title":"Go-Explore: a New Approach for Hard-Exploration Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.10995","snapshot_observed_at":"2026-08-10T22:40:16.207803Z","title":"O.; and Clune, J","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.207803Z"},"links":{"cited_paper":"/paper/1901.10995","citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:3a8c998a292217ffab09f8d66cbf1e7366adea1f800e9ba9cfe301a8beeb6787","observation_id":"96295912-f944-4152-9570-c5a163b8ebc5","resolution":{"observed_at":"2026-08-10T22:40:16.207803Z","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":"2026-08-10T22:40:17.056881Z","title":null,"venue":null,"work_id":"d015e669-2e71-4f0f-b95d-a5a5d5cd68fd","year":2018},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.212723Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:6936b4f2e921648b6573be6a081d957762f735c8df0fe5b0ee6dab380e58e48f","observation_id":"180a52b2-5d38-40c7-a322-7752397eb9d6","resolution":{"observed_at":"2026-08-10T22:40:17.061205Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.216965Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.216965Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:d67887887361097458a0f086ec5341389dc9f505a741ffb9c219962143f1c018","observation_id":"ab6f3954-fddd-4788-9563-6e225780419f","resolution":{"observed_at":"2026-08-10T22:40:16.216965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.09281","last_updated":"2019-11-11T14:18:31Z","snapshot_observed_at":"2026-08-14T00:33:35.701152Z","submitted_at":"2019-10-21T12:06:28Z","title":"Dealing with Sparse Rewards in Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.09281","snapshot_observed_at":"2026-08-10T22:40:16.221035Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.221035Z"},"links":{"cited_paper":"/paper/1910.09281","citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:89aefff42379dd11ab1a7a07b66c24d893a1b88e0a654b4b604f29972a1e6e0a","observation_id":"337f6a73-6a5b-45ff-b647-f0f198d752fb","resolution":{"observed_at":"2026-08-10T22:40:16.221035Z","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":"2026-08-10T22:40:17.034575Z","title":null,"venue":null,"work_id":"1bfa2386-2c65-47a1-9461-b198c1710532","year":2023},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.225745Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:905f5888cd6c7c68185c54a8b092e4556b90cf770e18dcca06f7ea965c7dea36","observation_id":"7d766e21-be2c-472b-8e67-f48e55b66413","resolution":{"observed_at":"2026-08-10T22:40:17.038817Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:17.020855Z","title":null,"venue":null,"work_id":"ed421742-acd0-45e7-a4f0-e92d49a06d03","year":2023},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.229762Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:f49c8432800449859a3cd33147e3e7e93851b105908f2229f19ee5a1e3333760","observation_id":"5c0a60f4-0bec-4909-a453-286ed584faa1","resolution":{"observed_at":"2026-08-10T22:40:17.024900Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:17.007975Z","title":"J.; yelong shen; Wallis, P.; Allen-Zhu, Z.; Li, Y.; et al","venue":null,"work_id":"80a8b433-dbc4-470a-b4a0-860367f56de4","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.233936Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:004eff53b93eb8b85a644e6616f0a784fc2104c5d45a7c374f963b442e5f9cbb","observation_id":"b2d38414-83df-4670-b112-23ac00763784","resolution":{"observed_at":"2026-08-10T22:40:17.012236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.994275Z","title":null,"venue":null,"work_id":"3f8b1ae8-971c-4ea5-a742-c25409a8a286","year":2017},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.238161Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ef8cb4f808fed243fb7841a0252fb5d1338e666f90efdc9946ec7d501c5ccc02","observation_id":"75306550-62e0-403e-ac41-440ccf0e83b7","resolution":{"observed_at":"2026-08-10T22:40:16.998909Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.981109Z","title":null,"venue":null,"work_id":"a24f8ed6-fbf3-4b04-88cd-4aef41c13ba8","year":2023},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.242090Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:5c2d3bfe9ebede2f6636c8f00cbd52ca958d3d07e24512a228ca7583ed2ea003","observation_id":"9ce90e59-d9b2-40a7-b63e-4e6f9da16c41","resolution":{"observed_at":"2026-08-10T22:40:16.985314Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.967075Z","title":null,"venue":null,"work_id":"d57ff2ff-fa8d-42f6-a6dd-31e0981e7fa5","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.246155Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:cd75fca23fc73302d3983e0647b3a3f32f7c3c9ce970252d2d6cd16c34081764","observation_id":"68670949-f5b6-4942-9729-c72fcf855fe4","resolution":{"observed_at":"2026-08-10T22:40:16.971474Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.953563Z","title":null,"venue":null,"work_id":"684a6008-48fe-4ece-93f4-2c01b622ba09","year":2023},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.250192Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:d40098807c1a5ec929b7843654eaf465b23e4456bee6e5d771608e2ad8c6ca6f","observation_id":"f66fe583-281b-48d4-af5c-943751e8fe80","resolution":{"observed_at":"2026-08-10T22:40:16.957886Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.939087Z","title":"Y.; Mukherjee, S.; Gilbert, M.; Jing, L.; C eperi \\'c , V.; and Solja c i \\'c , M","venue":null,"work_id":"92008e8b-3f77-4147-9cab-83d38a805fe1","year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.254231Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ff8e4d00c95ed6ea0a679f4a8cff6579413a7973874cb3327660c62dade159b9","observation_id":"90ce4d9b-124a-46f4-a7df-ec1b4df5c4a3","resolution":{"observed_at":"2026-08-10T22:40:16.944525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.925893Z","title":"P.; Aravena, I.; et al","venue":null,"work_id":"019e4c7d-46a6-476f-ac47-6c80f52c442a","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.258638Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ae89e0bc375644c13a2839b5ec7924a4af1a610c38228053736c9386f469c570","observation_id":"19b4d6a2-1755-4264-ab9d-e0cce808b20e","resolution":{"observed_at":"2026-08-10T22:40:16.930115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.912234Z","title":"K.; Kim, S.; Santiago, C","venue":null,"work_id":"71b9b53f-c488-4c68-a4d7-3337a8bbb416","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.262871Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:c9f1f9740c0a0391abb2d4ecfa81edaa36a44a084ffdfd395ae1a5f9816d7214","observation_id":"d00991dd-c009-4898-bd3a-a31fd8f51e0c","resolution":{"observed_at":"2026-08-10T22:40:16.916452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.899100Z","title":"K.; Kim, S","venue":null,"work_id":"a3a18abb-0e69-4ef6-a459-7b938a21ccfc","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.266769Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:35d7334d1fc2f51f7038ea9070936224407074ee6e2493d49d8f7b36e462e8be","observation_id":"aad10887-561a-41fd-a731-b7a6a9942932","resolution":{"observed_at":"2026-08-10T22:40:16.903359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.885842Z","title":null,"venue":null,"work_id":"b4b7fd11-318b-4a9e-b1d8-9f39243b0a46","year":2018},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.270729Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:d2de754698477e9963e5353577b4ed683ee90567c3f2fde62a833a3dcce94ff3","observation_id":"ace4f821-bcb1-493d-8105-14e1d5e533e7","resolution":{"observed_at":"2026-08-10T22:40:16.890244Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.872793Z","title":null,"venue":null,"work_id":"d058a8d7-31b3-43ee-ad2f-65203e2f0886","year":2019},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.275418Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ec44279ab8ece69880091a97e17e8a661d02236c490eeced97e97726e90c8a7e","observation_id":"e8785740-05ff-49fd-8d71-95c9cd0d65b4","resolution":{"observed_at":"2026-08-10T22:40:16.877148Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.859305Z","title":"P.; Mirza, M.; Graves, A.; Lillicrap, T.; et al","venue":null,"work_id":"f8bb7581-a302-4b32-b258-c41bd40c37f7","year":2016},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.279456Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:e17c1201dfb32705064c40048a88f945af353a554c12330e112feec883c5bee2","observation_id":"33abd302-c2e3-4f91-b15e-bead153b0bde","resolution":{"observed_at":"2026-08-10T22:40:16.863658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.846314Z","title":"P.; Petersen, B","venue":null,"work_id":"496f9e6e-61a4-44bb-a40a-945eab7da825","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.283508Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:dedcca7851313b99c60e16b64fdbed4c074a2c5d27da46e4e5a9e96bd8f73999","observation_id":"a6c3e420-1bde-40d2-b57a-2b0be79f34ca","resolution":{"observed_at":"2026-08-10T22:40:16.850417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.832891Z","title":"N.; Landajuela, M.; Glatt, R.; Santiago, C","venue":null,"work_id":"b0a41bed-c776-4dbc-b79f-ef0c5f950dbc","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.287748Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:e83e6404f462c7c5c35ce5c76132a47548d8741d80d63edd2554fcc932ddd454","observation_id":"ee828253-bb3b-4d8b-9f16-a1bccf3893cd","resolution":{"observed_at":"2026-08-10T22:40:16.837305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.819515Z","title":null,"venue":null,"work_id":"e2212f43-4d6c-43e3-9865-3d42c90a99b2","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.292122Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:b835865bf27d7e161c45f263e0e977232e32caece2e93436047c73c82163d75c","observation_id":"c132b050-0de5-45ce-8b5e-609fac9f83ba","resolution":{"observed_at":"2026-08-10T22:40:16.823800Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.806293Z","title":null,"venue":null,"work_id":"46488ed6-c3c6-4194-8f92-7645e87e97ca","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.296342Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:9b1dec5a8a285208e8b8d0d5fcaf7d6bb75370e1501989764879bbbcc0527550","observation_id":"f681cb9f-d10f-4d6a-ba72-f3374158c561","resolution":{"observed_at":"2026-08-10T22:40:16.810343Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.792241Z","title":null,"venue":null,"work_id":"54c00a2c-930c-471c-8cc5-cf1bef57c21b","year":2004},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.300326Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:dcdc6866ea8423759ba1fd925fcd2c75806161922198b2e085316b59c41e18fc","observation_id":"9a94ef12-c839-4e7a-afb0-7f1154a61fb7","resolution":{"observed_at":"2026-08-10T22:40:16.796871Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.777897Z","title":"T.; Nguyen, N","venue":null,"work_id":"066a81a2-1e99-4330-8cd1-1945ca2aad19","year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.304998Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:6f7ab3c9a421502039ba4cb6334ab8595ff69cd93a48504c98f8dd3a62f8a8e3","observation_id":"cc41b4e6-70f7-4dd3-aa28-c80e80490427","resolution":{"observed_at":"2026-08-10T22:40:16.782476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.764861Z","title":null,"venue":null,"work_id":"49e713c2-9670-462e-8b0b-893a37648498","year":2018},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.308765Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:6853b5876bde4f3cdf897e617ddc455b8425a5e87eaec929679cf8357220a895","observation_id":"34b31958-424b-4617-abd7-60d852cec466","resolution":{"observed_at":"2026-08-10T22:40:16.769064Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.751757Z","title":null,"venue":null,"work_id":"c5d3915a-2f7e-4589-b621-a65c1fdb4c96","year":1979},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.312766Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:57de2e9f7ba1af4fdb512118e3874d3d4465b9ed24cac8f66c44e813fbdf969c","observation_id":"3f0033fd-ccce-427a-a88f-0c3ba886476a","resolution":{"observed_at":"2026-08-10T22:40:16.755939Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.738411Z","title":"K.; Landajuela, M.; Mundhenk, T","venue":null,"work_id":"bcc484d7-026d-4d0c-85e6-d95a10a4c9e3","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.316605Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:1c0f9bbc28c51f44cc2b30083e971295d90436220e0b3acbd51e9e88214fb63a","observation_id":"6324339f-f1a2-4dd7-bc70-633f56e205f7","resolution":{"observed_at":"2026-08-10T22:40:16.742643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.724567Z","title":"A.; Kageorge, L","venue":null,"work_id":"d2a08e7a-be3d-4d75-9d0a-ff27c453069f","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.320511Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:50b8287f5ea7f38399c50401669da416051961756a34b0b4be49f686ed26c057","observation_id":"219fa716-ff81-44c0-a081-d0e936650735","resolution":{"observed_at":"2026-08-10T22:40:16.729364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.325391Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.325391Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:2745d65eb3196fdedbe4de8df99a6781d7f777b12520e05f28170a8e47e12be1","observation_id":"b53a33e5-c60c-4c07-8bd9-2294f4cc353f","resolution":{"observed_at":"2026-08-10T22:40:16.325391Z","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":"2026-08-10T22:40:16.329552Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.329552Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:3d8ffaf9aa448b61f651229f0bc9f07d4d474e60933fdf73e552b8fd0fc3bfcb","observation_id":"be629ef2-054b-4ba3-9089-311d9f43f3da","resolution":{"observed_at":"2026-08-10T22:40:16.329552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-10T22:40:16.333836Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.333836Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:6d0e3b678401719facd61cbdc48bc2e31a01d69f1b9e1a7780ee9d4a4b64f998","observation_id":"7d24595f-0dab-4cdb-b2c7-d6a3625763a3","resolution":{"observed_at":"2026-08-10T22:40:16.333836Z","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":"2026-08-10T22:40:16.693631Z","title":"P.; Leahy, D","venue":null,"work_id":"3557808f-5fb6-4599-a7ec-ab7bec39bbb6","year":2010},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.338069Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:bbf3cb8b0def15f447380180f69cb9a91929fdb84eb9c0a439bdc5f6218cf4e2","observation_id":"d5402dc5-91d6-4b69-9889-d282c13ab595","resolution":{"observed_at":"2026-08-10T22:40:16.697745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.679600Z","title":null,"venue":null,"work_id":"0507bdac-bc43-4dd1-ad92-61c1c2589f16","year":2023},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.342231Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:380f50fe5e5c9472940d68380184e7877ba7728dc9b2fd06c5611394cd4fd298","observation_id":"86c4267e-ac87-4f9d-b155-fc4458a8fdf8","resolution":{"observed_at":"2026-08-10T22:40:16.684319Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.665392Z","title":"F.; Abdolmaleki, A.; Springenberg, J","venue":null,"work_id":"7689b8ba-b868-4162-a202-4baaba99e311","year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.346292Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:9e5856916e0f44901d2fbb31cc0a0501f632d2c102c4f7be6b9737c4ebed5e44","observation_id":"a4304833-fb6c-4dd0-bf3b-2999e5427295","resolution":{"observed_at":"2026-08-10T22:40:16.669881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.652054Z","title":null,"venue":null,"work_id":"3886c079-dcaa-4e82-a936-d97ac630aaaf","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.351119Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ae19bb9d7854e157fb630aedb423c3284e0d0e42de317bc9487facd63182eadf","observation_id":"99dc15a6-3f30-4ced-a350-382554b728b8","resolution":{"observed_at":"2026-08-10T22:40:16.656328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.637968Z","title":null,"venue":null,"work_id":"dd2ce48e-3f77-4584-8bf6-b155c0d28ce2","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.355679Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:efad1e80854cf800b9d31733071efc24caa11f9d4a8f51d1e63524e911425ca4","observation_id":"cb9f1a99-35b2-43c5-9b01-d5afecf1fdff","resolution":{"observed_at":"2026-08-10T22:40:16.642220Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.624513Z","title":null,"venue":null,"work_id":"678f3a8c-3872-430e-8c0d-a120a4739ae6","year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.359892Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:449e94e9fede99b00ca00d88ec60a8aeddac40e1b749baa62aef5ce3a57a0a20","observation_id":"222951e6-9918-4f99-979c-010c8ce97dac","resolution":{"observed_at":"2026-08-10T22:40:16.628738Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.611324Z","title":null,"venue":null,"work_id":"f56b6d78-9791-43b3-9ecf-460f2a2b716c","year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.364007Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:4f5f036e81497552e4ec00fd27c95107c88604dd166de86276f159521d454529","observation_id":"8bc28ca8-1289-4f38-aa25-0a71d9c39387","resolution":{"observed_at":"2026-08-10T22:40:16.615395Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.367987Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.367987Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:5fee90e66418ef75eafe40d2aef5683c681782aa5970a667ee93e729a347392e","observation_id":"d19b569a-dfe1-4c72-8d71-daf97ddeef22","resolution":{"observed_at":"2026-08-10T22:40:16.367987Z","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":"2026-08-10T22:40:16.589394Z","title":"Q.; Hoai, N","venue":null,"work_id":"47476021-0452-4311-b4be-c76cf5819537","year":2011},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.372047Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:6e259cd9279d4b98f7bb226ea7dca0a426bbe76b33a63c208c62af401f2e664d","observation_id":"b87e8c73-2f71-4acb-b4d0-3c7f7476ae93","resolution":{"observed_at":"2026-08-10T22:40:16.593645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14131","last_updated":"2021-06-27T03:26:35Z","snapshot_observed_at":"2026-08-19T21:54:51.569755Z","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":"2026-08-10T22:40:16.376029Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.376029Z"},"links":{"cited_paper":"/paper/2106.14131","citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:13b214c03564dcebbc91a8d36b73b058c78ae26b2910fa05a410720afb0b8050","observation_id":"19869ac7-fc2e-4114-aad6-aeca1c8c6bc0","resolution":{"observed_at":"2026-08-10T22:40:16.376029Z","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":"2026-08-10T22:40:16.574401Z","title":"M.; Mathieu, M.; Dudzik, A.; Chung, J.; et al","venue":null,"work_id":"f659f732-ee86-4ac4-a45d-2580b120ac3f","year":2019},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.381203Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:7cf9db62794aebd342f0040838f8062d0def3d3359f25874e140a66e64fb3788","observation_id":"70ebcece-f8e1-460b-a669-a799ffd4a66e","resolution":{"observed_at":"2026-08-10T22:40:16.579345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.559928Z","title":null,"venue":null,"work_id":"2f9a1c8e-9fb8-4162-bc82-2a99e6462966","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.385626Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:dd3dc48dadfa950e978d27515554de735b4fadccbc5c7dac8f05ee7e148dc6ec","observation_id":"813c6e8b-b8fe-447e-86a9-e98aaf2af3d3","resolution":{"observed_at":"2026-08-10T22:40:16.564026Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.546380Z","title":null,"venue":null,"work_id":"ca544657-c82a-4fce-b759-fd700ed8559e","year":2022},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.389683Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:8ca0a5f3db7d7be8fbdc72131a092abb0b93318d577b8dfddb9dafb32d1eb5ec","observation_id":"44434fc2-dc95-4d1d-ab39-3730bdeca1cf","resolution":{"observed_at":"2026-08-10T22:40:16.550538Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.532601Z","title":null,"venue":null,"work_id":"4d70f255-957e-4904-ae8e-3c0df01212f2","year":2012},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.394246Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:015615d891b11cb58281d0c6c695ac3abb6fc536caec7797b742593349ccb921","observation_id":"2d680081-f4ab-4d4e-8d0e-77ee1715c0c6","resolution":{"observed_at":"2026-08-10T22:40:16.536986Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.519376Z","title":null,"venue":null,"work_id":"10b3a432-5a8f-497d-a6fd-b0f616e73b1f","year":2019},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.398344Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:ee9061e0056cbf6118e6a320ce1d65da9d7c316876c81055daf3ab693521041b","observation_id":"a47a48dc-a542-4afa-9007-686f4ef8b68a","resolution":{"observed_at":"2026-08-10T22:40:16.523551Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T22:40:16.504241Z","title":null,"venue":null,"work_id":"8b07828c-d158-46a9-ba69-9e58ee93e13d","year":2021},"citing_paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T22:40:16.402454Z"},"links":{"citing_paper":"/paper/2501.01085"},"observation_digest":"sha256:19d5ac935ac3ade98874d5533cc20f2e145a51d039dd9cf016098e221caa771c","observation_id":"08c96c6d-8d9e-4369-bf43-beb82182e203","resolution":{"observed_at":"2026-08-10T22:40:16.509499Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01085","last_updated":"2025-01-02T06:05:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T22:14:17.772107Z","submitted_at":"2025-01-02T06:05:59Z","title":"Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":39,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":54},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2501.01085."}