{"as_of":"2026-08-09T11:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8a9b6aa96ffe46c775c845954ddb2806773f87f20b9efbb2e42b8dda19032dc6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T10:14:21.497631Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T13:06:58.490309Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-09T10:14:21.497631Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03029","last_updated":"2025-06-18T12:24:26Z","snapshot_observed_at":"2026-08-09T10:06:03.418620Z","submitted_at":"2025-02-05T09:31:27Z","title":"On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T10:14:21.497631Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2502.03029"},"observation_digest":"sha256:28d15633b12c0ca05ab8090d2ab2793ba70bcbc6ca58ae1a924c65da367d7544","observation_id":"e16a1324-88c0-492b-be00-54154a85934d","resolution":{"observed_at":"2026-08-09T10:14:21.497631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-08T17:22:21.844545Z","title":"Theory on mixture- of-experts in continual learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05943","last_updated":"2025-02-17T04:48:56Z","snapshot_observed_at":"2026-08-08T17:14:56.769923Z","submitted_at":"2025-02-09T16:06:00Z","title":"Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T17:22:21.844545Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2502.05943"},"observation_digest":"sha256:c9789aa71b84d33c2dc85f228c663de605726e23d32410f4f0a3088b7fb61b85","observation_id":"601d9866-9993-4d64-a292-80b0bc3e99a3","resolution":{"observed_at":"2026-08-08T17:22:21.844545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-06T16:56:13.047216Z","title":"Theory on mixture-of-experts in continual learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12187","last_updated":"2025-07-16T12:34:17Z","snapshot_observed_at":"2026-08-09T03:21:52.358824Z","submitted_at":"2025-07-16T12:34:17Z","title":"Learning, fast and slow: a two-fold algorithm for data-based model adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:56:13.047216Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2507.12187"},"observation_digest":"sha256:f52f519feb08d7806016a8af10310d47f2669ada1134f94144fff7cbaa9481e4","observation_id":"3324e47c-1905-4177-bc58-b93b7f318d50","resolution":{"observed_at":"2026-08-06T16:56:13.047216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-06T14:36:34.678116Z","title":"arXiv preprint arXiv:2406.16437 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18464","last_updated":"2025-07-24T14:39:20Z","snapshot_observed_at":"2026-08-08T23:43:23.195177Z","submitted_at":"2025-07-24T14:39:20Z","title":"DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:36:34.678116Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2507.18464"},"observation_digest":"sha256:9c048b1dd8564df6da096abec86d01c541d53926d89ff4a03d807140a50d32fa","observation_id":"9c823260-410f-4a47-bffd-6a1ef00f8768","resolution":{"observed_at":"2026-08-06T14:36:34.678116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2508.12247","last_updated":"2026-05-22T16:07:20Z","snapshot_observed_at":"2026-08-03T04:23:49.885895Z","submitted_at":"2025-08-17T05:29:58Z","title":"STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-21T22:16:00.668338Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2508.12247"},"observation_digest":"sha256:b8e021151885816902990400f1666d1701bd2e2ca1b846261946faa8fe7df39f","observation_id":"543bb187-5b46-4e2c-be36-d78c56097962","resolution":{"observed_at":"2026-05-21T22:20:42.187048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2508.12247","last_updated":"2026-05-22T16:07:20Z","snapshot_observed_at":"2026-08-03T04:23:49.885895Z","submitted_at":"2025-08-17T05:29:58Z","title":"STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T07:35:27.894693Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2508.12247"},"observation_digest":"sha256:223a74d2f834820a1dc3f28d729f2545bce861b8202e9ddda28b66dce84d321d","observation_id":"22bdfa9b-ff29-4907-bf94-c5ef477ff867","resolution":{"observed_at":"2026-05-25T07:36:41.891120Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-05T17:19:14.823552Z","title":"Theory on mixture-of-experts in continual learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.16512","last_updated":"2025-08-22T16:35:19Z","snapshot_observed_at":"2026-08-07T08:47:26.164430Z","submitted_at":"2025-08-22T16:35:19Z","title":"Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T17:19:14.823552Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2508.16512"},"observation_digest":"sha256:71ff445a9ca5212d27e6e57e0cc7fef7dd6cf050c6a3d0f8497f0e9ae015cb4d","observation_id":"68df0bab-fd88-449f-a713-09f6a8e20ef4","resolution":{"observed_at":"2026-08-05T17:19:14.823552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2511.21343","last_updated":"2026-05-06T13:39:27Z","snapshot_observed_at":"2026-07-06T22:37:03.716474Z","submitted_at":"2025-11-26T12:49:53Z","title":"Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-17T04:55:36.263833Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2511.21343"},"observation_digest":"sha256:f545ba5a8b7e11d379a3fec84d670900349a616b2809daf94a4b8edaf0512559","observation_id":"0295e4d7-bf52-435b-ad49-a3b3297fa4be","resolution":{"observed_at":"2026-05-17T04:59:04.100262Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-03T04:54:46.682044Z","title":"Theory on mixture-of- experts in continual learning.arXiv preprint arXiv:2406.16437, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.03846","last_updated":"2026-06-15T22:17:43Z","snapshot_observed_at":"2026-08-09T04:46:21.995253Z","submitted_at":"2026-02-03T18:59:42Z","title":"PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T04:54:46.682044Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2602.03846"},"observation_digest":"sha256:e6a8ad7e0f806440b2f0a00d3306862c2b5852a5db5e2c8f0cbc047dc5c3880c","observation_id":"bd440bc7-7c20-4864-8df5-131ff1dd8a41","resolution":{"observed_at":"2026-08-03T04:54:46.682044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2605.09355","last_updated":"2026-05-10T06:09:32Z","snapshot_observed_at":"2026-07-06T23:21:26.017420Z","submitted_at":"2026-05-10T06:09:32Z","title":"FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-12T04:14:04.151375Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2605.09355"},"observation_digest":"sha256:6e936b46473ad97ac0c69762224b1e7e4959c770de1d957b9654f2b96231051e","observation_id":"8bf91395-6ca4-4400-8201-68fc14b18f5e","resolution":{"observed_at":"2026-05-12T06:31:25.523847Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2605.14364","last_updated":"2026-05-20T00:19:26Z","snapshot_observed_at":"2026-08-02T11:02:20.818629Z","submitted_at":"2026-05-14T04:46:54Z","title":"MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T02:55:46.616590Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2605.14364"},"observation_digest":"sha256:3fb587aad068f06ee4642fa755a7e76bbb643430e629e9910bfc30807b33b4bf","observation_id":"260e97cd-a405-4c10-9092-69527145b035","resolution":{"observed_at":"2026-05-15T02:59:40.798113Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2605.14364","last_updated":"2026-05-20T00:19:26Z","snapshot_observed_at":"2026-08-02T11:02:20.818629Z","submitted_at":"2026-05-14T04:46:54Z","title":"MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-20T20:39:11.311301Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2605.14364"},"observation_digest":"sha256:2ab397ab9bdef87200484e6ea1e26eb23d8f66a5998d7b0620cf7277e612f6da","observation_id":"a7bd1cff-5127-46f4-9ae9-6630ed7a8369","resolution":{"observed_at":"2026-05-20T20:43:43.497761Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2605.14364","last_updated":"2026-05-20T00:19:26Z","snapshot_observed_at":"2026-08-02T11:02:20.818629Z","submitted_at":"2026-05-14T04:46:54Z","title":"MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-21T07:43:49.653592Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2605.14364"},"observation_digest":"sha256:d2ecaa9d262ffd1da9863fd00fb654e049902ced95436f01049895bb731dc0d4","observation_id":"10fec6b7-9f84-425c-8d92-be1c11fb3c93","resolution":{"observed_at":"2026-05-21T07:44:02.726024Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":"2406.16437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-07-02T13:06:58.490309Z","title":"Theory on mixture- of-experts in continual learning","venue":null,"work_id":"c60f555e-3d3f-42b1-8d79-ac32b8daaebb","year":2024},"citing_paper":{"arxiv_id":"2607.00457","last_updated":"2026-07-01T05:23:56Z","snapshot_observed_at":"2026-07-07T00:06:09.887791Z","submitted_at":"2026-07-01T05:23:56Z","title":"Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-02T13:01:58.866764Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2607.00457"},"observation_digest":"sha256:6b91047134f9f1fbb257128f9ba94462e13c6718520ceae31b93d0af7d2ee3b6","observation_id":"d6c4e379-537e-4c11-84bb-22ad9195cbec","resolution":{"observed_at":"2026-07-02T13:06:58.492512Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16437","snapshot_observed_at":"2026-08-01T14:37:45.457302Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.18716","last_updated":"2026-07-21T05:16:36Z","snapshot_observed_at":"2026-08-05T20:25:01.690963Z","submitted_at":"2026-07-21T05:16:36Z","title":"Continual Video-MLLM Adaptation over Evolving Domains","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T14:37:45.457302Z"},"links":{"cited_paper":"/paper/2406.16437","citing_paper":"/paper/2607.18716"},"observation_digest":"sha256:2a203b5e658cf9b70e12bd0bdb87f00911435f830dd26441ab93ee70cf12f2e2","observation_id":"805f5f9f-e2c2-4ebe-9fe0-77ca64e242ab","resolution":{"observed_at":"2026-08-01T14:37:45.457302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.16437/citation-record","integrity":"/paper/2406.16437/integrity","json":"/paper/2406.16437/citation-record.json","paper":"/paper/2406.16437"},"outbound":[],"paper":{"arxiv_id":"2406.16437","last_updated":"2025-02-19T14:35:07Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T18:35:54.300599Z","submitted_at":"2024-06-24T08:29:58Z","title":"Theory on Mixture-of-Experts in Continual Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2406.16437."}