{"as_of":"2026-08-09T20:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:24d2db0a9c94c8559724e34cb7d064b773f1decd2d739a254c01499e1ece17cf","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":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":16,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:53:44.127841Z","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":"2026-07-02T17:27:14.931333Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2412.06464","last_updated":"2025-03-06T06:57:34Z","snapshot_observed_at":"2026-08-03T00:27:18.402796Z","submitted_at":"2024-12-09T13:09:04Z","title":"Gated Delta Networks: Improving Mamba2 with Delta Rule","version":3},"reference_index":292,"source":"arxiv_source","source_observed_at":"2026-05-13T14:50:23.991809Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2412.06464"},"observation_digest":"sha256:8987819ca6abd6a8ebb7b5055f94f7ae7c6acad23a51f808706a87a367dfd32b","observation_id":"72da9601-f3ea-4c15-bfc8-571dbe3c91ed","resolution":{"observed_at":"2026-05-13T14:50:24.384889Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-07T13:53:44.127841Z","title":"In-context language learning: Architectures and algorithms.arXiv preprint arXiv:2401.12973, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20836","last_updated":"2025-05-27T07:57:35Z","snapshot_observed_at":"2026-08-09T05:14:09.485665Z","submitted_at":"2025-05-27T07:57:35Z","title":"HAD: Hybrid Architecture Distillation Outperforms Teacher in Genomic Sequence Modeling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:53:44.127841Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2505.20836"},"observation_digest":"sha256:67679a18e9ccfa561c3372d06f9fc6d3f054dce2787a2dfc3405558cf82ef463","observation_id":"344e05e0-6168-4468-aa52-0fdfcf3bd63e","resolution":{"observed_at":"2026-08-07T13:53:44.127841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-07T10:30:12.537488Z","title":"In-context language learning: Architectures and algorithms, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05233","last_updated":"2026-06-03T16:16:54Z","snapshot_observed_at":"2026-08-07T10:20:00.883360Z","submitted_at":"2025-06-05T16:50:23Z","title":"MesaNet: Sequence Modeling by Locally Optimal Test-Time Training","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T10:30:12.537488Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2506.05233"},"observation_digest":"sha256:19935931c9bd8d8e00c317d1f8baa8e1491f7009e709417ee156b2c3a4dc8a17","observation_id":"0739f1d3-dd2f-47c2-8837-c00d5167ed29","resolution":{"observed_at":"2026-08-07T10:30:12.537488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-06T23:48:38.966817Z","title":"In-context language learning: Architectures and algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.16288","last_updated":"2025-06-19T13:05:12Z","snapshot_observed_at":"2026-08-09T19:24:13.217680Z","submitted_at":"2025-06-19T13:05:12Z","title":"Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T23:48:38.966817Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2506.16288"},"observation_digest":"sha256:6ba1255f41f5123abf7ff5359d12d79cad3862b4440ae6ee857bfaefee2c2356","observation_id":"e1e8f6b3-cd97-4661-ace3-9b8430c64eb2","resolution":{"observed_at":"2026-08-06T23:48:38.966817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-06T20:57:47.325633Z","title":"In-context language learning: Architectures and algorithms.arXiv preprint arXiv:2401.12973, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01414","last_updated":"2026-06-16T22:29:23Z","snapshot_observed_at":"2026-08-09T15:53:14.345071Z","submitted_at":"2025-07-02T07:09:09Z","title":"Decomposing Prediction Mechanisms for In-Context Recall","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:57:47.325633Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2507.01414"},"observation_digest":"sha256:91d19cdeb0b5a6cb8a9634e695bae13652e231ca612e1a0ef0fb49ce08a17441","observation_id":"5d63445b-9904-4aa1-9fcd-352603307b18","resolution":{"observed_at":"2026-08-06T20:57:47.325633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-06T15:54:31.579512Z","title":"In-context language learning: Arhitectures and algorithms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14777","last_updated":"2025-07-20T00:33:19Z","snapshot_observed_at":"2026-08-06T15:45:37.558605Z","submitted_at":"2025-07-20T00:33:19Z","title":"Rethinking Memorization Measures and their Implications in Large Language Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:54:31.579512Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2507.14777"},"observation_digest":"sha256:7da4768cddd074ede9a4123d6fa8184b38821ae1b8805dd92a35811f515f0c3c","observation_id":"a94ead0e-82a4-49da-9c2c-6319821ec3fb","resolution":{"observed_at":"2026-08-06T15:54:31.579512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2601.22766","last_updated":"2026-05-08T15:01:26Z","snapshot_observed_at":"2026-07-06T22:43:41.372821Z","submitted_at":"2026-01-30T09:45:35Z","title":"Sparse Attention as Compact Kernel Regression","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T09:24:11.492573Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2601.22766"},"observation_digest":"sha256:38f109f02b292739306736ee388b9e890d43f4c3be7ab087f4c841467b7313c2","observation_id":"00ee54f1-8c3c-4d97-bbba-796d7a9d45c9","resolution":{"observed_at":"2026-05-16T09:27:41.347242Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2604.10946","last_updated":"2026-04-13T03:37:08Z","snapshot_observed_at":"2026-07-06T22:59:27.499664Z","submitted_at":"2026-04-13T03:37:08Z","title":"Learning to Adapt: In-Context Learning Beyond Stationarity","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-10T16:30:36.771589Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2604.10946"},"observation_digest":"sha256:178325c925d9f563c85911ffd40e13cf86598c45686a196d7eba0c23cfcb451a","observation_id":"44e6306c-f9b0-45d3-b0cd-25860c71e5f1","resolution":{"observed_at":"2026-05-11T08:45:59.399516Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2605.12412","last_updated":"2026-05-12T17:09:41Z","snapshot_observed_at":"2026-07-06T23:24:08.763444Z","submitted_at":"2026-05-12T17:09:41Z","title":"Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-13T05:17:34.283917Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2605.12412"},"observation_digest":"sha256:ce1f87c2d65fa315f976a74baf2be1bd2994712661780ece443c8120f65fcc6c","observation_id":"2ab38a35-f223-47df-96dc-229892a2da89","resolution":{"observed_at":"2026-05-13T05:27:19.327680Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2606.03979","last_updated":"2026-07-10T17:52:03Z","snapshot_observed_at":"2026-07-15T23:18:25.559225Z","submitted_at":"2026-06-02T17:56:55Z","title":"Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:13.058872Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2606.03979"},"observation_digest":"sha256:a3284933c99f5400519e5474ee263cfb9b10b7b83c5b76d2c3baacc8480212a9","observation_id":"51f522db-60f3-404c-8eb9-dc5ccff3e43b","resolution":{"observed_at":"2026-07-02T02:26:26.680556Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-13T07:44:25.325808Z","title":"In-context language learning: Architectures and algorithms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03979","last_updated":"2026-07-10T17:52:03Z","snapshot_observed_at":"2026-07-15T23:18:25.559225Z","submitted_at":"2026-06-02T17:56:55Z","title":"Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-13T07:44:25.325808Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2606.03979"},"observation_digest":"sha256:0a51f2266e8bb8859686bf63dcb4bcffb900f8ac6ab6f400cd5a76a1ad25a8ff","observation_id":"dc1debd0-7c2c-40c6-ba79-6e1aee351bfc","resolution":{"observed_at":"2026-07-13T07:44:25.325808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":"2401.12973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-02T17:27:14.931333Z","title":"Barbero, F., Banino, A., Kapturowski, S., Kumaran, D., Madeira Ara ´ujo, J., Vitvitskyi, O., Pascanu, R., and Veliˇckovi´c, P","venue":null,"work_id":"8a31912b-1bd2-448c-8f51-7ab66dbdb4df","year":2024},"citing_paper":{"arxiv_id":"2606.07479","last_updated":"2026-06-05T17:34:07Z","snapshot_observed_at":"2026-08-05T14:47:33.060849Z","submitted_at":"2026-06-05T17:34:07Z","title":"Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification","version":1},"reference_index":152,"source":"arxiv_source","source_observed_at":"2026-06-27T22:03:52.672777Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2606.07479"},"observation_digest":"sha256:376afae79d9e5fb1a425a44f61f09cc77951f31fedfadcae656833a259fb4c00","observation_id":"e3abfc7c-f592-45c5-8e86-5c8aa6986d1f","resolution":{"observed_at":"2026-07-02T17:27:14.934698Z","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":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-07-12T00:51:41.905788Z","title":"Alexander Atanasov, Jacob A Zavatone-Veth, and Cengiz Pehlevan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03660","last_updated":"2026-07-04T01:47:29Z","snapshot_observed_at":"2026-08-07T05:34:25.091820Z","submitted_at":"2026-07-04T01:47:29Z","title":"Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-12T00:51:41.905788Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2607.03660"},"observation_digest":"sha256:e27166bd03c9d2d118d2e32b3de894a3fb8dd140381d1538a1c8a4c78e91a8e4","observation_id":"9759b7d4-a60e-48b1-b1b6-beb5f0e6a568","resolution":{"observed_at":"2026-07-12T00:51:41.905788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-02T05:16:33.278944Z","title":"In-context language learning: Architectures and algorithms.arXiv preprint arXiv:2401.12973,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13425","last_updated":"2026-07-15T04:03:24Z","snapshot_observed_at":"2026-08-03T17:12:53.669172Z","submitted_at":"2026-07-15T04:03:24Z","title":"Data-Efficient Adaptation of LLMs via Attention Head Reweighting","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-02T05:16:33.278944Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2607.13425"},"observation_digest":"sha256:8a90270bddeee3919ffddb746eeefd6dfa9537122e209e123735908842fc2c11","observation_id":"6b7039ce-688f-4801-8c97-a49ecdef920e","resolution":{"observed_at":"2026-08-02T05:16:33.278944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-03T08:50:38.013907Z","title":"In-context language learning: Architectures and algorithms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29353","last_updated":"2026-07-31T12:37:48Z","snapshot_observed_at":"2026-08-05T23:13:16.167684Z","submitted_at":"2026-07-31T12:37:48Z","title":"Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T08:50:38.013907Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2607.29353"},"observation_digest":"sha256:2d6562333195175117953ab14748328ff44cf56086885969e03f97f074a553cc","observation_id":"ef486c4e-ca27-45d3-a0dc-7a42fe14ada8","resolution":{"observed_at":"2026-08-03T08:50:38.013907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12973","snapshot_observed_at":"2026-08-06T00:32:44.036382Z","title":"arXiv preprint arXiv:2401.12973 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.01205","last_updated":"2026-08-02T12:42:20Z","snapshot_observed_at":"2026-08-08T14:31:26.602348Z","submitted_at":"2026-08-02T12:42:20Z","title":"ReBRAC-v2: The Return of the King","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-06T00:32:44.036382Z"},"links":{"cited_paper":"/paper/2401.12973","citing_paper":"/paper/2608.01205"},"observation_digest":"sha256:9b95beeb2a798953f3ba76e7f5519eec5f24a479987ed04b2707bbded6fedf16","observation_id":"78e6e41a-b965-48e2-b837-58b422475eeb","resolution":{"observed_at":"2026-08-06T00:32:44.036382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.12973/citation-record","integrity":"/paper/2401.12973/integrity","json":"/paper/2401.12973/citation-record.json","paper":"/paper/2401.12973"},"outbound":[],"paper":{"arxiv_id":"2401.12973","last_updated":"2024-01-30T18:59:34Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:19:31.847730Z","submitted_at":"2024-01-23T18:59:21Z","title":"In-Context Language Learning: Architectures and Algorithms"},"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 16 inbound Pith citation observations for arXiv:2401.12973."}