{"as_of":"2026-08-20T07:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38b256ebdf740e3f4b47302b5d0395c12bd1a8da62328ca064a417484a73da96","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:21:22.661675Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.12090/citation-record","integrity":"/paper/2608.12090/integrity","json":"/paper/2608.12090/citation-record.json","paper":"/paper/2608.12090"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:21:23.359853Z","title":null,"venue":null,"work_id":"ef0c5379-ea59-45c1-bba1-e5dab3c76b67","year":2021},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.441181Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:5d86ceca4f2f4bad8b26fb332560673133835e8c4f79be54cabe048eb5f58c9d","observation_id":"e2b586d2-d2cc-46a4-8f06-1148551677ee","resolution":{"observed_at":"2026-08-16T00:21:23.363461Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.348842Z","title":"Comparative assessment of protein large language models for enzyme commission number prediction.BMC bioinfor- matics, 26(1):68, 2025","venue":null,"work_id":"000e5693-bb60-4185-87ee-fd29183d6298","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.445339Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:eb60bc34c38bb54102294246b7d83db2f03545b64bc1b6ce3faffd8d1a4e40f6","observation_id":"a0373031-d8f1-4df2-93b1-bcc8b89fd58b","resolution":{"observed_at":"2026-08-16T00:21:23.352661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.337603Z","title":"Language mod- els can identify enzymatic binding sites in pro- tein sequences.Computational and structural biotechnology journal, 23:1929–1937, 2024","venue":null,"work_id":"d026eb98-cf5a-4d71-bd25-d012f704149d","year":1929},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.449601Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:ff0a62a1abb33fade378b84a3c8d8a1422d2988237fe444c3e5964ad9cebb7df","observation_id":"3b29df39-ef54-444f-b691-d459a6c16291","resolution":{"observed_at":"2026-08-16T00:21:23.341478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.326608Z","title":"Iglm: Infilling language model- ing for antibody sequence design.Cell systems, 14(11):979–989, 2023","venue":null,"work_id":"9f4f6a4d-d555-4da4-947a-91689d33ccac","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.454332Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:5db98553da6b7bbaf17f36e9a9206132cc81cab94f40ef1309f0f6ee9a2faa16","observation_id":"19f9d383-93e1-46e2-b9a3-516103b6cf6c","resolution":{"observed_at":"2026-08-16T00:21:23.330449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.315372Z","title":"Protein language models: Applications and perspectives","venue":null,"work_id":"9c3064ea-e567-4133-9432-c582783e5c6d","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.458849Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:878e71f24e3fb43af27ca60d12de0b7b947536b06dc64cb154e903190def89fb","observation_id":"66be9fb1-5ff8-4a0b-b682-d3697ca794da","resolution":{"observed_at":"2026-08-16T00:21:23.319494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.01644","last_updated":"2018-11-22T23:40:00Z","snapshot_observed_at":"2026-08-09T22:50:03.459615Z","submitted_at":"2016-10-05T20:59:01Z","title":"Understanding intermediate layers using linear classifier probes","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.01644","snapshot_observed_at":"2026-08-16T00:21:22.462990Z","title":"Under- standing intermediate layers using linear classi- fier probes.arXiv preprint arXiv:1610.01644, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.462990Z"},"links":{"cited_paper":"/paper/1610.01644","citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:fd97bdbf1de7ef47175418657f4b44c479698d15fc6ee98c430e4d48274b36c0","observation_id":"5ec56ba0-229c-4178-87b9-ceb0f44ee156","resolution":{"observed_at":"2026-08-16T00:21:22.462990Z","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":"2026-08-16T00:21:23.303636Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"45f6669a-bc27-4862-b58f-fc06f8c40022","year":2016},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.468589Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:17cfb3b0c72502f2763fd978373ae0a24eeae82b87e4a0ac03ced1549e6e70f3","observation_id":"5e65019e-b04e-4107-a0de-5c93eaa6f23b","resolution":{"observed_at":"2026-08-16T00:21:23.307681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.291603Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"3312d325-5abe-41ab-b0ac-b5fcae8f3673","year":2009},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.472432Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:c82b26237b7f1087035990702c607e15ca572c837d1b63f8f964392d1c55e789","observation_id":"347f3399-d8dd-46ef-8981-c7ff730b424f","resolution":{"observed_at":"2026-08-16T00:21:23.295656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.279888Z","title":"Bert: Pre-training of deep bidirectional transformers for language un- derstanding","venue":null,"work_id":"36831ead-957e-49ac-9dc3-e1cad6d88597","year":2019},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.476434Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:9828ef8a79b7bf2f4a1ae44fe402cf112cd22cf856774f4c0504f4d54cfdbb85","observation_id":"a443e45f-a347-478e-80c8-954e4ca1b61b","resolution":{"observed_at":"2026-08-16T00:21:23.283735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.268416Z","title":"The geometry of hidden representations of large transformer models.Ad- vances in Neural Information Processing Sys- tems, 36:51234–51252, 2023","venue":null,"work_id":"2bc207ec-a306-4ef0-add9-fbd4db7e552d","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.480275Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:ff8afe0f3e44c86022804ed39020ee1116a63f14d9649b82e93e7bf8335022c1","observation_id":"d0d4df10-5927-4a3d-8a33-525f2975ea3a","resolution":{"observed_at":"2026-08-16T00:21:23.272496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.15222","last_updated":"2021-03-28T21:56:26Z","snapshot_observed_at":"2026-08-13T22:18:06.264865Z","submitted_at":"2020-06-26T21:50:17Z","title":"BERTology Meets Biology: Interpreting Attention in Protein Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.15222","snapshot_observed_at":"2026-08-16T00:21:22.484806Z","title":"Bertology meets bi- ology: Interpreting attention in protein language models.arXiv preprint arXiv:2006.15222, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.484806Z"},"links":{"cited_paper":"/paper/2006.15222","citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:607a753340795a2423b5858139e6d6716cf1f9d57a77a29ad5fcb740e05cd5f9","observation_id":"5d0dcaff-3911-463d-9c45-99b05533631d","resolution":{"observed_at":"2026-08-16T00:21:22.484806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02013","last_updated":"2025-06-15T18:27:17Z","snapshot_observed_at":"2026-08-14T18:24:15.384302Z","submitted_at":"2025-02-04T05:03:42Z","title":"Layer by Layer: Uncovering Hidden Representations in Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02013","snapshot_observed_at":"2026-08-16T00:21:22.489512Z","title":"Layer by layer: Uncovering hid- den representations in language models.arXiv preprint arXiv:2502.02013, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.489512Z"},"links":{"cited_paper":"/paper/2502.02013","citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:d8e24383684a4712f9e3199bdc31aadb081b076234c3495a5c3f90de08031349","observation_id":"62f72406-c10e-48c5-811f-6bbf8a4b46d6","resolution":{"observed_at":"2026-08-16T00:21:22.489512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.10927","last_updated":"2025-06-03T10:08:01Z","snapshot_observed_at":"2026-08-17T22:30:36.235531Z","submitted_at":"2025-02-15T23:08:02Z","title":"The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.10927","snapshot_observed_at":"2026-08-16T00:21:22.493770Z","title":"The underlying structures of self- attention: symmetry, directionality, and emer- gent dynamics in transformer training.arXiv preprint arXiv:2502.10927, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.493770Z"},"links":{"cited_paper":"/paper/2502.10927","citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:bd7698024cc1d537cb90fd6a1bdf5281c8e984f29b81af456e24a456eeffd91d","observation_id":"a2149d53-b84a-42a6-9aca-2cfb69cef741","resolution":{"observed_at":"2026-08-16T00:21:22.493770Z","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":"2026-08-16T00:21:23.257095Z","title":"Generative pretraining from pixels","venue":null,"work_id":"dd205235-53e1-4e12-879c-7794737bd0a8","year":2020},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.498312Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:70f83b9f4df647f9062bf356e09630b01bd5e1eb0ca98c16d8e46294ce508fe1","observation_id":"e8f32944-4ea1-4f36-98e7-69ac7f450143","resolution":{"observed_at":"2026-08-16T00:21:23.261091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.245220Z","title":"Evolutionary-scale prediction of atomic-level protein structure with a language model.Sci- ence, 379(6637):1123–1130, 2023","venue":null,"work_id":"b07c44e8-fb85-4ca0-9a57-4d3f09d74345","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.502413Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:2d766b779f2e089817cfddc56aa40f36d216dc97972f14301ff42c6a8a9e4bd5","observation_id":"bc828bb8-22de-4f9b-b73f-0d7d552c9403","resolution":{"observed_at":"2026-08-16T00:21:23.249126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.00376","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:21:22.749377Z","title":"Layer probing improves kinase functional prediction with protein language models.arXiv preprint arXiv:2512.00376, 2025","venue":null,"work_id":"439f352f-f5b6-4338-a762-f309635a5627","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.506486Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:5713bfc5827901a52e05c6677b792400fb343074baf178c90055e41839441465","observation_id":"9a6fbca4-6151-4207-a1c1-f91619c249eb","resolution":{"observed_at":"2026-08-16T00:21:22.759904Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.510830Z","title":"Visualizing and understanding convolutional networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.510830Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:2bc1832d0479504f59fffcae470563b69424b2c0be708f24a26cc9551fcf61c7","observation_id":"4927b43e-4f8c-446e-a58e-f632969c07be","resolution":{"observed_at":"2026-08-16T00:21:22.510830Z","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":"2026-08-16T00:21:23.227368Z","title":"Quantify- ing uncertainty in protein representations across models and tasks.Nature Methods, pages 1–9, 2026","venue":null,"work_id":"0f0969d9-fc37-4edd-9f41-33e454b5102f","year":2026},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.514799Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:bb71b4ad114659d4f95dc4deb2303e31af9349b5fe6a305ebb9c9b1c834f63c6","observation_id":"787edd45-84a6-490c-93e8-b5a8cf46d50b","resolution":{"observed_at":"2026-08-16T00:21:23.231255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.216735Z","title":"Local fitness land- scape of the green fluorescent protein.Nature, 533(7603):397–401, 2016","venue":null,"work_id":"55d44ee0-f0b5-499a-b4db-551af412f94a","year":2016},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.518795Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:572197a6b7178e1f5ccb4e418910b7d43bccdcc88da6ffd738ad17078ef4bc5e","observation_id":"affba33a-c15a-4ff1-98ed-99d92615f57f","resolution":{"observed_at":"2026-08-16T00:21:23.220465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.206534Z","title":"A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain.Current biology, 24(22):2643–2651, 2014","venue":null,"work_id":"ca3c5cea-da52-42a8-8aa6-a9f6164453f3","year":2014},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.523176Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:1cf87e8911bb8cca2180c5c4bbe958b8fa06a49665d54ebc7fc09a97636c7726","observation_id":"9c51a8cd-0dad-4a8c-8d9e-8c4291e8266b","resolution":{"observed_at":"2026-08-16T00:21:23.210185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.195937Z","title":"Global analysis of protein folding using mas- sively parallel design, synthesis, and testing.Sci- ence, 357(6347):168–175, 2017","venue":null,"work_id":"f7b32486-d31d-4d27-b501-57699afc82b5","year":2017},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.527272Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:849144ced0a9e0c5d1347eb1a2577098fbdd25e4b63299369d9682797ca8a044","observation_id":"18ec8e7e-ca78-481b-a162-5cb71479aa5f","resolution":{"observed_at":"2026-08-16T00:21:23.199597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.185957Z","title":"Mega-scale experimental analysis of protein folding stability in biology and design.Nature, 620(7973):434–444, 2023","venue":null,"work_id":"9b999b79-6ef7-4f70-8b22-facb2b1c20d7","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.531933Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:243a1d12034a67f060440ebd93db434f32d3b897b6550d72da5662c32e21661b","observation_id":"98f5cdba-1841-4feb-b522-0e6b455cdbf9","resolution":{"observed_at":"2026-08-16T00:21:23.189553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.175765Z","title":"Large language models generate functional protein se- quences across diverse families.Nature biotech- nology, 41(8):1099–1106, 2023","venue":null,"work_id":"808e11ff-f7e8-492e-bb18-83976998a0aa","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.536168Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:6c87b15356e59038c5bd1bf3f2d94bdd434242ab3811c636fb8dc8aaf1d6b341","observation_id":"679cd716-47f9-41a1-b306-fb4896a38368","resolution":{"observed_at":"2026-08-16T00:21:23.179462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.165158Z","title":"Meltome atlas—thermal proteome stability across the tree of life.Nature methods, 17(5):495–503, 2020","venue":null,"work_id":"07086b25-a915-4e52-a5ef-2607d31d8f0c","year":2020},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.540403Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:a324ed0ebfd11ca1d2f66e786d8caca1fa066fb0fc9cac993b3802d26bac4756","observation_id":"f395fae3-9f83-4bb2-bd55-0fc44dd02ec7","resolution":{"observed_at":"2026-08-16T00:21:23.168764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.154485Z","title":"Deepsol: a deep learning framework for sequence-based protein solubility prediction.Bioinformatics, 34(15):2605–2613, 2018","venue":null,"work_id":"01525953-9abf-4053-9245-f2e0eff2c2e5","year":2018},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.544763Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:e2889f58ffe7f3e468420ceaf1312e07332e55d77cda19184890aae5e82a339d","observation_id":"24f4290b-2258-4710-8b3a-8534c969ddd2","resolution":{"observed_at":"2026-08-16T00:21:23.158196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.143473Z","title":"Deeploc 2.0: multi-label subcellular localization prediction using protein language models.Nucleic acids research, 50(W1):W228–W234, 2022","venue":null,"work_id":"3fc0aab4-6213-46c4-8ef7-64eb35bde89c","year":2022},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.548652Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:d1ab510fbfc69e5604e09a0d9b7dc242746ad0e4a31d3218f8eaf3fa997387e9","observation_id":"cdcafc98-16db-41b2-b238-b35f141b23fa","resolution":{"observed_at":"2026-08-16T00:21:23.147480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.132682Z","title":null,"venue":null,"work_id":"1aaec442-dda2-47b9-bc67-70aa733573ac","year":2022},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.553502Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:7c0cda10de4b180904aa21c5db93bcb8e151cdcdc5b5da3fbe7f1e8c68904f6c","observation_id":"095ce127-bc6a-4f5b-a75e-f67543c31313","resolution":{"observed_at":"2026-08-16T00:21:23.136468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.120354Z","title":"Protein embeddings and deep learning pre- dict binding residues for various ligand classes","venue":null,"work_id":"af1fad85-6bf6-4902-b791-06e68b9729d2","year":2021},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.557835Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:360e4847621c580a2cba39d05233a79fd828793a415b86625f487c237fce3997","observation_id":"7ac8219f-c06e-4e3e-b970-3fa94f85d856","resolution":{"observed_at":"2026-08-16T00:21:23.124372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.108151Z","title":null,"venue":null,"work_id":"6e4bd8f4-af20-4bf1-ba2b-93e3ed651955","year":1983},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.562059Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:c0ac4d5f0da13011d49769a178e0ad927bb34949b8ca21c09c0e42e33046f1cb","observation_id":"eec03d69-071c-4580-b4bd-ee33c1573289","resolution":{"observed_at":"2026-08-16T00:21:23.111790Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.095187Z","title":"ESM Cambrian: Revealing the mysteries of proteins with unsupervised learn- ing.EvolutionaryScale Website, 12 2024","venue":null,"work_id":"859a70e8-c80d-4fe7-9b20-f5fff84edda0","year":2024},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.566476Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:1769e33680adb13fd8860554133178213e6c4e12aa9a69c8d5fd13dce2b1d35a","observation_id":"b84c58da-917e-4e2d-83f5-6ea38a859b11","resolution":{"observed_at":"2026-08-16T00:21:23.099597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.080365Z","title":"Prottrans: to- ward understanding the language of life through self-supervised learning.IEEE transactions on pattern analysis and machine intelligence, 44(10):7112–7127, 2021","venue":null,"work_id":"732107dd-ce5b-4225-a150-227d88499d88","year":2021},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.571864Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:957df1db027e86dd2d49ab291b4fcf80a6493ac7fb8fc9d82db993553b5078ae","observation_id":"79d08c83-8a84-4280-b843-c20f2a796559","resolution":{"observed_at":"2026-08-16T00:21:23.084985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.066009Z","title":"Bilingual language model for protein sequence and structure.NAR Genomics and Bioinformat- ics, 6(4):lqae150, 2024","venue":null,"work_id":"0d971e30-f44b-4c31-a6cd-83f7d355b09b","year":2024},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.575612Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:0b6c579ab080da14a0488245701fdcde66f592368b795f2ea47fb48308c1c812","observation_id":"ab0fa540-dd3a-43a3-a0a4-786644be7b19","resolution":{"observed_at":"2026-08-16T00:21:23.070869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.049854Z","title":"Progen2: exploring the boundaries of protein language models.Cell systems, 14(11):968–978, 2023","venue":null,"work_id":"f1c982bc-89ae-4b76-a8d2-fcfc180c8ce4","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.579772Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:e996cba7fb1c60828eaf9a183a3ddec07af01dc4d1cf85f661be09b80c978335","observation_id":"827f0015-d9cb-4368-a9f6-f3d668d5924e","resolution":{"observed_at":"2026-08-16T00:21:23.054851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.036067Z","title":"Protgpt2 is a deep unsupervised lan- guage model for protein design.Nature commu- nications, 13(1):4348, 2022","venue":null,"work_id":"59373ae4-d78c-4916-a9ed-9f9d7fbf342d","year":2022},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.584750Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:77cf44bc98d3129f9afe12926c8a322159f20cb7b7c1cba50c41a2e45a18e8ba","observation_id":"0da90fa4-31d1-4ecb-89b6-75c94efe179f","resolution":{"observed_at":"2026-08-16T00:21:23.040928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:23.021639Z","title":"A new algorithm for data com- pression.The C Users Journal, 12(2):23–38, 1994","venue":null,"work_id":"17f6fdc1-abb5-4bc0-bd72-927eaa789953","year":1994},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.588991Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:c67747b61e7fdcc2dcabb85138e3079d4b77179a80b07db162a27114752d7979","observation_id":"dc7a5a5d-2b50-49c3-a959-a369965e136f","resolution":{"observed_at":"2026-08-16T00:21:23.026298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.593624Z","title":"Attention is all you need.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.593624Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:2384db4d8a1e84430eff033fafa9424d96c49ed478b25853af85f16f50fd1a0b","observation_id":"94dbf839-807e-42fd-9695-17a334e5a92e","resolution":{"observed_at":"2026-08-16T00:21:22.593624Z","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":"2026-08-16T00:21:22.598320Z","title":"Exploring the limits of transfer learning with a unified text- to-text transformer.Journal of machine learning research, 21(140):1–67, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.598320Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:51aaa3b444ce7cd9f9396aa929532e71b1e3ee18e3307a325a7b72298fb03f0a","observation_id":"b82ec052-1763-4f1e-a2a1-247daaa0045c","resolution":{"observed_at":"2026-08-16T00:21:22.598320Z","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":"2026-08-16T00:21:22.992205Z","title":"Improving language understanding by generative pre-training, 2018","venue":null,"work_id":"28f29221-6d02-4e6c-8ee8-9d5fca72b866","year":2018},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.602776Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:373678b629d96898ddf0124b84e8f9649941e02b7cfa172f5eac74ad41e3ad45","observation_id":"b01ff8e6-3a95-42d8-9f72-49bd30b1f195","resolution":{"observed_at":"2026-08-16T00:21:22.996272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.979395Z","title":"Estimating the intrinsic dimension of datasets by a minimal neighbor- hood information.Scientific reports, 7(1):12140, 2017","venue":null,"work_id":"a7387972-2693-45c2-9ba2-3b98a642c2ef","year":2017},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.606977Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:56acd9642e628145a5787dedfc4f5f7f79a348b83a75ab62e0f3cc0c7561e693","observation_id":"505a94e6-87b6-4391-b8c7-4cdf28040293","resolution":{"observed_at":"2026-08-16T00:21:22.983483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.966182Z","title":"Hierarchical nucleation in deep neural networks.Advances in Neural Information Processing Systems, 33:7526–7536, 2020","venue":null,"work_id":"8bfd19c4-cb27-45ec-813b-de7e967ef04e","year":2020},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.611548Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:7e74b3682b175eb92958e28ebad8fe9acd18d361a3932beece80bef2ef091d91","observation_id":"c7e8de6a-1124-4b18-bdbb-ee0359f5db26","resolution":{"observed_at":"2026-08-16T00:21:22.970591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.615709Z","title":null,"venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.615709Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:6e200a7b38f97e65a2bd1f4583f56e35bfb5868f8d63758710706c64827e97ba","observation_id":"57120472-2682-4746-81d2-b1f5e2ce2285","resolution":{"observed_at":"2026-08-16T00:21:22.615709Z","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":"2026-08-16T00:21:22.944733Z","title":"Supervised learning of protein melting temperature: Cross-species vs","venue":null,"work_id":"2fb7a177-6bff-485b-8eb5-8789a66b37ab","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.620030Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:d472b1251daae9605d5dcb296f94080be253ecd5ea70ef21daedceb5428027b7","observation_id":"c55c9f16-10be-4c00-8cf9-25f2dd9193fd","resolution":{"observed_at":"2026-08-16T00:21:22.949301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.931861Z","title":"Data splitting to avoid information leakage with datasail.Nature Communications, 16(1):3337, 2025","venue":null,"work_id":"0319b081-1d58-46d0-b1aa-2b7acff32289","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.625050Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:df8045c177544f0df30a97ea8dee52e223cd4936fb558f26b5e627371b3c5c28","observation_id":"2333d5cc-af7b-426c-9024-0b885ce5865a","resolution":{"observed_at":"2026-08-16T00:21:22.936098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.629326Z","title":"Bert rediscovers the classical nlp pipeline","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.629326Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:34aaef254901d7eb22556e64468ac248a56e0191b2d5ad42891c43c7e8cbdcef","observation_id":"038a28ee-0d4c-4795-9a68-e352c6b85eb9","resolution":{"observed_at":"2026-08-16T00:21:22.629326Z","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":"2026-08-16T00:21:22.910067Z","title":"Uniprot: the universal protein knowledgebase in 2025.Nucleic acids research, 52(D 1):D609– D617, 2024","venue":null,"work_id":"bfd76cc2-1399-494d-bcdd-dd3404a33618","year":2025},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.633252Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:8f246fd55ceaf7291bccc407756df06bc7dd1f355737a775dcf824eeef05ff9c","observation_id":"7079a065-90e6-4582-9f3f-37b07189fba0","resolution":{"observed_at":"2026-08-16T00:21:22.914030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.897476Z","title":"Mgnify: the microbiome sequence data analy- sis resource in 2023.Nucleic acids research, 51(D1):D753–D759, 2023","venue":null,"work_id":"e20fc53c-06c0-450f-a360-77fcb517b0d7","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.637567Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:3b84fa3b80304a5596c6d8c6e6bd64c294a53cef030d58f7e85d40da30368a36","observation_id":"702b9f0f-3f7e-4e3d-88cd-14993b7b4dc0","resolution":{"observed_at":"2026-08-16T00:21:22.901606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.883800Z","title":"The genome portal of the de- partment of energy joint genome institute: 2014 updates.Nucleic acids research, 42(D1):D26– D31, 2014","venue":null,"work_id":"b4b0be30-8de2-42e3-8a74-da782bfac82c","year":2014},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.641738Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:1ebe801257ae997d9c9205ffd5112f967297663bfceb993b60cfb39783e1e758","observation_id":"d8e46ead-7a25-4d08-940e-9e656c8f5589","resolution":{"observed_at":"2026-08-16T00:21:22.888881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.870540Z","title":"Protein-level assembly increases pro- tein sequence recovery from metagenomic sam- 17 ples manyfold.Nature methods, 16(7):603–606, 2019","venue":null,"work_id":"88be5383-7e99-400c-9d71-598809465130","year":2019},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.645690Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:b65235ccfbc8d6e0a91d7d91accc8cdf4eb1edd36ef58760944c7b7701d87ef8","observation_id":"c4204ce7-ed48-4e50-aa84-2c4e76e6257b","resolution":{"observed_at":"2026-08-16T00:21:22.874828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.856891Z","title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.Bioinformat- ics, 22(13):1658–1659, 2006","venue":null,"work_id":"b468ece9-8b49-402f-ba61-3abf2fd6f33f","year":2006},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.649584Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:0ab1bc5d0b18fc3e3498da2275a0be63b27fb2c4113522d84d460b92d5e748fa","observation_id":"d753ca65-2053-4d42-afab-7dde942d2669","resolution":{"observed_at":"2026-08-16T00:21:22.861405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.653467Z","title":"Evaluating protein transfer learning with tape.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.653467Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:23f8b5a7e333434b0eb5f4a0711d08291ad6a05b08a9b26b89a7efa1fc302e8d","observation_id":"c4231b2b-06df-411c-bea7-74a50a8568ae","resolution":{"observed_at":"2026-08-16T00:21:22.653467Z","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":"2026-08-16T00:21:22.835277Z","title":"Proteingym: Large- scale benchmarks for protein fitness prediction and design.Advances in neural information processing systems, 36:64331–64379, 2023","venue":null,"work_id":"b464aef5-e0ce-4935-96ee-93d40ba11b4b","year":2023},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.657311Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:8c2194d6a13914ff847b8ea9ec1cc580cff0ccda0e723a741130a16abd5bb5dc","observation_id":"e8f0e8f7-6f26-4c0e-92fb-f1baf6f94c3c","resolution":{"observed_at":"2026-08-16T00:21:22.839603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:21:22.822308Z","title":"Kalinina","venue":null,"work_id":"74699920-1c48-45c4-aca0-4fa46e82710f","year":2026},"citing_paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T00:21:22.661675Z"},"links":{"citing_paper":"/paper/2608.12090"},"observation_digest":"sha256:cb8724e503e9b3bf1925aef0e1fb089431d548a3ea8fa5af1517055d3833c1da","observation_id":"6a449864-71e8-4a6e-aabb-9a2d6ab84b6b","resolution":{"observed_at":"2026-08-16T00:21:22.826276Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.12090","last_updated":"2026-08-13T07:47:02Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T11:37:43.941012Z","submitted_at":"2026-08-12T14:13:48Z","title":"Task- and dataset-specific information in protein language models"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":37},"total_outbound_references":52},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2608.12090."}