{"as_of":"2026-08-13T03:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a49e70b59d0d3af6f3105f5cf073d0e2ec05313b6bd9e2b85a0b8d43c0898ce","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T16:49:12.885268Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2411.15208/citation-record","integrity":"/paper/2411.15208/integrity","json":"/paper/2411.15208/citation-record.json","paper":"/paper/2411.15208"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T16:49:13.047705Z","title":null,"venue":null,"work_id":"4f2a04dd-243e-4ab3-bdff-8371a5d326d8","year":2020},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.825256Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:1cbf4319926339813c76782994f0c473a92559388de9c014d7c591659ecfd56d","observation_id":"09afa277-6dd3-483e-b977-f027c95335fc","resolution":{"observed_at":"2026-08-12T16:49:13.050271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.040326Z","title":"Deepmspeptide: peptide detectability prediction using deep learning,","venue":null,"work_id":"539bc66a-4f67-450e-9436-446c7381d66d","year":2020},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.828701Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:9278a669e05458060f16838fe002a3f2ab2bcbee4cbd243a3321275103eb8522","observation_id":"3aa9d2a7-6b50-44f9-aaf0-392870dbaf53","resolution":{"observed_at":"2026-08-12T16:49:13.043134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.032391Z","title":"Sequence- based peptide identification, generation, and property prediction with deep learning: a review,","venue":null,"work_id":"d27ea3dd-6270-4164-81ff-3cf6ec9cc29e","year":2021},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.831402Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:47083dd592038b008ed749cc42e9c301ef5b5b0824a057a834b56574880bae10","observation_id":"773437a7-11ae-419b-bf4e-4434d01b9495","resolution":{"observed_at":"2026-08-12T16:49:13.035177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.025120Z","title":"Explainable deep hypergraph learning modeling the peptide secondary structure prediction,","venue":null,"work_id":"bbb77278-6900-4aa8-b8e8-512f064f96a8","year":2023},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.834130Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:6278ff187abbfbc967001c54cb347871ac5739e2d69a8a8fb080ba7bc2e06aee","observation_id":"24c155c5-f5b0-43ad-a1e7-2a420ec9b31d","resolution":{"observed_at":"2026-08-12T16:49:13.027863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.017821Z","title":"Deep learning for novel antimicro- bial peptide design,","venue":null,"work_id":"2e55e52b-1088-4a18-aaae-ac1d0136dffa","year":2021},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.837040Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:4d7321454f7fa4e5428b8b479f1161ff8f31ba52d5faf15ef10562d6b12920be","observation_id":"c39195e2-3812-4bc9-a2be-5d46c6143264","resolution":{"observed_at":"2026-08-12T16:49:13.020641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.010339Z","title":"Models and data of amplify: a deep learning tool for antimicrobial peptide prediction,","venue":null,"work_id":"a3f69a8c-a1e8-4b8f-9534-dd08c02baa5b","year":2023},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.840025Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:d89e810d602e18a571703f7f9f161609d6236f583c84d5acf1e000c546d5edf4","observation_id":"81064c43-2f55-4afb-83d9-225e8a2f2d93","resolution":{"observed_at":"2026-08-12T16:49:13.013096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:13.002796Z","title":"xdeep-acpep: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning,","venue":null,"work_id":"916905b9-e04c-4a1e-a66a-04425d05b8f1","year":2021},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.843075Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:8aba18988321628ad28e098634f9185a0c016a8e100a3fc96a1ad21349c57cb5","observation_id":"a211c931-68f3-49ee-bfd1-788bc25425fa","resolution":{"observed_at":"2026-08-12T16:49:13.005754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.845777Z","title":"Finding structure in time,","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.845777Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:ba24464bbf7b9bbf29fe64d8adc996865cf91ee2239dbc78e52449d1e15b0b36","observation_id":"666cd7db-bc40-4d35-97b6-a4861f82cfb2","resolution":{"observed_at":"2026-08-12T16:49:12.845777Z","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":"2026-08-12T16:49:12.848445Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.848445Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:81e75149bcb2652c542299903c9026cdb4f7ab6754ee45ebbefd985e0f673638","observation_id":"2daa8183-443d-454b-92fe-33f9e80017ab","resolution":{"observed_at":"2026-08-12T16:49:12.848445Z","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":"2026-08-12T16:49:12.850956Z","title":"Bidirectional recurrent neural net- works,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.850956Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:7906cec28459ff94b25ce943e4de5cd4e92edd5e0a3c449e09acc86355bc1e1d","observation_id":"f5660b63-018b-4363-95d3-c833d4c8b569","resolution":{"observed_at":"2026-08-12T16:49:12.850956Z","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":"2026-08-12T16:49:12.853366Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.853366Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:974fe0b231afb72a7af96f2565016319c2e9801e19679341b24b0394f9fd2cc5","observation_id":"81d4c851-6372-4466-aa0f-cd05e959f134","resolution":{"observed_at":"2026-08-12T16:49:12.853366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-12T16:49:12.855881Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.855881Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:0e6cb1cc356d3e2679731008ae9e2030c30a4d09001b7667df78de02b3f65c41","observation_id":"fed7909b-127f-445e-9f57-a85d6a64b560","resolution":{"observed_at":"2026-08-12T16:49:12.855881Z","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":"2026-08-12T16:49:12.980547Z","title":"Efficient prediction of peptide self-assembly through sequential and graphical encoding,","venue":null,"work_id":"efa05f70-59a4-41f9-bb0f-dadca7b9da99","year":2023},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.858943Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:2f64ea8f4506837e029ffb03ca5c5178b394f9480206c9823129b61e05088588","observation_id":"551d8502-6f51-450d-9b8e-40ca93dfc95a","resolution":{"observed_at":"2026-08-12T16:49:12.983382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.973217Z","title":"Git-mol: A multi-modal large language model for molecular science with graph, image, and text,","venue":null,"work_id":"3304b835-2aa6-41b2-8c4a-98c510d621b5","year":2024},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.861485Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:00b719d157ede022b00674bce4e33794ad515834b89f26d4d8400f3ef411189c","observation_id":"147153ca-701e-4266-a337-17dc3f0516f2","resolution":{"observed_at":"2026-08-12T16:49:12.976092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.966036Z","title":"Graph mixture of experts: Learning on large-scale graphs with explicit diversity modeling,","venue":null,"work_id":"7a1eb90e-e6f6-43ea-8331-5db19e4673f4","year":2024},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.863916Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:b79935f9872c5fa4294f886c660a07618cddc1820b70fdc82a99633313a376ad","observation_id":"697d1538-e72c-4add-a875-63b23040a926","resolution":{"observed_at":"2026-08-12T16:49:12.968755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.866782Z","title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.866782Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:ff5b1e77de94b0994cd12222cf3c3a3cfb0d987de1a6be1927447b7331d6cb28","observation_id":"cd832dca-6a61-4599-962f-4b6f39e70663","resolution":{"observed_at":"2026-08-12T16:49:12.866782Z","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":"2026-08-12T16:49:12.954648Z","title":"Using hierarchi- cal mixture of experts model for fusion of outbreak detection methods,","venue":null,"work_id":"6fb72579-3fac-48bc-bf78-aace5c01d8b5","year":2013},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.869339Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:13abb9313690157cf918a1ce45cd5ad1f69a12d011d2ca0b8294d854af7cce1a","observation_id":"c64d7294-e1c8-4281-8126-04b3280d923f","resolution":{"observed_at":"2026-08-12T16:49:12.957588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.947210Z","title":"A multimodal mixture-of-experts model for dynamic emotion prediction in movies,","venue":null,"work_id":"c6bd118d-bf99-49ae-8a5f-7c76475222de","year":2016},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.871972Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:ef12fd7499bcfdc7f4d086e0f69c10ecbf0ae5b98dd26383c62ee3dc28a97172","observation_id":"b1f55f8a-7dca-4c66-9ca3-98c33a004fca","resolution":{"observed_at":"2026-08-12T16:49:12.949935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.939745Z","title":"Mixture- of-experts for biomedical question answering,","venue":null,"work_id":"85a1c479-d84d-42b6-8a22-a10294c76f91","year":2023},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.874734Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:6639ad88763287776bb5743032bb661dc75d7d14f686d45b58b6a6fcf2d42196","observation_id":"22f94fae-148a-4f11-b45d-e7d1fd14aefa","resolution":{"observed_at":"2026-08-12T16:49:12.942496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.932166Z","title":"Task-customized mixture of adapters for general image fusion,","venue":null,"work_id":"c5f959a9-93a4-4e83-a197-fb9ff220b2a8","year":2024},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.877264Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:074e30af9c34f33624ee0399f0dfb086a73dd86bbc3229927340219eeea2c0e0","observation_id":"a3fa98c5-1228-418f-9a69-4e0b493dac8f","resolution":{"observed_at":"2026-08-12T16:49:12.934986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.924501Z","title":"Co-modeling the sequential and graphical routes for peptide representation learning,","venue":null,"work_id":"d50819c4-64ca-40b1-bfa6-a6845a0c5c96","year":2023},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.880097Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:636bc03a8da9c9f9824eb0b0dc8ee020ff26465e03908501e4238bb987ba69a5","observation_id":"b36a4d97-f6d3-4467-a17a-c9c6fb3be034","resolution":{"observed_at":"2026-08-12T16:49:12.927424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T16:49:12.915164Z","title":"Ampep: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest,","venue":null,"work_id":"8975c2a4-72f6-4d88-b155-e5b31d66ea30","year":2018},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.882767Z"},"links":{"citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:5f83e63de520dc1d7cade35e1438ff35ae499bcd7fd76bc4b49caeae42aad993","observation_id":"5324a11b-eaff-4afa-8dc1-3f67a94c5867","resolution":{"observed_at":"2026-08-12T16:49:12.919487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-08-12T04:14:58.866318Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-12T16:49:12.885268Z","title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T16:49:12.885268Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2411.15208"},"observation_digest":"sha256:6df725cfc38f7e3d281f44fc45fe119c813a565761f5b506b4ad111f36bce77d","observation_id":"d76b5307-3997-4de0-880d-773fabe5ee91","resolution":{"observed_at":"2026-08-12T16:49:12.885268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.15208","last_updated":"2024-11-20T09:52:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T18:37:20.363793Z","submitted_at":"2024-11-20T09:52:52Z","title":"M2oE: Multimodal Collaborative Expert Peptide Model"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":23},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.15208."}