{"as_of":"2026-08-07T10:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a12240776382143ec94908876e9497fc08d9e22bf29a68935611ec4b5b82b68","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:57:25.245487Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T19:57:19.019883Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15103","snapshot_observed_at":"2026-08-04T17:57:25.245487Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10377","last_updated":"2025-09-12T16:09:39Z","snapshot_observed_at":"2026-08-05T12:09:01.164266Z","submitted_at":"2025-09-12T16:09:39Z","title":"Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-04T17:57:25.245487Z"},"links":{"cited_paper":"/paper/2501.15103","citing_paper":"/paper/2509.10377"},"observation_digest":"sha256:407c4d218c21d7590e87c0e2dbd978a2f0e395d3cf1b55ca72eb8b9c31b8b2f5","observation_id":"cf65be58-0a90-4f4b-a38d-d32fbfe1f93c","resolution":{"observed_at":"2026-08-04T17:57:25.245487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15103","snapshot_observed_at":"2026-08-04T13:31:06.541083Z","title":"Each rank could be an expert: Single-ranked mixture of experts lora for multi-task learning.arXiv preprint arXiv:2501.15103, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.00192","last_updated":"2026-07-28T23:06:32Z","snapshot_observed_at":"2026-08-04T13:30:56.253163Z","submitted_at":"2025-09-30T19:10:35Z","title":"Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation","version":3},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-04T13:31:06.541083Z"},"links":{"cited_paper":"/paper/2501.15103","citing_paper":"/paper/2510.00192"},"observation_digest":"sha256:c7840d4189b2d720e903f467b8e29ce37af379840c34d1d23c3b80f047392af0","observation_id":"db05630d-57f8-4472-8431-ac7392949e70","resolution":{"observed_at":"2026-08-04T13:31:06.541083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","version":2},"cited_work":{"arxiv_id":"2501.15103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.15103","snapshot_observed_at":"2026-07-02T19:57:19.019883Z","title":"Each rank could be an expert: Single-ranked mixture of experts LoRA for multi-task learning,","venue":null,"work_id":"56452430-24ce-415c-b435-628ec70a3edb","year":2025},"citing_paper":{"arxiv_id":"2606.01062","last_updated":"2026-05-31T07:08:16Z","snapshot_observed_at":"2026-07-06T23:41:39.172310Z","submitted_at":"2026-05-31T07:08:16Z","title":"DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-28T17:14:53.648013Z"},"links":{"cited_paper":"/paper/2501.15103","citing_paper":"/paper/2606.01062"},"observation_digest":"sha256:e2274d99cb52e9a11b7e29a700bb41cecd608bec203074bfa423e6ffced96452","observation_id":"f4bd96ab-002e-4018-8795-a3c4cc2b2faf","resolution":{"observed_at":"2026-07-01T21:16:14.381989Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","version":2},"cited_work":{"arxiv_id":"2501.15103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.15103","snapshot_observed_at":"2026-07-02T19:57:19.019883Z","title":"Each rank could be an expert: Single-ranked mixture of experts LoRA for multi-task learning,","venue":null,"work_id":"56452430-24ce-415c-b435-628ec70a3edb","year":2025},"citing_paper":{"arxiv_id":"2606.31432","last_updated":"2026-07-02T08:43:43Z","snapshot_observed_at":"2026-07-07T00:05:07.912609Z","submitted_at":"2026-06-30T09:59:05Z","title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-01T05:53:55.141657Z"},"links":{"cited_paper":"/paper/2501.15103","citing_paper":"/paper/2606.31432"},"observation_digest":"sha256:038cc8e05b2d962ed34aa7435a51fe078081420cb1e02ffa75ed53afa3c2a215","observation_id":"917a3134-5e4c-404a-8579-451135f9c617","resolution":{"observed_at":"2026-07-01T10:05:40.984015Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","version":2},"cited_work":{"arxiv_id":"2501.15103","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.15103","snapshot_observed_at":"2026-07-02T19:57:19.019883Z","title":"Each rank could be an expert: Single-ranked mixture of experts LoRA for multi-task learning,","venue":null,"work_id":"56452430-24ce-415c-b435-628ec70a3edb","year":2025},"citing_paper":{"arxiv_id":"2606.31432","last_updated":"2026-07-02T08:43:43Z","snapshot_observed_at":"2026-07-07T00:05:07.912609Z","submitted_at":"2026-06-30T09:59:05Z","title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-02T19:53:25.252752Z"},"links":{"cited_paper":"/paper/2501.15103","citing_paper":"/paper/2606.31432"},"observation_digest":"sha256:5b7a0e89ac1896e426aeecf192e524f1aad6867e758e45817b5b7a270e5ab293","observation_id":"2374fd49-aa79-4004-8a4f-1fb9dd61cd72","resolution":{"observed_at":"2026-07-02T19:57:19.021556Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.15103/citation-record","integrity":"/paper/2501.15103/integrity","json":"/paper/2501.15103/citation-record.json","paper":"/paper/2501.15103"},"outbound":[],"paper":{"arxiv_id":"2501.15103","last_updated":"2025-06-19T04:00:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-30T22:08:16.206174Z","submitted_at":"2025-01-25T06:56:39Z","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.15103."}