{"as_of":"2026-08-14T21:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:df0a27f6ca8a29396de8ac7d8639f92195207cb86f301291cfa4cf9feead90ee","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":"2026-08-14T06:32:32.682623+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":"2026-08-12T11:54:05.034577Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21804","snapshot_observed_at":"2026-08-12T11:54:05.034577Z","title":"Efficient and effective weight-ensembling mixture of experts for multi-task model merging","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00081","last_updated":"2025-04-04T10:10:41Z","snapshot_observed_at":"2026-08-13T19:09:02.154746Z","submitted_at":"2024-11-26T22:53:06Z","title":"Task Singular Vectors: Reducing Task Interference in Model Merging","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T11:54:05.034577Z"},"links":{"cited_paper":"/paper/2410.21804","citing_paper":"/paper/2412.00081"},"observation_digest":"sha256:aebeefc2886426b6ba23d58efb96dc7f2f60c457dc7d8e886c08b48dd2949fb1","observation_id":"6b7f6c33-bd1d-46b5-82a0-bf35901e86dd","resolution":{"observed_at":"2026-08-12T11:54:05.034577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21804","snapshot_observed_at":"2026-08-11T19:24:43.490746Z","title":"Efficient and effective weight-ensembling mixture of experts for multi-task model merging","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06712","last_updated":"2024-12-09T18:01:13Z","snapshot_observed_at":"2026-08-14T12:39:45.946347Z","submitted_at":"2024-12-09T18:01:13Z","title":"How to Merge Your Multimodal Models Over Time?","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T19:24:43.490746Z"},"links":{"cited_paper":"/paper/2410.21804","citing_paper":"/paper/2412.06712"},"observation_digest":"sha256:027177cbcc7c017f72e0cbfe34c938142e8b8bec8f153d392f16e879b34fbc65","observation_id":"797f7a5b-3a0a-4f43-a313-2e1c95d07e27","resolution":{"observed_at":"2026-08-11T19:24:43.490746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21804","snapshot_observed_at":"2026-08-10T22:38:48.724112Z","title":"Efficient and effec- tive weight-ensembling mixture of experts for multi-task model merging","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01230","last_updated":"2025-05-26T13:01:08Z","snapshot_observed_at":"2026-08-12T14:30:32.191018Z","submitted_at":"2025-01-02T12:45:21Z","title":"Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T22:38:48.724112Z"},"links":{"cited_paper":"/paper/2410.21804","citing_paper":"/paper/2501.01230"},"observation_digest":"sha256:f2283eea8ff3dbd9a595c9e8c3871364917b87eb0c11aace075eff2d89b720a9","observation_id":"fc830387-0989-4ecb-8404-f49a33862afe","resolution":{"observed_at":"2026-08-10T22:38:48.724112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21804","snapshot_observed_at":"2026-08-10T20:03:11.187055Z","title":"Efficient and effec- tive weight-ensembling mixture of experts for multi-task model merging","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.09522","last_updated":"2025-01-16T13:17:24Z","snapshot_observed_at":"2026-08-14T17:33:08.484861Z","submitted_at":"2025-01-16T13:17:24Z","title":"Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T20:03:11.187055Z"},"links":{"cited_paper":"/paper/2410.21804","citing_paper":"/paper/2501.09522"},"observation_digest":"sha256:5a281a23436097f35f9830a7fed4d1da8865c1d94874aa736b04c342d70475c3","observation_id":"f25d15e5-d1ac-4c35-bcc4-794c32f6d9ee","resolution":{"observed_at":"2026-08-10T20:03:11.187055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","version":1},"cited_work":{"arxiv_id":"2410.21804","doi":"10.48550/arxiv.2410.21804","metadata_source":"pith","pith_arxiv_id":"2410.21804","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging","venue":"cs.LG","work_id":"8b8bf4aa-e4a0-4a0e-8e3d-d0ac8053dd14","year":2024},"citing_paper":{"arxiv_id":"2509.01548","last_updated":"2025-09-01T15:24:41Z","snapshot_observed_at":"2026-08-13T10:42:34.934443Z","submitted_at":"2025-09-01T15:24:41Z","title":"Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-05T12:34:51.265434Z"},"links":{"cited_paper":"/paper/2410.21804","citing_paper":"/paper/2509.01548"},"observation_digest":"sha256:476f13de61faf08f146a2e5e2dd18836f68bcaee2ae8f0098103d6b1ee63e0b6","observation_id":"e65984c6-590e-4b2c-b280-06719314b3f4","resolution":{"observed_at":"2026-08-05T12:34:51.580787Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.21804/citation-record","integrity":"/paper/2410.21804/integrity","json":"/paper/2410.21804/citation-record.json","paper":"/paper/2410.21804"},"outbound":[],"paper":{"arxiv_id":"2410.21804","last_updated":"2024-10-29T07:16:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T14:31:25.120213Z","submitted_at":"2024-10-29T07:16:31Z","title":"Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.21804."}