{"as_of":"2026-08-07T22:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6d6c1dfd5cbc951f2c0641579e16a225a2d3ddd64f15e0b46e20ad5c49d7c550","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:33:13.031651Z","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":"2026-07-03T04:07:36.851193Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":"2311.15653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-07-03T04:07:36.851193Z","title":"MoDS: Model-Oriented Data Selection for Instruc- tion Tuning","venue":null,"work_id":"bc5df1c5-b91f-4fa1-a315-fb563f35a649","year":2023},"citing_paper":{"arxiv_id":"2412.04468","last_updated":"2026-04-25T07:16:42Z","snapshot_observed_at":"2026-08-03T08:48:57.969106Z","submitted_at":"2024-12-05T18:59:55Z","title":"NVILA: Efficient Frontier Visual Language Models","version":3},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-23T07:42:22.478647Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2412.04468"},"observation_digest":"sha256:053033713852bf7c81750a71b017a7f4eee5925a5cd685664c6d04d131393c3e","observation_id":"976e3530-1f39-4120-b463-541ccb795faf","resolution":{"observed_at":"2026-05-23T07:42:43.091735Z","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":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":"2311.15653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-07-03T04:07:36.851193Z","title":"MoDS: Model-Oriented Data Selection for Instruc- tion Tuning","venue":null,"work_id":"bc5df1c5-b91f-4fa1-a315-fb563f35a649","year":2023},"citing_paper":{"arxiv_id":"2502.10248","last_updated":"2025-02-24T10:12:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-14T15:58:10Z","title":"Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model","version":3},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-05-19T08:02:23.002090Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2502.10248"},"observation_digest":"sha256:4c1c480d96fa54236d14909fed3e89ecb709689d07e16cc86bd96a1895c8fb3a","observation_id":"c3bb6a7c-123a-4557-91b0-7405ff36a7e1","resolution":{"observed_at":"2026-05-19T08:02:23.945355Z","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":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-07T14:33:13.031651Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.18458","last_updated":"2025-06-01T16:00:34Z","snapshot_observed_at":"2026-08-07T20:59:19.597449Z","submitted_at":"2025-05-24T01:57:12Z","title":"A Survey of LLM $\\times$ DATA","version":3},"reference_index":126,"source":"pdf_text","source_observed_at":"2026-08-07T14:33:13.031651Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2505.18458"},"observation_digest":"sha256:b276e5592b64b631c28f9bcbeded62b2b6b7cd8f0593bee1cc9a516fc7325e5b","observation_id":"6a76be25-945e-4607-bc00-8c7b1c55cfd1","resolution":{"observed_at":"2026-08-07T14:33:13.031651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-07T13:16:12.857648Z","title":"Mods: Model-oriented data selection for instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22375","last_updated":"2025-05-29T01:59:00Z","snapshot_observed_at":"2026-08-07T13:06:05.806802Z","submitted_at":"2025-05-28T14:03:02Z","title":"Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:16:12.857648Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2505.22375"},"observation_digest":"sha256:798218c34968711d81d6d35205fa86197b11c4b6ec263714201007b8be7e5d85","observation_id":"c75b3534-689f-4481-899d-f695a8e51882","resolution":{"observed_at":"2026-08-07T13:16:12.857648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-07T12:18:35.076667Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24768","last_updated":"2025-05-30T16:31:05Z","snapshot_observed_at":"2026-08-07T12:11:53.994747Z","submitted_at":"2025-05-30T16:31:05Z","title":"From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:18:35.076667Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2505.24768"},"observation_digest":"sha256:697ab0d4be5405b4918a9f60d4983504233f93ac1d56207a1e9d40520b3184b3","observation_id":"710484db-8632-42f5-8d21-7c052288458a","resolution":{"observed_at":"2026-08-07T12:18:35.076667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-06T12:47:43.389554Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21482","last_updated":"2025-07-29T03:51:00Z","snapshot_observed_at":"2026-08-07T22:01:33.492073Z","submitted_at":"2025-07-29T03:51:00Z","title":"Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T12:47:43.389554Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2507.21482"},"observation_digest":"sha256:f19f570e4faf2d3f9a47e635e8d9576de6e3f47931ce7349e5b5c454b5ffadec","observation_id":"a95737c2-ff48-4f42-ae82-28597335bc0e","resolution":{"observed_at":"2026-08-06T12:47:43.389554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-05T14:13:47.664410Z","title":"Mods: Model-oriented data selection for instruction tuning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.21589","last_updated":"2025-10-22T11:09:23Z","snapshot_observed_at":"2026-08-05T14:13:44.585649Z","submitted_at":"2025-08-29T12:47:27Z","title":"Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning","version":5},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:13:47.664410Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2508.21589"},"observation_digest":"sha256:9dfcdea4dc4fa7eed7eec7db243a55ad26d40c1084dbde82cdfe74e22f547ca7","observation_id":"800f6515-9b60-4c4a-8258-06dbde6f080b","resolution":{"observed_at":"2026-08-05T14:13:47.664410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-08-05T12:52:01.002494Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01221","last_updated":"2025-09-04T09:30:16Z","snapshot_observed_at":"2026-08-07T10:55:12.173457Z","submitted_at":"2025-09-01T08:06:49Z","title":"DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T12:52:01.002494Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2509.01221"},"observation_digest":"sha256:7331309d3ebc4e53d549b5e2b243d7937a0fa9955b54001d4e3b9103b54ecfe9","observation_id":"2c6005bb-6faf-4a17-99c6-11629a656cc0","resolution":{"observed_at":"2026-08-05T12:52:01.002494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":"2311.15653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-07-03T04:07:36.851193Z","title":"MoDS: Model-Oriented Data Selection for Instruc- tion Tuning","venue":null,"work_id":"bc5df1c5-b91f-4fa1-a315-fb563f35a649","year":2023},"citing_paper":{"arxiv_id":"2605.30857","last_updated":"2026-05-29T05:28:36Z","snapshot_observed_at":"2026-07-30T16:26:50.901854Z","submitted_at":"2026-05-29T05:28:36Z","title":"MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T22:53:48.040141Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2605.30857"},"observation_digest":"sha256:925c1e16efdb43317af6ee3d8e05247041f7c7b255f09ab95ca61ee7c20369a8","observation_id":"684eab1d-0b66-41c2-a5bc-abde8a3e0001","resolution":{"observed_at":"2026-07-01T19:16:01.035498Z","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":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":"2311.15653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-07-03T04:07:36.851193Z","title":"MoDS: Model-Oriented Data Selection for Instruc- tion Tuning","venue":null,"work_id":"bc5df1c5-b91f-4fa1-a315-fb563f35a649","year":2023},"citing_paper":{"arxiv_id":"2606.00400","last_updated":"2026-05-29T22:32:55Z","snapshot_observed_at":"2026-08-02T08:39:16.367723Z","submitted_at":"2026-05-29T22:32:55Z","title":"Dynamic Proxy-Mixing: Transferring Replay Controllers from Small to Large Models for Continual Instruction Tuning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-28T22:58:30.457420Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2606.00400"},"observation_digest":"sha256:29b38af1e87370b20d7545647a33bdfe583e7f60cf50219e0a1d227fc8074f49","observation_id":"2ec530ac-58d9-427c-a2a6-01bd5b4bf00c","resolution":{"observed_at":"2026-06-28T23:02:46.609536Z","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":"2311.15653","last_updated":"2023-11-27T09:33:13Z","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning","version":1},"cited_work":{"arxiv_id":"2311.15653","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.15653","snapshot_observed_at":"2026-07-03T04:07:36.851193Z","title":"MoDS: Model-Oriented Data Selection for Instruc- tion Tuning","venue":null,"work_id":"bc5df1c5-b91f-4fa1-a315-fb563f35a649","year":2023},"citing_paper":{"arxiv_id":"2606.10706","last_updated":"2026-06-09T11:09:58Z","snapshot_observed_at":"2026-08-06T18:35:58.803050Z","submitted_at":"2026-06-09T11:09:58Z","title":"Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-06-27T14:12:14.785572Z"},"links":{"cited_paper":"/paper/2311.15653","citing_paper":"/paper/2606.10706"},"observation_digest":"sha256:0cd66687eecc1232952798aefed549471f1cb0bcc77fb1b282947561b3369d68","observation_id":"d87d8d73-aed0-4292-a8ce-37fc28b54d4e","resolution":{"observed_at":"2026-07-03T04:07:36.852543Z","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/2311.15653/citation-record","integrity":"/paper/2311.15653/integrity","json":"/paper/2311.15653/citation-record.json","paper":"/paper/2311.15653"},"outbound":[],"paper":{"arxiv_id":"2311.15653","last_updated":"2023-11-27T09:33:13Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T16:52:57.823137Z","submitted_at":"2023-11-27T09:33:13Z","title":"MoDS: Model-oriented Data Selection for Instruction Tuning"},"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 11 inbound Pith citation observations for arXiv:2311.15653."}