{"as_of":"2026-08-21T15:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cbe6a989dda9bea5296c8c7a2d00cd7038063c9beb4e9adf5e97afd5b1076edd","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T11:52:37.200189Z","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":"2026-07-02T02:26:26.681775Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-08-11T11:52:37.200189Z","title":"Learning to generate instruction tuning datasets for zero-shot task adaptation.arXiv preprint arXiv:2402.18334,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14964","last_updated":"2025-08-07T09:12:17Z","snapshot_observed_at":"2026-08-14T18:22:07.618078Z","submitted_at":"2024-12-19T15:44:01Z","title":"Efficient Knowledge Injection in LLMs via Self-Distillation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T11:52:37.200189Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2412.14964"},"observation_digest":"sha256:2cf2f2771f9f9fa7d606a136c9d6387ee40c85c069cf118ce73c1bc63821daeb","observation_id":"a180c0bd-0333-46ef-aed6-f19a61b16814","resolution":{"observed_at":"2026-08-11T11:52:37.200189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-08-11T11:18:33.595166Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15652","last_updated":"2024-12-20T08:07:11Z","snapshot_observed_at":"2026-08-19T18:47:39.592738Z","submitted_at":"2024-12-20T08:07:11Z","title":"Error-driven Data-efficient Large Multimodal Model Tuning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T11:18:33.595166Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2412.15652"},"observation_digest":"sha256:48366714649f91bda7fc8d0e8d56b7dd3db83a4c47254d51c961fbfc644ba334","observation_id":"60719554-ca92-453e-9c60-d13603a36822","resolution":{"observed_at":"2026-08-11T11:18:33.595166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-08-07T06:04:33.139683Z","title":"Learning to generate instruction tuning datasets for zero-shot task adaptation.arXiv preprint arXiv:2402.18334, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06266","last_updated":"2025-06-13T17:58:55Z","snapshot_observed_at":"2026-08-16T06:23:05.141323Z","submitted_at":"2025-06-06T17:48:23Z","title":"Cartridges: Lightweight and general-purpose long context representations via self-study","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:33.139683Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2506.06266"},"observation_digest":"sha256:aba21a7189c867153012a7c613a8fcb15558ee6766c3f0cc16965fc6045b19d7","observation_id":"14fd887c-578c-40e3-a8da-0089ceb992d2","resolution":{"observed_at":"2026-08-07T06:04:33.139683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-08-06T21:36:44.522717Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23762","last_updated":"2025-06-30T12:09:29Z","snapshot_observed_at":"2026-08-18T21:24:26.814763Z","submitted_at":"2025-06-30T12:09:29Z","title":"Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead","version":1},"reference_index":266,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:44.522717Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2506.23762"},"observation_digest":"sha256:6ff443c16fa900e40e33533b95be0d48c42f4241aebc197a5da6683d8d1df723","observation_id":"b45f2bae-cf7a-47b7-8989-3650b2a6f022","resolution":{"observed_at":"2026-08-06T21:36:44.522717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":"2402.18334","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-07-02T02:26:26.681775Z","title":"Learning to generate instruction tuning datasets for zero-shot task adaptation","venue":null,"work_id":"e951cf61-6435-4914-b555-a378aa23d18f","year":2024},"citing_paper":{"arxiv_id":"2606.03979","last_updated":"2026-07-10T17:52:03Z","snapshot_observed_at":"2026-07-15T23:18:25.559225Z","submitted_at":"2026-06-02T17:56:55Z","title":"Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:13.058872Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2606.03979"},"observation_digest":"sha256:bc60b802492a2d0cb05b8f88fd648334130a48f7a9124863e3b6d64d61348fc9","observation_id":"0ea9e1fe-a485-40f3-b93a-061f43f19554","resolution":{"observed_at":"2026-07-02T02:26:26.683363Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-07-13T07:44:25.325808Z","title":"Learning to generate instruction tuning datasets for zero-shot task adaptation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03979","last_updated":"2026-07-10T17:52:03Z","snapshot_observed_at":"2026-07-15T23:18:25.559225Z","submitted_at":"2026-06-02T17:56:55Z","title":"Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-07-13T07:44:25.325808Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2606.03979"},"observation_digest":"sha256:0da82e2b23055f4b4d208855a52bcadc0932904bd5cc3687d57f0b65b5cba3c2","observation_id":"4178797d-4c31-4678-b031-3e998fe059b9","resolution":{"observed_at":"2026-07-13T07:44:25.325808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18334","snapshot_observed_at":"2026-08-01T08:29:25.573305Z","title":"arXiv preprint arXiv:2402.18334 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27379","last_updated":"2026-07-29T18:37:14Z","snapshot_observed_at":"2026-08-13T14:19:47.083659Z","submitted_at":"2026-07-29T18:37:14Z","title":"HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T08:29:25.573305Z"},"links":{"cited_paper":"/paper/2402.18334","citing_paper":"/paper/2607.27379"},"observation_digest":"sha256:c0c317877d42d8c27f913818b7c3381f2fda682a3505cc8e83fe5a66ef2610b7","observation_id":"808cf4fb-badd-45d7-8f65-3b8326106b25","resolution":{"observed_at":"2026-08-01T08:29:25.573305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.18334/citation-record","integrity":"/paper/2402.18334/integrity","json":"/paper/2402.18334/citation-record.json","paper":"/paper/2402.18334"},"outbound":[],"paper":{"arxiv_id":"2402.18334","last_updated":"2024-09-11T16:28:29Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T14:14:32.308300Z","submitted_at":"2024-02-28T13:54:57Z","title":"Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2402.18334."}