{"as_of":"2026-08-07T06:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03834271de931361e0b67b80a2f170eb7c43dc5eee66a805320e3ed45b4994dc","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:55:24.531488Z","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":"2026-07-03T01:27:31.680002Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.13659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-07-03T01:27:31.680002Z","title":"Privacy-preserving instructions for aligning large language models","venue":null,"work_id":"d5c7667f-8528-4d9a-b2a2-ebe5035d1682","year":2024},"citing_paper":{"arxiv_id":"2506.02153","last_updated":"2025-09-15T22:15:00Z","snapshot_observed_at":"2026-08-05T18:56:11.132981Z","submitted_at":"2025-06-02T18:35:16Z","title":"Small Language Models are the Future of Agentic AI","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-16T11:55:50.897500Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2506.02153"},"observation_digest":"sha256:5e9fc5c06dbd85bdc7e0fcb592903987825a607f767317c2d1093dc30ad60404","observation_id":"439afbff-8488-40ff-bdb5-c81ddcf60480","resolution":{"observed_at":"2026-05-16T11:55:51.058609Z","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":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.13659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-07-03T01:27:31.680002Z","title":"Privacy-preserving instructions for aligning large language models","venue":null,"work_id":"d5c7667f-8528-4d9a-b2a2-ebe5035d1682","year":2024},"citing_paper":{"arxiv_id":"2507.02974","last_updated":"2026-05-05T10:38:51Z","snapshot_observed_at":"2026-08-02T17:49:51.122968Z","submitted_at":"2025-06-30T18:00:41Z","title":"InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-19T06:51:03.385016Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2507.02974"},"observation_digest":"sha256:ce4938b03a54bcb4dc7fb6b7b4251c812ef20cfc8c604b3fe98ee967e21a7980","observation_id":"b965c7f0-186e-4ccf-86f7-c291b94990a3","resolution":{"observed_at":"2026-05-19T06:52:07.906231Z","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":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-08-05T14:55:24.531488Z","title":"Method A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.20757","last_updated":"2025-09-03T07:21:02Z","snapshot_observed_at":"2026-08-06T23:24:51.954985Z","submitted_at":"2025-08-28T13:14:20Z","title":"GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:55:24.531488Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2508.20757"},"observation_digest":"sha256:1882c169b3c15b4b4cf7bb74d96e6690c770b92fa704c1cf8b24f6256ff0774a","observation_id":"ded81d64-c4b8-4b6e-acbc-93d23d15e2c1","resolution":{"observed_at":"2026-08-05T14:55:24.531488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-08-03T20:29:00.948306Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.19711","last_updated":"2026-07-23T20:56:40Z","snapshot_observed_at":"2026-08-06T13:59:53.166673Z","submitted_at":"2025-11-24T21:21:55Z","title":"CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation","version":2},"reference_index":127,"source":"pdf_text","source_observed_at":"2026-08-03T20:29:00.948306Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2511.19711"},"observation_digest":"sha256:9d46acadce10d9abdea28952857d091725e93bc7b39d2c8b7c15879c5f24607f","observation_id":"fcb3228b-c53d-492f-8595-d82a2f8d42c1","resolution":{"observed_at":"2026-08-03T20:29:00.948306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.13659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-07-03T01:27:31.680002Z","title":"Privacy-preserving instructions for aligning large language models","venue":null,"work_id":"d5c7667f-8528-4d9a-b2a2-ebe5035d1682","year":2024},"citing_paper":{"arxiv_id":"2604.12160","last_updated":"2026-04-14T00:35:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-14T00:35:39Z","title":"PubSwap: Public-Data Off-Policy Coordination for Federated RLVR","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T16:05:27.319466Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2604.12160"},"observation_digest":"sha256:95253ac3b920296f60c69164ea07a4551bf5d205415a9282eca9ec8717843c27","observation_id":"042e56c1-f948-4226-ab85-e70ced49770f","resolution":{"observed_at":"2026-05-11T09:21:00.143270Z","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":"2402.13659","last_updated":"2024-07-02T11:57:07Z","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.13659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.13659","snapshot_observed_at":"2026-07-03T01:27:31.680002Z","title":"Privacy-preserving instructions for aligning large language models","venue":null,"work_id":"d5c7667f-8528-4d9a-b2a2-ebe5035d1682","year":2024},"citing_paper":{"arxiv_id":"2606.09145","last_updated":"2026-06-08T07:42:44Z","snapshot_observed_at":"2026-07-06T23:48:30.569726Z","submitted_at":"2026-06-08T07:42:44Z","title":"PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-27T16:28:06.351376Z"},"links":{"cited_paper":"/paper/2402.13659","citing_paper":"/paper/2606.09145"},"observation_digest":"sha256:a4efff38f57390be3c8d2a7d1f6de078b5ce653f6fe6538b804c312b48d1b01e","observation_id":"14934bd4-fcc1-4866-832f-dda84e123d10","resolution":{"observed_at":"2026-07-03T01:27:31.681344Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2402.13659/citation-record","integrity":"/paper/2402.13659/integrity","json":"/paper/2402.13659/citation-record.json","paper":"/paper/2402.13659"},"outbound":[],"paper":{"arxiv_id":"2402.13659","last_updated":"2024-07-02T11:57:07Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-06T16:09:41.701226Z","submitted_at":"2024-02-21T09:45:08Z","title":"Privacy-Preserving Instructions for Aligning Large Language Models"},"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 6 inbound Pith citation observations for arXiv:2402.13659."}