{"as_of":"2026-08-10T11:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19afc06e8544bf12cd2a2a469695b45af6c0c68c5e81c62d97b1e7f07897d134","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":"2026-08-10T06:31:04.303077+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":"2026-08-09T00:50:00.321494Z","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":"2026-06-30T16:04:52.605296Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10630","snapshot_observed_at":"2026-08-09T00:50:00.321494Z","title":"Emerging safety attack and defense in federated instruction tuning of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05224","last_updated":"2025-02-06T04:43:05Z","snapshot_observed_at":"2026-08-09T05:58:57.244749Z","submitted_at":"2025-02-06T04:43:05Z","title":"A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-09T00:50:00.321494Z"},"links":{"cited_paper":"/paper/2406.10630","citing_paper":"/paper/2502.05224"},"observation_digest":"sha256:78587ab6062252b03e2ec7fe0a96f8c1b477e25803b458ab11834154d07f7978","observation_id":"433e8a0a-e080-43f3-a453-1f1df46206b3","resolution":{"observed_at":"2026-08-09T00:50:00.321494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.10630","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10630","snapshot_observed_at":"2026-06-30T16:04:52.605296Z","title":"Emerging safety attack and defense in federated instruction tuning of large language models","venue":null,"work_id":"bdc30d83-c241-4829-865e-fc9f5471d2ab","year":2024},"citing_paper":{"arxiv_id":"2604.06833","last_updated":"2026-04-08T08:51:46Z","snapshot_observed_at":"2026-07-06T22:55:15.791334Z","submitted_at":"2026-04-08T08:51:46Z","title":"FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T17:22:40.613937Z"},"links":{"cited_paper":"/paper/2406.10630","citing_paper":"/paper/2604.06833"},"observation_digest":"sha256:1261070f1cb908860bf00a9dd09634127de9b0b5b21176a9a6718d00cbf89f05","observation_id":"2705b889-85ec-4a10-b550-af35e5a2100a","resolution":{"observed_at":"2026-05-11T06:56:01.919305Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.10630","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10630","snapshot_observed_at":"2026-06-30T16:04:52.605296Z","title":"Emerging safety attack and defense in federated instruction tuning of large language models","venue":null,"work_id":"bdc30d83-c241-4829-865e-fc9f5471d2ab","year":2024},"citing_paper":{"arxiv_id":"2605.07961","last_updated":"2026-07-05T18:31:05Z","snapshot_observed_at":"2026-08-03T10:02:43.771073Z","submitted_at":"2026-05-08T16:24:54Z","title":"Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-11T02:42:04.741382Z"},"links":{"cited_paper":"/paper/2406.10630","citing_paper":"/paper/2605.07961"},"observation_digest":"sha256:0deafa9b3bc5e4de421b1c9148d6839effc793e2a1dc9cc2438e197e917c67c6","observation_id":"70421370-835f-4fb0-9d50-877954eb5082","resolution":{"observed_at":"2026-05-11T02:45:58.691689Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10630","snapshot_observed_at":"2026-07-12T17:18:42.590627Z","title":"Emerging safety attack and defense in federated instruction tuning of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.07961","last_updated":"2026-07-05T18:31:05Z","snapshot_observed_at":"2026-08-03T10:02:43.771073Z","submitted_at":"2026-05-08T16:24:54Z","title":"Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-12T17:18:42.590627Z"},"links":{"cited_paper":"/paper/2406.10630","citing_paper":"/paper/2605.07961"},"observation_digest":"sha256:33f1e441741829347cb7579557a5a698a4f3ec001c2aad4deb61a4be67dbe32e","observation_id":"f92bb088-bae5-4314-8b0f-0eb913073a3d","resolution":{"observed_at":"2026-07-12T17:18:42.590627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.10630","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10630","snapshot_observed_at":"2026-06-30T16:04:52.605296Z","title":"Emerging safety attack and defense in federated instruction tuning of large language models","venue":null,"work_id":"bdc30d83-c241-4829-865e-fc9f5471d2ab","year":2024},"citing_paper":{"arxiv_id":"2605.24154","last_updated":"2026-05-22T19:22:17Z","snapshot_observed_at":"2026-07-06T23:34:13.859813Z","submitted_at":"2026-05-22T19:22:17Z","title":"Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T16:03:12.728352Z"},"links":{"cited_paper":"/paper/2406.10630","citing_paper":"/paper/2605.24154"},"observation_digest":"sha256:c4bd517c599fd98084d7893b10924c400898d92375ce7080ecfb3730f04e3084","observation_id":"361aa8e8-f599-4038-8c5e-18e1d689ced5","resolution":{"observed_at":"2026-06-30T16:04:52.606815Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.10630/citation-record","integrity":"/paper/2406.10630/integrity","json":"/paper/2406.10630/citation-record.json","paper":"/paper/2406.10630"},"outbound":[],"paper":{"arxiv_id":"2406.10630","last_updated":"2024-06-15T13:24:22Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T15:55:04.436415Z","submitted_at":"2024-06-15T13:24:22Z","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of 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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.10630."}