{"as_of":"2026-08-07T15:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e43a1b21631b649ecd7c3070fa9663f89a247e03d97c33a4d9947e489f9718d5","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:27:49.311523Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-07T14:27:49.311523Z","title":"Direct preference knowledge distillation for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18952","last_updated":"2025-05-25T02:56:18Z","snapshot_observed_at":"2026-08-07T14:20:33.731542Z","submitted_at":"2025-05-25T02:56:18Z","title":"Online Knowledge Distillation with Reward Guidance","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:27:49.311523Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2505.18952"},"observation_digest":"sha256:0ed52010d562f483566228071b405f156360440351dd3f3cbb2a529886f8be23","observation_id":"5fbb818f-063b-466e-85ef-e368a5800469","resolution":{"observed_at":"2026-08-07T14:27:49.311523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-07T11:31:46.108909Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15717","last_updated":"2025-06-03T03:39:29Z","snapshot_observed_at":"2026-08-07T11:22:31.753795Z","submitted_at":"2025-06-03T03:39:29Z","title":"daDPO: Distribution-Aware DPO for Distilling Conversational Abilities","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T11:31:46.108909Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2506.15717"},"observation_digest":"sha256:b80833cd04e012ee0ee6fa3bd12c4c8bd03b872d50599d8428012d494725f828","observation_id":"cbf0e1d1-9422-455e-89cd-b2c3611ec0ae","resolution":{"observed_at":"2026-08-07T11:31:46.108909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-06T18:38:09.954900Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.07725","last_updated":"2025-07-10T12:58:45Z","snapshot_observed_at":"2026-08-06T18:31:41.527762Z","submitted_at":"2025-07-10T12:58:45Z","title":"Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T18:38:09.954900Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2507.07725"},"observation_digest":"sha256:5b23e7f14008990254be1415f34fda5f265f76716e4a1d68007c377f0bc5fb54","observation_id":"9f0bc812-d474-4d61-bbfa-f4c4e9543a29","resolution":{"observed_at":"2026-08-06T18:38:09.954900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-06T18:52:36.169261Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.07855","last_updated":"2026-06-22T14:01:09Z","snapshot_observed_at":"2026-08-06T18:28:35.982595Z","submitted_at":"2025-07-10T15:38:17Z","title":"DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T18:52:36.169261Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2507.07855"},"observation_digest":"sha256:3231944512192d2300cd9af55da53bfeb040decbcf49f216fd864fe03c0e3b77","observation_id":"d31023e5-7897-444b-a6ca-3cbc8878500c","resolution":{"observed_at":"2026-08-06T18:52:36.169261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-02T20:24:30.971052Z","title":"Direct preference knowledge distillation for large language models.arXiv preprint arXiv:2406.19774, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.23565","last_updated":"2026-08-05T18:39:18Z","snapshot_observed_at":"2026-08-07T15:15:23.592181Z","submitted_at":"2026-02-27T00:21:36Z","title":"Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T20:24:30.971052Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2602.23565"},"observation_digest":"sha256:0b11954f24dcf1b260e596f56ad2bd8defbba4e2337774206fd155ae93cf3fdf","observation_id":"4f22e24f-e5c6-414b-ab97-9ac9f30be085","resolution":{"observed_at":"2026-08-02T20:24:30.971052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.19774","doi":"10.48550/arxiv.2406.19774","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Direct preference knowledge distillation for large language models","venue":"arXiv (Cornell University)","work_id":"b079f921-7a94-4a1f-b51e-22acf2f1cb5a","year":2024},"citing_paper":{"arxiv_id":"2604.09741","last_updated":"2026-04-09T23:27:46Z","snapshot_observed_at":"2026-08-03T03:51:17.779334Z","submitted_at":"2026-04-09T23:27:46Z","title":"ExecTune: Effective Steering of Black-Box LLMs with Guide Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-10T16:55:34.091812Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2604.09741"},"observation_digest":"sha256:0a30ee8ecd79a65134968cb1c0b049b974dd902e688049fb1d868addc9d3e926","observation_id":"60baec07-81e0-4715-b37d-6ab6c6af7ea0","resolution":{"observed_at":"2026-05-11T07:55:58.672118Z","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":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.19774","doi":"10.48550/arxiv.2406.19774","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Direct preference knowledge distillation for large language models","venue":"arXiv (Cornell University)","work_id":"b079f921-7a94-4a1f-b51e-22acf2f1cb5a","year":2024},"citing_paper":{"arxiv_id":"2606.07604","last_updated":"2026-05-29T09:40:38Z","snapshot_observed_at":"2026-08-03T04:00:50.272221Z","submitted_at":"2026-05-29T09:40:38Z","title":"Contribution Weights: A Geometrical Analysis of Self-Attention Transformers","version":1},"reference_index":118,"source":"arxiv_source","source_observed_at":"2026-06-28T23:29:02.457697Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2606.07604"},"observation_digest":"sha256:c27d878a2b2a764dd7ebe3c06b8c10614ad920e5204b429e583afa9a0c0c3870","observation_id":"d87bd08b-3725-4d2c-a8a8-141c09aa6a51","resolution":{"observed_at":"2026-06-28T23:32:46.720349Z","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":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.19774","doi":"10.48550/arxiv.2406.19774","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Direct preference knowledge distillation for large language models","venue":"arXiv (Cornell University)","work_id":"b079f921-7a94-4a1f-b51e-22acf2f1cb5a","year":2024},"citing_paper":{"arxiv_id":"2606.22942","last_updated":"2026-06-22T07:19:43Z","snapshot_observed_at":"2026-08-04T01:05:11.779529Z","submitted_at":"2026-06-22T07:19:43Z","title":"Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-26T08:31:47.633155Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2606.22942"},"observation_digest":"sha256:59fac2290c9ff1b0e5c8d889b82c123a499df4a7bced2aaa4d1c5325f2436bd5","observation_id":"a62cec2c-5f63-478d-8ccf-34e53e1695cd","resolution":{"observed_at":"2026-06-26T08:39:15.122401Z","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":"2406.19774","last_updated":"2025-04-07T06:11:54Z","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.19774","doi":"10.48550/arxiv.2406.19774","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Direct preference knowledge distillation for large language models","venue":"arXiv (Cornell University)","work_id":"b079f921-7a94-4a1f-b51e-22acf2f1cb5a","year":2024},"citing_paper":{"arxiv_id":"2606.29869","last_updated":"2026-06-29T07:05:56Z","snapshot_observed_at":"2026-08-02T12:22:24.399410Z","submitted_at":"2026-06-29T07:05:56Z","title":"ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-06-30T05:59:13.483829Z"},"links":{"cited_paper":"/paper/2406.19774","citing_paper":"/paper/2606.29869"},"observation_digest":"sha256:849c85b99764d211da3183637e02805b534d854528b3f0072c3f62f4447b588a","observation_id":"ee6be6f7-8aeb-45d6-83dd-b39a70fba64e","resolution":{"observed_at":"2026-06-30T06:04:21.662373Z","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/2406.19774/citation-record","integrity":"/paper/2406.19774/integrity","json":"/paper/2406.19774/citation-record.json","paper":"/paper/2406.19774"},"outbound":[],"paper":{"arxiv_id":"2406.19774","last_updated":"2025-04-07T06:11:54Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T18:38:21.977142Z","submitted_at":"2024-06-28T09:23:40Z","title":"Direct Preference Knowledge Distillation for 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 9 inbound Pith citation observations for arXiv:2406.19774."}