{"as_of":"2026-08-20T02:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d09ddd595828d5db56cd74a1e421155d08b46183a213c39546c625a6a0fbe58","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:23:49.608848Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.17846/citation-record","integrity":"/paper/2412.17846/integrity","json":"/paper/2412.17846/citation-record.json","paper":"/paper/2412.17846"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.11495","last_updated":"2023-09-25T15:25:49Z","snapshot_observed_at":"2026-08-12T01:35:36.884518Z","submitted_at":"2023-09-20T17:50:55Z","title":"Chain-of-Verification Reduces Hallucination in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.11495","snapshot_observed_at":"2026-08-11T12:23:49.550149Z","title":"Preprint, arXiv:2309.11495","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.550149Z"},"links":{"cited_paper":"/paper/2309.11495","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:f5667849358d23ee195ab1dd126dd1a69ad15a0685f415064e4446c9e5ffe140","observation_id":"da89694f-64b4-44e0-9baf-f12810825526","resolution":{"observed_at":"2026-08-11T12:23:49.550149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T12:23:49.554525Z","title":"Preprint, arXiv:2407.21783","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.554525Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:5f1ca41d674f411df9b2310599cea69ac13fd462d240254dea11469f4c373ac6","observation_id":"dc192043-941d-4534-b78c-4c316f36f370","resolution":{"observed_at":"2026-08-11T12:23:49.554525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08543","last_updated":"2026-01-31T09:16:35Z","snapshot_observed_at":"2026-08-16T00:37:04.298248Z","submitted_at":"2023-06-14T14:44:03Z","title":"MiniLLM: On-Policy Distillation of Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.08543","snapshot_observed_at":"2026-08-11T12:23:49.558604Z","title":"Preprint, arXiv:2306.08543","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.558604Z"},"links":{"cited_paper":"/paper/2306.08543","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:e24f15e42ba125f2198a82304f9e4918cfc5a6f0d8894cee4b68034bcb1d9ed4","observation_id":"bba77a41-6010-43a9-b355-8954694e8959","resolution":{"observed_at":"2026-08-11T12:23:49.558604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13442","last_updated":"2025-08-31T18:53:24Z","snapshot_observed_at":"2026-08-19T12:31:09.401286Z","submitted_at":"2024-08-24T02:48:40Z","title":"A Law of Next-Token Prediction in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13442","snapshot_observed_at":"2026-08-11T12:23:49.562582Z","title":"Preprint, arXiv:2408.13442","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.562582Z"},"links":{"cited_paper":"/paper/2408.13442","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:6222e7d859650bcaa0945105925db2d8a1feb48b34790b63697bc7062f72f7f6","observation_id":"b07aec9a-cc91-43a7-b8ad-a55cd0f3070e","resolution":{"observed_at":"2026-08-11T12:23:49.562582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-17T18:04:53.578114Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T12:23:49.566412Z","title":"Preprint, arXiv:2106.09685","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.566412Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:f1c39c3765a09d37f8d419830d3132d56935d8a8eaea0d810afa85dcb0debacf","observation_id":"debbd286-4513-43a1-b1a1-f29d18efa5bd","resolution":{"observed_at":"2026-08-11T12:23:49.566412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:23:49.831722Z","title":"https://www.reddit.com/r/LocalLLaMA/ 9 comments/1efg2wv/llama_31_405b_exl2_quant_ results/","venue":null,"work_id":"decb4f6f-f476-4a97-a5b7-b4e0ec3c3952","year":2024},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.571087Z"},"links":{"citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:b6949767dd5ba67b39d34fcf23b1eec0a1cae0d7b87881862b17ced34fc1041d","observation_id":"8ea102be-be1d-4e00-9638-4e01f5491abd","resolution":{"observed_at":"2026-08-11T12:23:49.835687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11916","last_updated":"2023-01-29T05:14:17Z","snapshot_observed_at":"2026-08-13T05:54:24.242465Z","submitted_at":"2022-05-24T09:22:26Z","title":"Large Language Models are Zero-Shot Reasoners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11916","snapshot_observed_at":"2026-08-11T12:23:49.575057Z","title":"Preprint, arXiv:2205.11916","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.575057Z"},"links":{"cited_paper":"/paper/2205.11916","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:adb5febc1822af0ba46cc8e4b2419ce890a9268e16ecfd0614f454079af97e42","observation_id":"1d97299f-0cf7-4975-b6f9-82caac028818","resolution":{"observed_at":"2026-08-11T12:23:49.575057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13269","last_updated":"2024-02-21T07:44:48Z","snapshot_observed_at":"2026-08-16T15:30:59.018192Z","submitted_at":"2023-05-22T17:34:23Z","title":"Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13269","snapshot_observed_at":"2026-08-11T12:23:49.582812Z","title":"Preprint, arXiv:2305.13269","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.582812Z"},"links":{"cited_paper":"/paper/2305.13269","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:94e26879d2a06139fd166d6b9825bd05951bfc865bc98ed5e67a3b4c03cd13d4","observation_id":"e894e154-00ef-49ed-bbc1-c8ba0c646212","resolution":{"observed_at":"2026-08-11T12:23:49.582812Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08410","last_updated":"2023-06-01T12:17:01Z","snapshot_observed_at":"2026-08-18T17:26:54.648877Z","submitted_at":"2022-12-16T11:24:42Z","title":"Teaching Small Language Models to Reason","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08410","snapshot_observed_at":"2026-08-11T12:23:49.586847Z","title":"Preprint, arXiv:2212.08410","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.586847Z"},"links":{"cited_paper":"/paper/2212.08410","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:84d818e66252d6d87ce5f3759cfe5bf136876e2d6b848953a60cda8007fb18f3","observation_id":"f955d8be-c0bc-4e5e-816a-7c815a19c2f3","resolution":{"observed_at":"2026-08-11T12:23:49.586847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06196","last_updated":"2025-03-23T14:51:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-09T05:37:09Z","title":"Large Language Models: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06196","snapshot_observed_at":"2026-08-11T12:23:49.590700Z","title":"Preprint, arXiv:2402.06196","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.590700Z"},"links":{"cited_paper":"/paper/2402.06196","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:817d2ca1e3055c2d44d1d842a31b5d0951b003572785a4be9ea5ccf24a3d6d3c","observation_id":"91132530-20a7-4a01-866f-d4880cc54a33","resolution":{"observed_at":"2026-08-11T12:23:49.590700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.06435","last_updated":"2024-10-17T01:10:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-12T20:01:52Z","title":"A Comprehensive Overview of Large Language Models","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.06435","snapshot_observed_at":"2026-08-11T12:23:49.595602Z","title":"Preprint, arXiv:2307.06435","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.595602Z"},"links":{"cited_paper":"/paper/2307.06435","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:f887bbc1af72c98363ec464e2d4074e80487f78f6c93521c0e2411f6c0fbc40c","observation_id":"27348bc0-d3b8-42e9-bff3-d9e90f18bf87","resolution":{"observed_at":"2026-08-11T12:23:49.595602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-08-13T07:04:41.220509Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-11T12:23:49.600536Z","title":"Preprint, arXiv:2201.11903","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.600536Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:17c40c1bab4aae109e0082ccf567b1e4ddaef73c9fdf338f3963113dba455aa7","observation_id":"8a01e56a-eb50-4a05-857b-2bb421e25bfd","resolution":{"observed_at":"2026-08-11T12:23:49.600536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14497","last_updated":"2024-04-18T07:27:00Z","snapshot_observed_at":"2026-08-19T10:58:03.599584Z","submitted_at":"2023-05-23T19:58:30Z","title":"Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14497","snapshot_observed_at":"2026-08-11T12:23:49.604801Z","title":"As a teacher, guide your student through solving the question below. Pro- vide a clear, simple explanation for someone unfa- miliar with the problem","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.604801Z"},"links":{"cited_paper":"/paper/2305.14497","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:a3e86e39887f3bc081d1b27d6e1974d09e97d2ab7a6272a7ee37a3df0820a709","observation_id":"4b2e88d9-dc96-43c8-be4a-025c5597d74f","resolution":{"observed_at":"2026-08-11T12:23:49.604801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:23:49.804335Z","title":"Llama 3.1 is available under the Llama 3.1 Community Licensing Agree- ment (Dubey et al., 2024)","venue":null,"work_id":"8d3cd8c2-74f5-4b0e-a4fe-996a88f49f26","year":2024},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.608848Z"},"links":{"citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:1d883e1f9a1381fe6b9f1504cb27a62e018a1eea88a96b686096a5d35f2b857d","observation_id":"402e9f50-a497-4766-a9aa-1cb70336a029","resolution":{"observed_at":"2026-08-11T12:23:49.810537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:23:49.820120Z","title":"https://www.tuananhle.co.uk/ notes/reverse-forward-kl.html","venue":null,"work_id":"d347a053-1c9c-475f-ae66-6cece8beacab","year":2024},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.578903Z"},"links":{"citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:c9f2c02027c36f536dea7835216cbf784259d39901fd606e8e19edc7f8df05e9","observation_id":"e901acd5-414e-4f01-a910-80a18c3fb550","resolution":{"observed_at":"2026-08-11T12:23:49.824283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-11T12:23:49.544741Z","title":"Preprint, arXiv:2110.14168","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.544741Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:59820b6207eff60142a82e23fbb9dea4206636b8512d6c7cb2c6f80467b2494c","observation_id":"f9086ab5-f68d-440b-9362-42ea8c6469e5","resolution":{"observed_at":"2026-08-11T12:23:49.544741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17311","last_updated":"2023-11-29T02:07:09Z","snapshot_observed_at":"2026-08-16T16:45:56.159370Z","submitted_at":"2023-11-29T02:07:09Z","title":"Universal Self-Consistency for Large Language Model Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17311","snapshot_observed_at":"2026-08-11T12:23:49.539881Z","title":"Preprint, arXiv:2311.17311","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.539881Z"},"links":{"cited_paper":"/paper/2311.17311","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:ad37ba438e8a84b5f9bad8e023780e87cd56a20a84908d98fbc4638eeb9de01f","observation_id":"321f8bdc-fcf5-4d53-b2d9-135287feebc3","resolution":{"observed_at":"2026-08-11T12:23:49.539881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01799","last_updated":"2024-04-24T04:58:46Z","snapshot_observed_at":"2026-08-16T14:22:15.817388Z","submitted_at":"2024-02-02T06:29:34Z","title":"Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01799","snapshot_observed_at":"2026-08-11T12:23:49.534996Z","title":"Preprint, arXiv:2402.01799","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T12:23:49.534996Z"},"links":{"cited_paper":"/paper/2402.01799","citing_paper":"/paper/2412.17846"},"observation_digest":"sha256:f9a6363072fff90adf86d4257cb2f944af1b6972532a5429b094bbfca88e0b73","observation_id":"bc89b021-57b0-494c-8338-3169b300951a","resolution":{"observed_at":"2026-08-11T12:23:49.534996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.17846","last_updated":"2024-12-18T20:41:44Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-19T19:54:51.719340Z","submitted_at":"2024-12-18T20:41:44Z","title":"Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":3},"total_outbound_references":18},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2412.17846."}