{"as_of":"2026-08-09T23:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:698f0c7f1c2703e77f2750c049f9f2f6506798c7e705c85a765d738595a892e7","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:19:43.250197Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2506.10885/citation-record","integrity":"/paper/2506.10885/integrity","json":"/paper/2506.10885/citation-record.json","paper":"/paper/2506.10885"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2012.13255","last_updated":"2020-12-22T07:42:30Z","snapshot_observed_at":"2026-08-08T07:37:46.709970Z","submitted_at":"2020-12-22T07:42:30Z","title":"Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.13255","snapshot_observed_at":"2026-08-07T04:19:41.803051Z","title":"Aghajanyan, L","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:41.803051Z"},"links":{"cited_paper":"/paper/2012.13255","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:a9d78f765cd35bc590915f6ff69b49692988c0498184187314f45eca41165484","observation_id":"b24f5cad-fbf4-4111-89a9-6f7730b111b9","resolution":{"observed_at":"2026-08-07T04:19:41.803051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","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":"2026-08-07T04:19:41.870990Z","title":"Cobbe, V","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:41.870990Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:a9ae65a3903a062fb356ed193dec12b9ffaeb9e656671d37156fe4ac8165e903","observation_id":"8481ca7f-1c56-474e-beac-5bb4e43d8cd7","resolution":{"observed_at":"2026-08-07T04:19:41.870990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.02861","last_updated":"2022-06-20T16:05:15Z","snapshot_observed_at":"2026-07-06T11:55:04.054344Z","submitted_at":"2021-10-06T15:43:20Z","title":"8-bit Optimizers via Block-wise Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.02861","snapshot_observed_at":"2026-08-07T04:19:41.968247Z","title":"Dettmers, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:41.968247Z"},"links":{"cited_paper":"/paper/2110.02861","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:1b5e8ef087fa6d48951d6ccf66f253982822a1ba3c9bb2883ec32fe0d8e181bc","observation_id":"f082671f-8362-49d4-9d86-5122c2bd84f8","resolution":{"observed_at":"2026-08-07T04:19:41.968247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14314","last_updated":"2023-05-23T17:50:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-23T17:50:33Z","title":"QLoRA: Efficient Finetuning of Quantized LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14314","snapshot_observed_at":"2026-08-07T04:19:42.116500Z","title":"Dettmers, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.116500Z"},"links":{"cited_paper":"/paper/2305.14314","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:bee0410d1bbac42d0219487fcc00f99dcfbc30aa26eb693c97c26c9fc4280211","observation_id":"85eb3b08-cf6a-41a2-a9c1-5b23d64cadb0","resolution":{"observed_at":"2026-08-07T04:19:42.116500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09720","last_updated":"2023-02-28T00:12:34Z","snapshot_observed_at":"2026-08-09T04:11:28.457434Z","submitted_at":"2022-12-19T18:48:33Z","title":"The case for 4-bit precision: k-bit Inference Scaling Laws","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09720","snapshot_observed_at":"2026-08-07T04:19:42.212714Z","title":"Dettmers and L","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.212714Z"},"links":{"cited_paper":"/paper/2212.09720","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:3effd03134e6359da0996f33962fd7aec46f6c528938f9d05ffca6c0552f486c","observation_id":"b18180a0-ba87-4ed2-b240-0dedfd50b85f","resolution":{"observed_at":"2026-08-07T04:19:42.212714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T04:19:42.351229Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.351229Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:c9349a7e947b64d235bc8d0a564c172efff22715aa696d6e8e6474e16e0437e2","observation_id":"c3270593-c912-46f5-9d7f-db67ee0c7b6a","resolution":{"observed_at":"2026-08-07T04:19:42.351229Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:19:42.458557Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.458557Z"},"links":{"citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:caecdfe1d90024fe0e7b29e79203811bc9bf9a00d3c3f3d977c0ab34933ebc1e","observation_id":"a48c8875-4fb1-4088-ae1c-388f20cce368","resolution":{"observed_at":"2026-08-07T04:19:42.458557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04366","last_updated":"2022-02-02T16:39:23Z","snapshot_observed_at":"2026-08-09T04:45:04.547249Z","submitted_at":"2021-10-08T20:22:26Z","title":"Towards a Unified View of Parameter-Efficient Transfer Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04366","snapshot_observed_at":"2026-08-07T04:19:42.568333Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.568333Z"},"links":{"cited_paper":"/paper/2110.04366","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:91116daa52ab93fc36718d5ae0f1aa3af84fda379de3fe77c1912ae0c291e9b0","observation_id":"198e7b69-e019-4e8f-8490-854de160bbb5","resolution":{"observed_at":"2026-08-07T04:19:42.568333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-08-09T10:28:06.906299Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-07T04:19:42.654574Z","title":"Hendrycks, C","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.654574Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:725eb9cafa0eb14b7e368d6bd12c953680cf344d115b4692fc7caf3a2ec2a5e2","observation_id":"50dda0ba-9406-4a17-be83-6b31ca54b75c","resolution":{"observed_at":"2026-08-07T04:19:42.654574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.00751","last_updated":"2019-06-13T17:48:30Z","snapshot_observed_at":"2026-07-06T07:30:44.870185Z","submitted_at":"2019-02-02T16:29:47Z","title":"Parameter-Efficient Transfer Learning for NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.00751","snapshot_observed_at":"2026-08-07T04:19:42.738755Z","title":"Houlsby, A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.738755Z"},"links":{"cited_paper":"/paper/1902.00751","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:9bf210de6180966f7117550e6780e230a3cfdeb86b4cfb72cd553c50c211f445","observation_id":"f7f04f52-7546-4466-b74f-60d78df90262","resolution":{"observed_at":"2026-08-07T04:19:42.738755Z","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":"2026-08-07T07:43:16.294957Z","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":"2026-08-07T04:19:42.867009Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.867009Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:a9544957df6c44c1543f0bffc786aff307e7dd682d58d266b65d7805fd54bdb5","observation_id":"cacfa10c-456c-49f0-b649-efe5b3ba6a41","resolution":{"observed_at":"2026-08-07T04:19:42.867009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00190","last_updated":"2021-01-01T08:00:36Z","snapshot_observed_at":"2026-07-06T10:29:18.734092Z","submitted_at":"2021-01-01T08:00:36Z","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00190","snapshot_observed_at":"2026-08-07T04:19:42.950788Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:42.950788Z"},"links":{"cited_paper":"/paper/2101.00190","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:5a85e3d6481adcfa483e32848796c5012b1a8be311b401080d644e6d4c815104","observation_id":"db95a001-f2b9-4774-89a0-0376105d9b07","resolution":{"observed_at":"2026-08-07T04:19:42.950788Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:19:43.034025Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:43.034025Z"},"links":{"citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:fbaf111a9f89ecab76e0be3082a5136a3efccaee730ff9cf5b692b4ce1df2e13","observation_id":"9023e8d4-3539-40df-9ad1-7e9a9f5087c2","resolution":{"observed_at":"2026-08-07T04:19:43.034025Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:19:43.081727Z","title":"Subakan, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:43.081727Z"},"links":{"citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:b3886f80b2ccc5bec5fd57953699c7fb8e3dd16c4296d46036a501a148749096","observation_id":"c54b3850-06c9-41b3-afbb-88dad1d87e6a","resolution":{"observed_at":"2026-08-07T04:19:43.081727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08011","last_updated":"2018-12-19T15:15:55Z","snapshot_observed_at":"2026-07-06T07:22:15.380277Z","submitted_at":"2018-12-19T15:15:55Z","title":"Training Deep Neural Networks with 8-bit Floating Point Numbers","version":1},"cited_work":{"arxiv_id":"1812.08011","doi":"10.48550/arxiv.1812.08011","metadata_source":"pith","pith_arxiv_id":"1812.08011","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Training Deep Neural Networks with 8-bit Floating Point Numbers","venue":"cs.LG","work_id":"2ea09575-7878-417c-8476-fb73b2ec1e1d","year":2018},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:43.192454Z"},"links":{"cited_paper":"/paper/1812.08011","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:7b455ae54c3553256af5c9e40e9e270dcb710e8546caf0800c47770f145f254d","observation_id":"4a1246e9-eba9-4b8f-8bf7-4454324a8f5b","resolution":{"observed_at":"2026-08-07T04:19:43.478897Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-07-31T00:09:56.948833Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-07T04:19:43.250197Z","title":"Zellers, A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T04:19:43.250197Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2506.10885"},"observation_digest":"sha256:57d393dba98ee8842dd4c6ffe1238c675b86d3ee6dd4a0a40d46b9ac5554590f","observation_id":"120ade8d-53f7-48bf-a21f-96b4fb6d3375","resolution":{"observed_at":"2026-08-07T04:19:43.250197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.10885","last_updated":"2025-06-12T16:49:40Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T05:55:07.421658Z","submitted_at":"2025-06-12T16:49:40Z","title":"Slimming Down LLMs Without Losing Their Minds"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.10885."}