{"as_of":"2026-08-19T13:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c59d5ffe045cd0ea98accc19fbfa9f28fdfb01f0e992179afe56e3b561fdcac8","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:17:37.922358Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"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/2505.01449/citation-record","integrity":"/paper/2505.01449/integrity","json":"/paper/2505.01449/citation-record.json","paper":"/paper/2505.01449"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.05961","last_updated":"2024-08-21T22:46:05Z","snapshot_observed_at":"2026-08-16T14:02:33.067745Z","submitted_at":"2024-04-09T02:51:05Z","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05961","snapshot_observed_at":"2026-08-16T05:17:37.757884Z","title":"Llm2vec: Large language models are secretly powerful text encoders","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.757884Z"},"links":{"cited_paper":"/paper/2404.05961","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:af0adfdb90f562c7250466ca73df5f27a75475b0f064dc03e1ff2fe61b7d255a","observation_id":"a385e7cb-d4b9-4510-8355-8dc0aa96e912","resolution":{"observed_at":"2026-08-16T05:17:37.757884Z","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":"2026-08-16T05:17:37.763497Z","title":"D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.763497Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:4ea27ec67a4c821f2f1a4528535e2d9f4e51d0fef1f28270616d356004a3eae5","observation_id":"e88622bb-574f-4644-be17-1726236e2ebb","resolution":{"observed_at":"2026-08-16T05:17:37.763497Z","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":"2026-08-16T05:17:38.513037Z","title":"Data shunt: Collaboration of small and large models for lower costs and better performance","venue":null,"work_id":"440eaed0-38a6-4903-b6d8-6d69a7d3e0a4","year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.767993Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:fe3b6aae945739c9d187bc3bf60e418bf36aeee51d4107fb9bb7accb265db350","observation_id":"f2c84246-4652-4eec-9841-9245ed1cb267","resolution":{"observed_at":"2026-08-16T05:17:38.517451Z","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":"2305.05176","last_updated":"2023-05-09T05:11:02Z","snapshot_observed_at":"2026-08-10T11:59:11.481480Z","submitted_at":"2023-05-09T05:11:02Z","title":"FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05176","snapshot_observed_at":"2026-08-16T05:17:37.772377Z","title":"Frugalgpt: How to use large language models while reducing cost and improving performance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.772377Z"},"links":{"cited_paper":"/paper/2305.05176","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:d5dad9f30f50a7dbc687507799f3d95b2c8d16f28e80ee1040c0e9b5bc3ca90b","observation_id":"c0cb4af4-4dd5-4315-9032-1698df4ffbf0","resolution":{"observed_at":"2026-08-16T05:17:37.772377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-08-14T19:36:07.505691Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-16T05:17:37.777071Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.777071Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:dd547bb29426ed598740bb62e54c23bbbe49efdf093f553aaaef7e298b11d54c","observation_id":"40f5eb59-1ff3-48f4-8657-8751caa0125c","resolution":{"observed_at":"2026-08-16T05:17:37.777071Z","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":"2026-08-14T02:43:01.480086Z","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":"2026-08-16T05:17:37.781727Z","title":"Training verifiers to solve math word problems, 2021.URL https://arxiv","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.781727Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:3ed969de46e7e827944a265eacd6cb26bcac02b0125e234482d818fc50c76519","observation_id":"af3f61b0-6d9c-4196-a97f-c0aea987ef57","resolution":{"observed_at":"2026-08-16T05:17:37.781727Z","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":"2026-08-16T05:17:37.786544Z","title":"Qlora: Efficient finetuning of quantized llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.786544Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:163058c937e5c8f511e7fd249682dab12ea3e54105d8d8c9f93ec801ca78e3b6","observation_id":"50e178ac-8538-43da-bfa0-e06b413eae77","resolution":{"observed_at":"2026-08-16T05:17:37.786544Z","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":"2026-08-16T05:17:37.790502Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.790502Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:837cf80bc0d63054e828d7e932fd61f3db8e83090f2788299ea9f5c660185ee0","observation_id":"b48b4569-b540-4870-98d4-5db4bb55d2fd","resolution":{"observed_at":"2026-08-16T05:17:37.790502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17179","last_updated":"2024-02-09T00:13:46Z","snapshot_observed_at":"2026-08-18T16:01:12.797308Z","submitted_at":"2023-09-29T12:20:19Z","title":"Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17179","snapshot_observed_at":"2026-08-16T05:17:37.794174Z","title":"M., Wen, Y ., Zhang, W., and Wang, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.794174Z"},"links":{"cited_paper":"/paper/2309.17179","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:a0de9a496b43ff4e30f6207274accee509dbc7adf68396ddef9bfebcba72ef5c","observation_id":"5b3048f0-41ac-45e5-baa7-7ad496a900d7","resolution":{"observed_at":"2026-08-16T05:17:37.794174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08295","last_updated":"2024-04-16T12:52:47Z","snapshot_observed_at":"2026-08-03T03:29:01.959523Z","submitted_at":"2024-03-13T06:59:16Z","title":"Gemma: Open Models Based on Gemini Research and Technology","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08295","snapshot_observed_at":"2026-08-16T05:17:37.798240Z","title":"S., Love, J., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.798240Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:0a4aa6751be1ba9ddc5134d84c5a5dddee987b40696ef59af1eb3243ce8e6991","observation_id":"559253ee-d7fd-46e6-933d-81ac07baae7a","resolution":{"observed_at":"2026-08-16T05:17:37.798240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-02T16:20:09.773989Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-16T05:17:37.802245Z","title":"G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.802245Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:edd00db0bd769e42a44549f37f9691d2bc1e390cf1545a5db12399d4cbec66a8","observation_id":"1978347e-9f75-4832-8dd5-e4aedf68fe43","resolution":{"observed_at":"2026-08-16T05:17:37.802245Z","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":"2026-08-13T20:44:28.824685Z","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":"2026-08-16T05:17:37.805904Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.805904Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:2041deeca86d3c3e39cf2e6f93ff0e6e2523c35d421c020cc7714f5eec338821","observation_id":"5600ccf9-079b-4800-9695-e1ef08accd50","resolution":{"observed_at":"2026-08-16T05:17:37.805904Z","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":"2026-08-16T05:17:38.491920Z","title":"A., Welbl, J., Clark, A., et al","venue":null,"work_id":"54278baf-883f-4dcf-bfdf-358624145391","year":2022},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.809409Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:5726d2392e7434b233cd805aae45da13448b0d20c4d5e240d2e758d422b1a7bb","observation_id":"64c55efa-9b7c-41f0-b34a-6f83ad2dfe98","resolution":{"observed_at":"2026-08-16T05:17:38.496130Z","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":"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":"2026-08-16T05:17:37.812632Z","title":"J., Shen, Y ., Wallis, P., Allen-Zhu, Z., Li, Y ., Wang, S., Wang, L., and Chen, W","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.812632Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:b21197ba3c48c3b36ba255bc554d25c65b4539f2016e8ed0a135adcc13c0afa0","observation_id":"5de80a18-fc33-477e-9c96-a4dd8725b6d5","resolution":{"observed_at":"2026-08-16T05:17:37.812632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12031","last_updated":"2024-03-28T17:56:28Z","snapshot_observed_at":"2026-08-16T16:38:21.205977Z","submitted_at":"2024-03-18T17:59:04Z","title":"RouterBench: A Benchmark for Multi-LLM Routing System","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12031","snapshot_observed_at":"2026-08-16T05:17:37.815957Z","title":"J., Bieker, J., Li, X., Jiang, N., Keigwin, B., Ranganath, G., Keutzer, K., and Upadhyay, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.815957Z"},"links":{"cited_paper":"/paper/2403.12031","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:f7cd1a047ce635b2d51771958b871d620b3804892788ee53500e7fb83f7700ac","observation_id":"86a46550-d776-4f6c-a424-04b5ca98f1c8","resolution":{"observed_at":"2026-08-16T05:17:37.815957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-08-17T20:30:34.016254Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-16T05:17:37.819798Z","title":"Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.819798Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:5eeb9f2d81e358064b9fff0c82bc813b9fac89b7cb7edd0c66cf16978a246380","observation_id":"36d51cec-536e-4d00-aa7f-78dd62081756","resolution":{"observed_at":"2026-08-16T05:17:37.819798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-16T05:17:37.823113Z","title":"B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.823113Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:df43988b7767f30828316109ae62561d3ac3a58fd0ad3f63567f51d28353f945","observation_id":"4be3fc77-cf12-42ea-b099-94dbbf050d25","resolution":{"observed_at":"2026-08-16T05:17:37.823113Z","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":"2026-08-16T05:17:37.826917Z","title":"The power of scale for parameter-efficient prompt tuning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.826917Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:2bfff6e0125cdf703c3a5dd667efea27a2de79a6c62214a07f6e0cbadb1e5ba1","observation_id":"7d8e7153-be8b-4fd0-a777-74b0ce6c0253","resolution":{"observed_at":"2026-08-16T05:17:37.826917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-17T09:42:34.746112Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-16T05:17:37.830382Z","title":"Let’s verify step by step","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.830382Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:1495230693dee16676134ef576f88801a0dbe707ca6abc34daca340bb9f34ff5","observation_id":"172f1c0d-1f03-49b3-92c3-6b44fb397a58","resolution":{"observed_at":"2026-08-16T05:17:37.830382Z","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":"2026-08-16T05:17:37.833786Z","title":"Grammar-aligned decoding, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.833786Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:3673c766d303e1952bb48256a52d35c7f9f7709f3f453c1b1551434c6e1993a3","observation_id":"b48b2aec-c63b-4f1a-9bc0-cd2853c4d175","resolution":{"observed_at":"2026-08-16T05:17:37.833786Z","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":"2026-08-16T05:17:38.479425Z","title":"Qwen2 technical report","venue":null,"work_id":"cc568321-b812-4dda-a17b-71f7a753f0ac","year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.837049Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:dcd5c59a3dc7c9d3433cea92e52d2523941285ad6892dc8ca28f889028d8f168","observation_id":"612d5170-2809-4e1d-b958-5faa28ce1d64","resolution":{"observed_at":"2026-08-16T05:17:38.483289Z","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":"2026-08-16T05:17:38.467478Z","title":null,"venue":null,"work_id":"10b30698-e486-4df4-9feb-d525cf4492af","year":2020},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.841142Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:486bbc8f5681bfee00fd7de40352b80f7d3b40e7346b4429de4ac9fb96df3ef3","observation_id":"4e38f7b1-7f04-43da-abae-f4e2350c4d9c","resolution":{"observed_at":"2026-08-16T05:17:38.471118Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-16T05:17:38.455447Z","title":"Rethinking neural operations for diverse tasks","venue":null,"work_id":"7a58f98e-8871-4dad-82fd-200d95c35553","year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.844989Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:c08ef79989d5e9a93a492e30fb614b068d73ccd7d47b60df7aeedb4de8caed21","observation_id":"11932654-76ed-4a38-9ef7-889c64d850eb","resolution":{"observed_at":"2026-08-16T05:17:38.459216Z","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":"2026-08-16T05:17:38.442299Z","title":null,"venue":null,"work_id":"4d4ee80e-38c6-415f-a7d9-c86fdbaf2d41","year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.849223Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:c434b6b46eeeedf7cb466c25a4cce6aa372bb8ba9c4d42f581a2b81fbd5ffb26","observation_id":"9e17a3da-30cc-491c-b621-b6aa385e7489","resolution":{"observed_at":"2026-08-16T05:17:38.446281Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-16T05:17:38.429656Z","title":"The probabilistic relevance framework: Bm25 and beyond","venue":null,"work_id":"eb6bcf55-f2df-4b2b-bfa6-39ce601a7f94","year":2009},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.853999Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:f72c1c48ab46072a4f0116defc17ca4e367708074f9c0946d29ae731d73cfce5","observation_id":"197fc689-0a2c-4572-bea1-598284974168","resolution":{"observed_at":"2026-08-16T05:17:38.433424Z","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":"2405.10938","last_updated":"2024-10-01T23:38:10Z","snapshot_observed_at":"2026-08-16T13:51:47.687448Z","submitted_at":"2024-05-17T17:49:44Z","title":"Observational Scaling Laws and the Predictability of Language Model Performance","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10938","snapshot_observed_at":"2026-08-16T05:17:37.858238Z","title":"J., and Hashimoto, T","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.858238Z"},"links":{"cited_paper":"/paper/2405.10938","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:6ccca19291039854f4598b6a8c23b7a784c0b37ca620a56d14d9810e4eb55af1","observation_id":"f1019de0-c494-4423-a481-935e171695da","resolution":{"observed_at":"2026-08-16T05:17:37.858238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15254","last_updated":"2025-06-10T21:52:15Z","snapshot_observed_at":"2026-08-18T09:10:51.874242Z","submitted_at":"2024-09-23T17:53:42Z","title":"Archon: An Architecture Search Framework for Inference-Time Techniques","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15254","snapshot_observed_at":"2026-08-16T05:17:37.862428Z","title":"G., Natarajan, S., Maru, N., Todorov, H., Guha, E., Buchanan, E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.862428Z"},"links":{"cited_paper":"/paper/2409.15254","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:937eaae44ca39ecca21a16b03ae62377491b4298b23f0f57c6f0f7bbc4237c0a","observation_id":"0d3de6b5-2bcf-4169-a928-c7c2aafe95b1","resolution":{"observed_at":"2026-08-16T05:17:37.862428Z","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":"2026-08-16T05:17:38.418284Z","title":"L., Bhagavatula, C., and Choi, Y","venue":null,"work_id":"3ab0cca9-5054-4566-a8cb-a036be9bc24b","year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.866541Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:2a9c2f667617b7f67b55503f5ce835bd99e940e432e17d96413907a94a8dc26a","observation_id":"bffae0c5-125f-4957-b64b-0b89d7876a43","resolution":{"observed_at":"2026-08-16T05:17:38.421652Z","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":"2026-08-16T05:17:38.405404Z","title":"Fly-swat or cannon? cost-effective language model choice via meta- modeling","venue":null,"work_id":"88f0f4b6-fb5a-4980-b8cc-9067a0ec44ac","year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.870423Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:8856561b5a3ced8935cc9e9cd37154de044aeae11c8ee5010adf820b2d681192","observation_id":"21f2a752-e6aa-44f0-8390-f0bbc851c28f","resolution":{"observed_at":"2026-08-16T05:17:38.409892Z","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":"2026-08-16T05:17:38.391681Z","title":"M., Staten, C., Khodak, M., Neubig, G., and Talwalkar, A","venue":null,"work_id":"f30004e8-e6f3-4c12-b214-f4dd81cf5cd0","year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.874205Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:f7fa283a9db1c2506173b7bd4b9d94a2dffff09c54cdf33529d063e13d0768e7","observation_id":"baef6bec-0199-4e96-8e23-a3a8148a085d","resolution":{"observed_at":"2026-08-16T05:17:38.396486Z","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":"2309.15789","last_updated":"2023-09-27T17:08:40Z","snapshot_observed_at":"2026-08-16T14:57:08.067521Z","submitted_at":"2023-09-27T17:08:40Z","title":"Large Language Model Routing with Benchmark Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15789","snapshot_observed_at":"2026-08-16T05:17:37.877990Z","title":"Large language model routing with benchmark datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.877990Z"},"links":{"cited_paper":"/paper/2309.15789","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:a9bd6230878549f0a912f1a29367550cedf85f234924e48701013e2718017787","observation_id":"1c8cc3be-1655-41f4-b1a2-7e0348838187","resolution":{"observed_at":"2026-08-16T05:17:37.877990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-16T05:17:37.882058Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.882058Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:760e69370928b8caf44f8282292050e0f4d92f716d3fc7365d89834b3dbe7f67","observation_id":"de813f25-8b4f-48be-909c-aaee25482f34","resolution":{"observed_at":"2026-08-16T05:17:37.882058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10930","last_updated":"2024-10-07T15:52:48Z","snapshot_observed_at":"2026-08-16T13:34:03.644189Z","submitted_at":"2024-07-15T17:30:31Z","title":"Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10930","snapshot_observed_at":"2026-08-16T05:17:37.885957Z","title":"Fine-tuning and prompt optimization: Two great steps that work better together","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.885957Z"},"links":{"cited_paper":"/paper/2407.10930","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:e70d7622c41621ddb69fb6331a0b7da69697b766a76fc6e31d7cc5e36c134218","observation_id":"69d8d470-5558-44dd-8cab-4694183abc19","resolution":{"observed_at":"2026-08-16T05:17:37.885957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10200","last_updated":"2024-05-23T20:53:59Z","snapshot_observed_at":"2026-08-18T18:57:10.378740Z","submitted_at":"2024-02-15T18:55:41Z","title":"Chain-of-Thought Reasoning Without Prompting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10200","snapshot_observed_at":"2026-08-16T05:17:37.890249Z","title":"and Zhou, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.890249Z"},"links":{"cited_paper":"/paper/2402.10200","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:07177b405dfd2cc2ee88116051fa0b3a48d44a2f740fbacfe645c703536d906c","observation_id":"cafeccec-5e79-48db-8b0b-010a079f09f7","resolution":{"observed_at":"2026-08-16T05:17:37.890249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01652","last_updated":"2022-02-08T20:26:45Z","snapshot_observed_at":"2026-08-14T06:11:14.515796Z","submitted_at":"2021-09-03T17:55:52Z","title":"Finetuned Language Models Are Zero-Shot Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01652","snapshot_observed_at":"2026-08-16T05:17:37.894403Z","title":"Y ., Guu, K., Yu, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.894403Z"},"links":{"cited_paper":"/paper/2109.01652","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:8d168e1fc84cdeb4532707313e2c8f75a88bd6954576ff22e1fc4ebe9610cd61","observation_id":"bc5a8f8a-96e0-4414-8d82-7aba5ad95225","resolution":{"observed_at":"2026-08-16T05:17:37.894403Z","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":"2026-08-16T05:17:38.377762Z","title":"V ., Zhou, D., et al","venue":null,"work_id":"f5ec8abb-c552-48a7-a47a-829250eb3904","year":2022},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.898697Z"},"links":{"citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:38abce8d13f7ea74550110291c1cac53e1c421f2ae9d65ba8d3efcbc2f23c572","observation_id":"6c532b69-f397-4571-a496-a5b23b49873c","resolution":{"observed_at":"2026-08-16T05:17:38.382643Z","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":"2406.16838","last_updated":"2024-11-20T17:57:26Z","snapshot_observed_at":"2026-08-16T13:40:02.392308Z","submitted_at":"2024-06-24T17:45:59Z","title":"From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16838","snapshot_observed_at":"2026-08-16T05:17:37.903172Z","title":"From decoding to meta-generation: Inference-time algorithms for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.903172Z"},"links":{"cited_paper":"/paper/2406.16838","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:38a78b5350187576fdc08c80dfe3789bcfca89d49f8c6a95c29e154331a4f45b","observation_id":"cbb915af-e343-4c3a-a9c7-0b98dac65a6a","resolution":{"observed_at":"2026-08-16T05:17:37.903172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05443","last_updated":"2023-06-08T14:20:29Z","snapshot_observed_at":"2026-08-16T15:25:28.218063Z","submitted_at":"2023-06-08T14:20:29Z","title":"PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05443","snapshot_observed_at":"2026-08-16T05:17:37.907493Z","title":"Pixiu: A large language model, instruction data and evaluation benchmark for finance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.907493Z"},"links":{"cited_paper":"/paper/2306.05443","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:b6dea1be69b6e2409df823f6618ddd47152d19e7f9e4f0f635b8947fb884d340","observation_id":"ce00ad48-d923-4f11-97ea-9ee4e8ea4d92","resolution":{"observed_at":"2026-08-16T05:17:37.907493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10601","last_updated":"2023-12-03T22:50:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T23:16:17Z","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10601","snapshot_observed_at":"2026-08-16T05:17:37.911693Z","title":"L., Cao, Y ., and Narasimhan, K","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.911693Z"},"links":{"cited_paper":"/paper/2305.10601","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:c836ab58a7954cef3be57922f67092428ceae52c20755afcb9a32b73317e2b92","observation_id":"f473890c-8ac1-406e-9ad1-48edb6e233ec","resolution":{"observed_at":"2026-08-16T05:17:37.911693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05689","last_updated":"2020-03-12T10:12:22Z","snapshot_observed_at":"2026-08-11T04:06:19.914166Z","submitted_at":"2020-03-12T10:12:22Z","title":"Hyper-Parameter Optimization: A Review of Algorithms and Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05689","snapshot_observed_at":"2026-08-16T05:17:37.915157Z","title":"and Zhu, H","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.915157Z"},"links":{"cited_paper":"/paper/2003.05689","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:83dd546953492ff44aa9647b06dc80b4b11c4e3c314dbb4cbf5a28c9e907f2a0","observation_id":"73c8174f-1a55-42eb-94b2-cddd25d5d5a7","resolution":{"observed_at":"2026-08-16T05:17:37.915157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03094","last_updated":"2024-02-08T22:02:22Z","snapshot_observed_at":"2026-08-16T14:55:00.404595Z","submitted_at":"2023-10-04T18:21:17Z","title":"Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03094","snapshot_observed_at":"2026-08-16T05:17:37.918676Z","title":"Large language model cascades with mixture of thoughts representations for cost-efficient reasoning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.918676Z"},"links":{"cited_paper":"/paper/2310.03094","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:78557bdc1c3fc2bfd3e36cfc9e91d22119d7dbe054c3d2f0a2460e33351c54e1","observation_id":"348fadd8-14d8-43b7-8563-4927146f2b48","resolution":{"observed_at":"2026-08-16T05:17:37.918676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-08-15T09:37:44.321271Z","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":"2026-08-16T05:17:37.922358Z","title":"Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T05:17:37.922358Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2505.01449"},"observation_digest":"sha256:431e425408bf4e34759cfe514b92cbcd6bc5fb38999035bf82bdb52e5efc0eca","observation_id":"65e71986-e44f-4b33-813b-461c209e08db","resolution":{"observed_at":"2026-08-16T05:17:37.922358Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.01449","last_updated":"2025-04-30T02:06:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T08:34:59.378341Z","submitted_at":"2025-04-30T02:06:26Z","title":"COSMOS: Predictable and Cost-Effective Adaptation of LLMs"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":42},"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 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2505.01449."}