{"as_of":"2026-08-18T14:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fc947c3ef599fe9838a18b973376af0fb0fb199049c14ba512e5016588580c34","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:08:34.119606Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2504.18580/citation-record","integrity":"/paper/2504.18580/integrity","json":"/paper/2504.18580/citation-record.json","paper":"/paper/2504.18580"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.07666","last_updated":"2025-12-31T04:06:49Z","snapshot_observed_at":"2026-08-07T23:28:24.025478Z","submitted_at":"2024-08-14T16:58:48Z","title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.07666","snapshot_observed_at":"2026-08-16T11:08:33.023237Z","title":"Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.023237Z"},"links":{"cited_paper":"/paper/2408.07666","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:6354af35059327ad6bfd436301b23147bf7066d61a1614c8cf0ae6c3dc0e20e3","observation_id":"f5860b02-7db9-4f78-9f27-eb780f683c95","resolution":{"observed_at":"2026-08-16T11:08:33.023237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13257","last_updated":"2025-01-09T22:21:56Z","snapshot_observed_at":"2026-08-17T18:27:55.545560Z","submitted_at":"2024-03-20T02:38:01Z","title":"Arcee's MergeKit: A Toolkit for Merging Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13257","snapshot_observed_at":"2026-08-16T11:08:33.149938Z","title":"Arcee's mergekit: A toolkit for merging large language models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.149938Z"},"links":{"cited_paper":"/paper/2403.13257","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:e57b4e0378fe041e1518247f37ce0482e13d8724787f56b1a6d8161f206bf6b3","observation_id":"e60bff05-2974-4a1b-b911-7ab1a367e151","resolution":{"observed_at":"2026-08-16T11:08:33.149938Z","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":"2026-08-16T11:08:34.618212Z","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","venue":null,"work_id":"61a3e599-a959-46e3-b988-4a60b1f8e224","year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.166755Z"},"links":{"citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:4d87123414ec5b91e05174e2aeb2dc95944fc0314c6987748a841e68a5efb7e3","observation_id":"18013c09-698e-4da4-80f7-4b9886f44280","resolution":{"observed_at":"2026-08-16T11:08:34.692962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-09T19:52:33.533277Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-16T11:08:33.172935Z","title":"Rae, Oriol Vinyals, and Laurent Sifre","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.172935Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:1dee92bac1c7d3eef7b0c1bf6c711b7318a434506259964820821272130a0e8e","observation_id":"507c2cf2-7aba-448a-ad39-7c658b58ffb9","resolution":{"observed_at":"2026-08-16T11:08:33.172935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.09832","last_updated":"2022-08-26T16:43:05Z","snapshot_observed_at":"2026-08-16T17:40:51.329959Z","submitted_at":"2021-11-18T17:59:35Z","title":"Merging Models with Fisher-Weighted Averaging","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.09832","snapshot_observed_at":"2026-08-16T11:08:33.239771Z","title":"Merging models with fisher-weighted averaging, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.239771Z"},"links":{"cited_paper":"/paper/2111.09832","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:a7f9183b4a5c77723db81c0f0b418d1b5abc584f8b9a88c1e775f142bb01959c","observation_id":"f3cea4ae-beaa-487f-a929-1356cb85877a","resolution":{"observed_at":"2026-08-16T11:08:33.239771Z","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":"2026-08-16T11:08:33.349679Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.349679Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:4b653ed8d52ebc8d9a74527bb9422d52e6b47c5aaca876ee30b21af490f090e1","observation_id":"7f558868-cae4-4071-a943-ab642daef82d","resolution":{"observed_at":"2026-08-16T11:08:33.349679Z","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":"2026-08-16T11:08:33.355692Z","title":"Editing models with task arithmetic","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.355692Z"},"links":{"citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:bf459690144db765f1328f347b183a8883a3ebb5bb844b890b1e6e423c63a030","observation_id":"aea9cb8c-3742-4dc1-87a2-5cfabd085bf9","resolution":{"observed_at":"2026-08-16T11:08:33.355692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19390","last_updated":"2025-06-03T06:01:14Z","snapshot_observed_at":"2026-08-18T14:06:07.369751Z","submitted_at":"2024-03-28T13:01:18Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.19390","snapshot_observed_at":"2026-08-16T11:08:33.360972Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining , March 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.360972Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:5465c958a45e7a83d5426f0c3e27c59086be2b467d0ed0a0011d68382a115740","observation_id":"0d851911-87b9-4c8d-97b1-5cff93249ce0","resolution":{"observed_at":"2026-08-16T11:08:33.360972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12148","last_updated":"2023-12-19T13:31:24Z","snapshot_observed_at":"2026-08-16T14:33:23.059607Z","submitted_at":"2023-12-19T13:31:24Z","title":"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.12148","snapshot_observed_at":"2026-08-16T11:08:33.457953Z","title":"Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.457953Z"},"links":{"cited_paper":"/paper/2312.12148","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:2c463c0c393c122e6f40a04926b5cb965f4cb80de72cabf8c9092964ec02ed61","observation_id":"37847fbc-de95-4241-ae4c-6a7481d45f3f","resolution":{"observed_at":"2026-08-16T11:08:33.457953Z","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":"2026-08-16T11:08:34.588184Z","title":"D o RA : Weight-decomposed low-rank adaptation","venue":null,"work_id":"dcd2a5a3-5fb3-4684-833a-daef9e649905","year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.464897Z"},"links":{"citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:d563fde1425bc6937a45c3f50c6ff85a4b9c53987f11a9387c6c2897c12abb05","observation_id":"93dd9090-1112-4084-9b98-755b3af1d91f","resolution":{"observed_at":"2026-08-16T11:08:34.595268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14314","last_updated":"2023-05-23T17:50:33Z","snapshot_observed_at":"2026-08-13T23:56:42.354755Z","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":"2026-08-16T11:08:33.470849Z","title":"Qlora: Efficient finetuning of quantized llms, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.470849Z"},"links":{"cited_paper":"/paper/2305.14314","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:f85ad98e005e3d2fffa90f30818101836dc06d408def5999ecc1ddce1af9ba4c","observation_id":"0a669d67-6568-4b64-9d7a-a16d5e3766ca","resolution":{"observed_at":"2026-08-16T11:08:33.470849Z","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":"2026-08-16T11:08:33.550262Z","title":"Instruction Tuning for Large Language Models : A Survey , March 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.550262Z"},"links":{"citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:afd08f52d4eda4c1a4c0cac7c05b3f73d077e6f9b5b63eeaa5ca7aa751d2aa6b","observation_id":"e608c803-f81c-433f-aeb3-9df50b5c393c","resolution":{"observed_at":"2026-08-16T11:08:33.550262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14734","last_updated":"2024-11-01T20:05:19Z","snapshot_observed_at":"2026-08-16T13:50:07.192537Z","submitted_at":"2024-05-23T16:01:46Z","title":"SimPO: Simple Preference Optimization with a Reference-Free Reward","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14734","snapshot_observed_at":"2026-08-16T11:08:33.628621Z","title":"SimPO : Simple Preference Optimization with a Reference - Free Reward , July 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.628621Z"},"links":{"cited_paper":"/paper/2405.14734","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:4c5ce2b408ae15a122f837229240efbd6cb8a0cca00d0b7ebbe17c9d214a9a52","observation_id":"a3ab10ac-f381-42a0-9f3a-b2659842781f","resolution":{"observed_at":"2026-08-16T11:08:33.628621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.06356","last_updated":"2022-06-13T17:52:38Z","snapshot_observed_at":"2026-08-17T04:35:21.129952Z","submitted_at":"2022-06-13T17:52:38Z","title":"Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.06356","snapshot_observed_at":"2026-08-16T11:08:33.686370Z","title":"Modern Distributed Data - Parallel Large - Scale Pre -training Strategies For NLP models, June 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.686370Z"},"links":{"cited_paper":"/paper/2206.06356","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:b669361392850406651f5e5a9a48f9609abf861521f7559c595e7d0ee9fc4760","observation_id":"45fa7540-30dd-4905-a29e-110b67cda58b","resolution":{"observed_at":"2026-08-16T11:08:33.686370Z","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":"2026-08-16T11:08:33.694147Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.694147Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:b79fe8f1be3c4f548ad3a325cbf5d102e27ee286de35597791ac547dbc1153ba","observation_id":"27ed8cfa-98bb-46fb-9a5b-949514c4f9b7","resolution":{"observed_at":"2026-08-16T11:08:33.694147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14830","last_updated":"2024-02-16T23:44:38Z","snapshot_observed_at":"2026-08-16T14:17:49.494789Z","submitted_at":"2024-02-16T23:44:38Z","title":"Orca-Math: Unlocking the potential of SLMs in Grade School Math","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14830","snapshot_observed_at":"2026-08-16T11:08:33.699233Z","title":"Orca- Math : Unlocking the potential of SLMs in Grade School Math , February 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.699233Z"},"links":{"cited_paper":"/paper/2402.14830","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:ffbc1414d4bf8cdbb54febef7a3c61f3b8db681a1104245a67c8826625485326","observation_id":"c398728c-91bd-41c9-899c-6af7ec514993","resolution":{"observed_at":"2026-08-16T11:08:33.699233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05862","last_updated":"2022-04-12T15:02:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-12T15:02:38Z","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05862","snapshot_observed_at":"2026-08-16T11:08:33.705313Z","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback , April 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.705313Z"},"links":{"cited_paper":"/paper/2204.05862","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:f40d260ee70beb0fff0aea582773c02f074a804db3ee10cd010d31a0d0d5b3d6","observation_id":"723cea49-6100-4c78-bf88-f90c35bb931d","resolution":{"observed_at":"2026-08-16T11:08:33.705313Z","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":"2026-08-16T11:08:33.779775Z","title":"Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.779775Z"},"links":{"citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:2a1264fcef74567a1019e85f4b2cc858d64f6a63735e7396b31fd18bcee01a75","observation_id":"bd8ada7b-e0db-4eda-a1dd-7aa0d32f10b5","resolution":{"observed_at":"2026-08-16T11:08:33.779775Z","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":"2026-08-16T11:08:33.861704Z","title":"Training Verifiers to Solve Math Word Problems , November 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.861704Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:55d6969ebfea9d9443c70188f2d408b812839260a56cb397198edb072fbe7a70","observation_id":"9ceef7f9-f054-4137-98ac-c3cafd74c7f6","resolution":{"observed_at":"2026-08-16T11:08:33.861704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19255","last_updated":"2024-07-02T03:46:03Z","snapshot_observed_at":"2026-08-16T14:14:06.180354Z","submitted_at":"2024-02-29T15:26:14Z","title":"GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19255","snapshot_observed_at":"2026-08-16T11:08:33.867719Z","title":"GSM - Plus : A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers , July 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.867719Z"},"links":{"cited_paper":"/paper/2402.19255","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:223f62add939612c969e6384adb193aaeca418c96e6f6bba467f9840d5aa6a97","observation_id":"f61b2e55-0f0b-44bd-97d7-20c10f3e7ed0","resolution":{"observed_at":"2026-08-16T11:08:33.867719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.09509","last_updated":"2022-07-14T13:04:29Z","snapshot_observed_at":"2026-08-16T17:13:42.482483Z","submitted_at":"2022-03-17T17:57:56Z","title":"ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.09509","snapshot_observed_at":"2026-08-16T11:08:33.943548Z","title":"ToxiGen : A Large - Scale Machine - Generated Dataset for Adversarial and Implicit Hate Speech Detection , July 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.943548Z"},"links":{"cited_paper":"/paper/2203.09509","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:00f5526ceb6ca5d29bcf28a4dc64b7092c765d257a7a7b6ec52286f4e5938e86","observation_id":"93eaebe5-4638-4aee-adf5-0fa46a3dcdce","resolution":{"observed_at":"2026-08-16T11:08:33.943548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.07958","last_updated":"2022-05-08T02:43:02Z","snapshot_observed_at":"2026-08-03T16:26:48.747700Z","submitted_at":"2021-09-08T17:15:27Z","title":"TruthfulQA: Measuring How Models Mimic Human Falsehoods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.07958","snapshot_observed_at":"2026-08-16T11:08:34.014620Z","title":"TruthfulQA : Measuring How Models Mimic Human Falsehoods , May 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:34.014620Z"},"links":{"cited_paper":"/paper/2109.07958","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:5ee3f398d0c4e9c480130f78fd365176ef9fafe9bda4a6bbb6bbb9a365c1a92b","observation_id":"baed840f-6caf-409e-8586-06591305abac","resolution":{"observed_at":"2026-08-16T11:08:34.014620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.01708","last_updated":"2023-10-27T01:09:31Z","snapshot_observed_at":"2026-08-16T20:27:57.883150Z","submitted_at":"2023-06-02T17:31:32Z","title":"TIES-Merging: Resolving Interference When Merging Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.01708","snapshot_observed_at":"2026-08-16T11:08:34.119606Z","title":"TIES - Merging : Resolving Interference When Merging Models , October 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:34.119606Z"},"links":{"cited_paper":"/paper/2306.01708","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:741df438743b5cea14a6b62664cff939ac1ec7701b338036d27d9127294076b3","observation_id":"c294cd32-c971-4ceb-bf3a-e0ef08655f91","resolution":{"observed_at":"2026-08-16T11:08:34.119606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":23},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2504.18580."}