{"as_of":"2026-08-08T22:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:52924d96f816ca4777f8923f3da51918a201a77a5fa36fd138165dd8154a92ab","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:33:45.716553Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T16:48:39.721562Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.19390","last_updated":"2025-06-03T06:01:14Z","snapshot_observed_at":"2026-07-06T17:52:30.328601Z","submitted_at":"2024-03-28T13:01:18Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2403.19390","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.19390","snapshot_observed_at":"2026-07-03T16:48:39.721562Z","title":"Checkpoint merging via bayesian optimization in llm pretraining.arXiv preprint arXiv:2403.19390,","venue":null,"work_id":"a14df99a-28b5-4776-92fb-992f8e325eb4","year":2024},"citing_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},"reference_index":132,"source":"pdf_text","source_observed_at":"2026-05-17T22:16:04.386706Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2408.07666"},"observation_digest":"sha256:683c35408f84c9b36c85a60bd406648326e7139d55371cf37f3be838c572f3af","observation_id":"792949d9-2653-4ad7-ab01-3a5f4db5f1ea","resolution":{"observed_at":"2026-05-17T22:16:04.542620Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19390","last_updated":"2025-06-03T06:01:14Z","snapshot_observed_at":"2026-07-06T17:52:30.328601Z","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":"2026-08-07T14:33:45.716553Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18548","last_updated":"2025-05-24T06:28:21Z","snapshot_observed_at":"2026-08-07T14:27:28.519611Z","submitted_at":"2025-05-24T06:28:21Z","title":"Composable Cross-prompt Essay Scoring by Merging Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T14:33:45.716553Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2505.18548"},"observation_digest":"sha256:7cdf308e67773fd5383adca5455a9a369a84bb795e05ed46b19f81e2957cb5e4","observation_id":"08f1d924-210a-4655-b52f-6b0d2e0c7bef","resolution":{"observed_at":"2026-08-07T14:33:45.716553Z","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":"2026-07-06T17:52:30.328601Z","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":"2026-08-06T14:49:40.278771Z","title":"Checkpoint merging via bayesian optimization in LLM pretraining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17634","last_updated":"2025-08-11T08:36:31Z","snapshot_observed_at":"2026-08-06T19:16:35.269127Z","submitted_at":"2025-07-23T16:02:06Z","title":"WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T14:49:40.278771Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2507.17634"},"observation_digest":"sha256:e013653009a1aba8fbd7f9509a7ad81313f322a0de3e349d53bf76d2b860e458","observation_id":"04763bb9-e42d-4b7d-98ae-5d4af2c4573e","resolution":{"observed_at":"2026-08-06T14:49:40.278771Z","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":"2026-07-06T17:52:30.328601Z","submitted_at":"2024-03-28T13:01:18Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2403.19390","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.19390","snapshot_observed_at":"2026-07-03T16:48:39.721562Z","title":"Checkpoint merging via bayesian optimization in llm pretraining.arXiv preprint arXiv:2403.19390,","venue":null,"work_id":"a14df99a-28b5-4776-92fb-992f8e325eb4","year":2024},"citing_paper":{"arxiv_id":"2606.02606","last_updated":"2026-05-23T15:56:16Z","snapshot_observed_at":"2026-08-02T04:44:49.560527Z","submitted_at":"2026-05-23T15:56:16Z","title":"ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T15:06:08.515588Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2606.02606"},"observation_digest":"sha256:6c5bc7f240cfca770ae4080d402b3114a2a64aee45118f9db1f0f53182f552e1","observation_id":"10300319-6727-4932-887f-5f9fda44b7fb","resolution":{"observed_at":"2026-06-30T15:44:48.862895Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19390","last_updated":"2025-06-03T06:01:14Z","snapshot_observed_at":"2026-07-06T17:52:30.328601Z","submitted_at":"2024-03-28T13:01:18Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2403.19390","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.19390","snapshot_observed_at":"2026-07-03T16:48:39.721562Z","title":"Checkpoint merging via bayesian optimization in llm pretraining.arXiv preprint arXiv:2403.19390,","venue":null,"work_id":"a14df99a-28b5-4776-92fb-992f8e325eb4","year":2024},"citing_paper":{"arxiv_id":"2607.02291","last_updated":"2026-07-02T15:08:56Z","snapshot_observed_at":"2026-08-07T15:44:37.020127Z","submitted_at":"2026-07-02T15:08:56Z","title":"Optimizing Visual Generative Models via Distribution-wise Rewards","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-03T16:39:12.711424Z"},"links":{"cited_paper":"/paper/2403.19390","citing_paper":"/paper/2607.02291"},"observation_digest":"sha256:ccc3034fd15c20f53b58630774c3a624ab1cb2a92b7095adacaaf74973b48003","observation_id":"2664816c-c256-4434-952a-102edce464c3","resolution":{"observed_at":"2026-07-03T16:48:39.723111Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.19390/citation-record","integrity":"/paper/2403.19390/integrity","json":"/paper/2403.19390/citation-record.json","paper":"/paper/2403.19390"},"outbound":[],"paper":{"arxiv_id":"2403.19390","last_updated":"2025-06-03T06:01:14Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:52:30.328601Z","submitted_at":"2024-03-28T13:01:18Z","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.19390."}