{"as_of":"2026-08-11T08:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:26793911c51aa4b7ff4d3b89446b4ebb212b8d1f1e3b83cf363f48f628ef516d","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:59:05.518604Z","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":"2026-05-16T07:00:43.273983Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.10559","last_updated":"2025-05-15T17:59:22Z","snapshot_observed_at":"2026-08-07T15:44:58.187412Z","submitted_at":"2025-05-15T17:59:22Z","title":"Neural Thermodynamic Laws for Large Language Model Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10559","snapshot_observed_at":"2026-08-06T19:59:05.518604Z","title":"Neural thermodynamic laws for large language model training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04206","last_updated":"2025-07-06T01:34:12Z","snapshot_observed_at":"2026-08-06T19:50:42.879569Z","submitted_at":"2025-07-06T01:34:12Z","title":"Mpemba Effect in Large-Language Model Training Dynamics: A Minimal Analysis of the Valley-River model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:59:05.518604Z"},"links":{"cited_paper":"/paper/2505.10559","citing_paper":"/paper/2507.04206"},"observation_digest":"sha256:b2fa7b87eed5c9fbf3e80408d468b6dbbd9cff8cc8c53b9bf8a4a01fe24ecfd9","observation_id":"52d8cbf4-a455-4428-b670-c19fcb8309d5","resolution":{"observed_at":"2026-08-06T19:59:05.518604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10559","last_updated":"2025-05-15T17:59:22Z","snapshot_observed_at":"2026-08-07T15:44:58.187412Z","submitted_at":"2025-05-15T17:59:22Z","title":"Neural Thermodynamic Laws for Large Language Model Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10559","snapshot_observed_at":"2026-08-06T11:44:05.058346Z","title":"Neural thermodynamic laws for large language model training, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22448","last_updated":"2025-07-30T07:55:33Z","snapshot_observed_at":"2026-08-09T19:06:02.138099Z","submitted_at":"2025-07-30T07:55:33Z","title":"Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-06T11:44:05.058346Z"},"links":{"cited_paper":"/paper/2505.10559","citing_paper":"/paper/2507.22448"},"observation_digest":"sha256:2e99ada315be3a9e94385872ad3e8753a8f164d90f35ed38a73b5b407fbcdfe3","observation_id":"b88f9cc7-7416-4d8d-b8e8-91e37a2868d3","resolution":{"observed_at":"2026-08-06T11:44:05.058346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10559","last_updated":"2025-05-15T17:59:22Z","snapshot_observed_at":"2026-08-07T15:44:58.187412Z","submitted_at":"2025-05-15T17:59:22Z","title":"Neural Thermodynamic Laws for Large Language Model Training","version":1},"cited_work":{"arxiv_id":"2505.10559","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.10559","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Liu, Z., Liu, Y ., Gore, J., and Tegmark, M","venue":null,"work_id":"bb972541-553e-494b-9884-8b8054dcf1c5","year":null},"citing_paper":{"arxiv_id":"2602.04774","last_updated":"2026-05-08T16:24:57Z","snapshot_observed_at":"2026-08-03T01:34:12.578849Z","submitted_at":"2026-02-04T17:11:36Z","title":"Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-16T06:58:38.927268Z"},"links":{"cited_paper":"/paper/2505.10559","citing_paper":"/paper/2602.04774"},"observation_digest":"sha256:5787723a7c1bde3a3d874536c8f616b5b5f17798c9512f1e1e94f9b7f0a994b1","observation_id":"4617356d-24ea-480a-be5f-e57e497bff5e","resolution":{"observed_at":"2026-05-16T07:00:43.275595Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.10559/citation-record","integrity":"/paper/2505.10559/integrity","json":"/paper/2505.10559/citation-record.json","paper":"/paper/2505.10559"},"outbound":[],"paper":{"arxiv_id":"2505.10559","last_updated":"2025-05-15T17:59:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:44:58.187412Z","submitted_at":"2025-05-15T17:59:22Z","title":"Neural Thermodynamic Laws for Large Language Model Training"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2505.10559."}