{"as_of":"2026-08-11T15:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e919dc545fdaadf957735c7c5301c4fd90e3bcb3bda1ff6d0f62a972f491f0e","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T05:22:33.116839Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-08T22:25:39.562757Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-08-09T05:22:33.116839Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.03275","last_updated":"2025-09-01T18:25:38Z","snapshot_observed_at":"2026-08-09T05:15:11.459630Z","submitted_at":"2025-02-05T15:33:00Z","title":"Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-09T05:22:33.116839Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2502.03275"},"observation_digest":"sha256:cd9cffcc5655d81e9e4ec5842b6fa1f53a777d81d5f31ab6e92f0b65b3ce537f","observation_id":"6271ed04-48b0-4f08-9354-d7415b1682a5","resolution":{"observed_at":"2026-08-09T05:22:33.116839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-08-07T13:06:26.598216Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.22922","last_updated":"2025-05-28T22:51:43Z","snapshot_observed_at":"2026-08-10T19:45:26.924147Z","submitted_at":"2025-05-28T22:51:43Z","title":"Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T13:06:26.598216Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2505.22922"},"observation_digest":"sha256:5b3bf06f88f8597a5036ae56681302dea85cc3ec2ba426c6eea61a52390f67cb","observation_id":"e1196af2-e476-43c0-b6e7-eea51c406164","resolution":{"observed_at":"2026-08-07T13:06:26.598216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":"2504.20437","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-07-08T22:25:39.562757Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection.ArXiv, abs/2504.20437","venue":"cs.LG","work_id":"526fd339-4aa2-4fc2-a52a-afb50445505e","year":2025},"citing_paper":{"arxiv_id":"2605.11838","last_updated":"2026-05-12T09:24:59Z","snapshot_observed_at":"2026-07-06T23:23:34.924221Z","submitted_at":"2026-05-12T09:24:59Z","title":"Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-13T07:01:37.086159Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2605.11838"},"observation_digest":"sha256:cbc2e0691e028ff24c23475a124b70245e146f1be5497c7f61e4bd603893c57d","observation_id":"752e5699-365f-4d5a-b44f-e5fe9bffeff9","resolution":{"observed_at":"2026-05-13T07:02:27.442424Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":"2504.20437","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-07-08T22:25:39.562757Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection.ArXiv, abs/2504.20437","venue":"cs.LG","work_id":"526fd339-4aa2-4fc2-a52a-afb50445505e","year":2025},"citing_paper":{"arxiv_id":"2605.13652","last_updated":"2026-05-19T00:27:00Z","snapshot_observed_at":"2026-08-11T03:47:38.970095Z","submitted_at":"2026-05-13T15:11:37Z","title":"Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-20T20:24:26.681383Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2605.13652"},"observation_digest":"sha256:29956c03476c2eaacdcc97fdef481a4be823a21750e9d82654646785b8b3a1ba","observation_id":"603d1909-7060-44cb-bd28-0c799ebbad0e","resolution":{"observed_at":"2026-05-20T20:28:59.911059Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":"2504.20437","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-07-08T22:25:39.562757Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection.ArXiv, abs/2504.20437","venue":"cs.LG","work_id":"526fd339-4aa2-4fc2-a52a-afb50445505e","year":2025},"citing_paper":{"arxiv_id":"2605.18106","last_updated":"2026-06-22T06:06:33Z","snapshot_observed_at":"2026-07-06T23:28:59.503300Z","submitted_at":"2026-05-18T09:17:26Z","title":"Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers","version":1},"reference_index":143,"source":"pdf_text","source_observed_at":"2026-05-20T09:34:45.186929Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2605.18106"},"observation_digest":"sha256:41c8163ea98010596c06f0dfa472375ad50e43d0d063ba81f4610b32b59406f8","observation_id":"5526992a-b395-49ef-b7ab-c1c204fee7e7","resolution":{"observed_at":"2026-05-20T09:38:10.965089Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":"2504.20437","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-07-08T22:25:39.562757Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection.ArXiv, abs/2504.20437","venue":"cs.LG","work_id":"526fd339-4aa2-4fc2-a52a-afb50445505e","year":2025},"citing_paper":{"arxiv_id":"2605.18106","last_updated":"2026-06-22T06:06:33Z","snapshot_observed_at":"2026-07-06T23:28:59.503300Z","submitted_at":"2026-05-18T09:17:26Z","title":"Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers","version":4},"reference_index":145,"source":"pdf_text","source_observed_at":"2026-06-30T18:42:01.854481Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2605.18106"},"observation_digest":"sha256:23ec491a0b3075c7260ced0fdf301d0189507854e77385f976a47c21528af09d","observation_id":"ead297c7-32fa-4ca7-b7de-28cea3b8fb8d","resolution":{"observed_at":"2026-06-30T18:45:00.322879Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":"2504.20437","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-07-08T22:25:39.562757Z","title":"Galore 2: Large-scale llm pre-training by gradient low-rank projection.ArXiv, abs/2504.20437","venue":"cs.LG","work_id":"526fd339-4aa2-4fc2-a52a-afb50445505e","year":2025},"citing_paper":{"arxiv_id":"2607.05872","last_updated":"2026-07-19T20:06:25Z","snapshot_observed_at":"2026-08-02T08:27:39.275378Z","submitted_at":"2026-07-07T06:06:04Z","title":"No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-08T22:16:44.668076Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2607.05872"},"observation_digest":"sha256:61f23a2ece99f5b5406579c5048a5ecd7350821510597bc8ce3ccd99592d7e99","observation_id":"312471be-0631-45bb-83c4-7336807d2ebf","resolution":{"observed_at":"2026-07-08T22:25:39.564479Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20437","snapshot_observed_at":"2026-08-02T08:27:40.399086Z","title":"GaLore 2: Large-scale LLM pre-training by gradient low-rank projection.arXiv:2504.20437,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.05872","last_updated":"2026-07-19T20:06:25Z","snapshot_observed_at":"2026-08-02T08:27:39.275378Z","submitted_at":"2026-07-07T06:06:04Z","title":"No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T08:27:40.399086Z"},"links":{"cited_paper":"/paper/2504.20437","citing_paper":"/paper/2607.05872"},"observation_digest":"sha256:c6d557f1ae206d04ac23651c4b29bafcbd7ee3839d74316bf164b8ca8da64862","observation_id":"ffd69fd4-8e4f-401b-ab51-86250de8f7f2","resolution":{"observed_at":"2026-08-02T08:27:40.399086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.20437/citation-record","integrity":"/paper/2504.20437/integrity","json":"/paper/2504.20437/citation-record.json","paper":"/paper/2504.20437"},"outbound":[],"paper":{"arxiv_id":"2504.20437","last_updated":"2025-04-29T05:27:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:58:22.109682Z","submitted_at":"2025-04-29T05:27:02Z","title":"GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection"},"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 8 inbound Pith citation observations for arXiv:2504.20437."}