{"as_of":"2026-08-16T02:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e61b818d12d791bcfd656b84cfa955257283ed43e2d47e5eddec07f7e9fd4ddf","coverage":[{"denominator":5,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T15:22:21.955640Z","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":"2026-08-15T06:32:42.880941+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/2604.10848/citation-record","integrity":"/paper/2604.10848/integrity","json":"/paper/2604.10848/citation-record.json","paper":"/paper/2604.10848"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.17686","last_updated":"2024-07-25T01:07:09Z","snapshot_observed_at":"2026-08-14T16:39:30.312151Z","submitted_at":"2024-07-25T01:07:09Z","title":"Transformers on Markov Data: Constant Depth Suffices","version":1},"cited_work":{"arxiv_id":"2407.17686","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.17686","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Transformers on markov data: Constant depth suffices","venue":null,"work_id":"fa1eaa12-c775-4d07-92c7-b5f93ee44576","year":2024},"citing_paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T15:22:21.955640Z"},"links":{"cited_paper":"/paper/2407.17686","citing_paper":"/paper/2604.10848"},"observation_digest":"sha256:0a048de2885d944252940ed14a7634c963722a296e1f3c424e8c61b0224d8ed5","observation_id":"fe242d53-4e3a-4b84-98e4-bc8ac54c0e9a","resolution":{"observed_at":"2026-05-11T10:41:05.038608Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-06-05T21:23:00.469572Z","title":"It represents the theoretical performance limit for inference under the MTD model assumptions, providing a gold-standard benchmark against which other estimators can be compared","venue":null,"work_id":"6042e035-9a6e-4ff3-b4e9-04f02882c324","year":2026},"citing_paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:22:21.955640Z"},"links":{"citing_paper":"/paper/2604.10848"},"observation_digest":"sha256:35f41e39d9023608e317a6f72c0d2d3d77e93505f757cf0d7461de693d04a917","observation_id":"212bbb76-5681-4c23-a220-48789a0d8d77","resolution":{"observed_at":"2026-05-18T02:40:41.893072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-06-05T21:23:00.469572Z","title":"no evidence","venue":null,"work_id":"029bd0c7-2735-4693-a214-c85af040b5be","year":2026},"citing_paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T15:22:21.955640Z"},"links":{"citing_paper":"/paper/2604.10848"},"observation_digest":"sha256:69a79c3cf2adc7f5aae29fcf699f24b6de96452fb46251c2a3cd04f52962ad49","observation_id":"449fe2f6-50c6-4cef-9bd1-ac964298f925","resolution":{"observed_at":"2026-05-18T02:40:41.889593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-06-05T21:23:00.469572Z","title":"Using equation 43 we get the upper bound∥c t∥2 2 ≤m·1 2 =mfor everyt","venue":null,"work_id":"a8bdc1a9-5d29-4cd8-9f27-c73d3ef419e2","year":2026},"citing_paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T15:22:21.955640Z"},"links":{"citing_paper":"/paper/2604.10848"},"observation_digest":"sha256:b0d451b60668f57882c6c540f15db28e1ec1d2ef869786972fc5946fa2f0fe85","observation_id":"d30ed26a-c687-4074-abbf-9cf3569d7c1d","resolution":{"observed_at":"2026-05-18T02:40:41.896390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-06-05T21:23:00.469572Z","title":"Using this inequality we obtain, for everyt, m2 S2 t ∥ct∥2 2 ≤ m2 S2 t S2 t =m 2","venue":null,"work_id":"428d403b-b738-4710-80a6-1dd829cdd4e2","year":2026},"citing_paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T15:22:21.955640Z"},"links":{"citing_paper":"/paper/2604.10848"},"observation_digest":"sha256:8d65cda1413ffd777bcc2af8c94329a44354c51daab1bc9adc5bf77445552519","observation_id":"b752d62d-d8ee-4a70-9892-056bfa49f1f3","resolution":{"observed_at":"2026-05-18T02:40:41.900041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.10848","last_updated":"2026-04-12T22:45:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T01:02:10.623303Z","submitted_at":"2026-04-12T22:45:43Z","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent"},"reference_resolution":{"displayed":5,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":5},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2604.10848."}