{"as_of":"2026-08-08T14:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:184a2268f75f8ece5ffd77e6374e01d00e45986329a83016a654a24239cd11a1","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:16:00.996148Z","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":"2026-07-04T18:40:03.180712Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-08-07T15:16:00.996148Z","title":"From 128k to 4m: Efficient training of ultra-long context large language models.arXiv preprint arXiv:2504.06214, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15774","last_updated":"2025-05-21T17:26:11Z","snapshot_observed_at":"2026-08-07T15:09:42.504814Z","submitted_at":"2025-05-21T17:26:11Z","title":"Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T15:16:00.996148Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2505.15774"},"observation_digest":"sha256:c8b86ad672585be6e1f3d19e790d33de218d014519cee3b2a2558779dd5fedc0","observation_id":"dfb3ad48-2a43-47d1-9fef-2f118a452604","resolution":{"observed_at":"2026-08-07T15:16:00.996148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-07-04T18:40:03.180712Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":"28a7d8f7-1a08-4ac0-87ad-4fa804c503cd","year":2024},"citing_paper":{"arxiv_id":"2604.14339","last_updated":"2026-04-15T18:46:35Z","snapshot_observed_at":"2026-07-06T23:02:09.426599Z","submitted_at":"2026-04-15T18:46:35Z","title":"Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T13:42:00.440049Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2604.14339"},"observation_digest":"sha256:01859f93b8353221e230ffdbf1197c75873aa5275b0fab578b1a9565c3debe50","observation_id":"dd34b158-bdcf-4517-8278-3005e3f6e66a","resolution":{"observed_at":"2026-05-10T13:45:28.120415Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-07-04T18:40:03.180712Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":"28a7d8f7-1a08-4ac0-87ad-4fa804c503cd","year":2024},"citing_paper":{"arxiv_id":"2605.05365","last_updated":"2026-05-06T18:44:08Z","snapshot_observed_at":"2026-08-07T21:50:57.011975Z","submitted_at":"2026-05-06T18:44:08Z","title":"ZAYA1-8B Technical Report","version":1},"reference_index":198,"source":"arxiv_source","source_observed_at":"2026-05-08T17:36:37.182196Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2605.05365"},"observation_digest":"sha256:d3eeb76815f7394a5899f93baeab1ab9ef8253ca55c38df341d3604261bdebf7","observation_id":"20ccfe7c-cfdc-409b-ad11-236c93f7e9b3","resolution":{"observed_at":"2026-05-11T17:26:05.154164Z","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":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-07-04T18:40:03.180712Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":"28a7d8f7-1a08-4ac0-87ad-4fa804c503cd","year":2024},"citing_paper":{"arxiv_id":"2605.22223","last_updated":"2026-05-21T09:26:37Z","snapshot_observed_at":"2026-08-06T07:24:40.257529Z","submitted_at":"2026-05-21T09:26:37Z","title":"How Many Different Outputs Can a Transformer Generate?","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-22T07:09:23.107309Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2605.22223"},"observation_digest":"sha256:8f04ac7e56b5485554c33e90d4ba224b90626b510f50733491f478e4d3c4a278","observation_id":"2c63f0d9-3753-42a0-b754-98863efae634","resolution":{"observed_at":"2026-05-22T07:11:12.744720Z","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":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-07-04T18:40:03.180712Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":"28a7d8f7-1a08-4ac0-87ad-4fa804c503cd","year":2024},"citing_paper":{"arxiv_id":"2606.24320","last_updated":"2026-06-25T20:48:22Z","snapshot_observed_at":"2026-07-06T23:58:54.018557Z","submitted_at":"2026-06-23T08:57:34Z","title":"ZONOS2 Technical Report","version":1},"reference_index":239,"source":"arxiv_source","source_observed_at":"2026-06-25T22:37:15.072758Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2606.24320"},"observation_digest":"sha256:09f276d36f1373ceedb43fff53732f477cfb8b14929443846560d28130240c37","observation_id":"ea8533d5-77ec-4638-b380-70359f522517","resolution":{"observed_at":"2026-07-04T18:40:03.182027Z","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":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":"2504.06214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-07-04T18:40:03.180712Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":"28a7d8f7-1a08-4ac0-87ad-4fa804c503cd","year":2024},"citing_paper":{"arxiv_id":"2606.24320","last_updated":"2026-06-25T20:48:22Z","snapshot_observed_at":"2026-07-06T23:58:54.018557Z","submitted_at":"2026-06-23T08:57:34Z","title":"ZONOS2 Technical Report","version":2},"reference_index":239,"source":"arxiv_source","source_observed_at":"2026-06-29T02:07:31.791835Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2606.24320"},"observation_digest":"sha256:9e7087753951d35b2b29e85ba259b0437823664e0e57646831b0798b2dbcb58e","observation_id":"62c3ea62-a6fd-4bec-9716-acb0b19113e2","resolution":{"observed_at":"2026-07-01T18:15:58.911311Z","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":"2504.06214","last_updated":"2025-04-08T16:58:58Z","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.06214","snapshot_observed_at":"2026-08-01T09:52:09.146522Z","title":"arXiv preprint arXiv:2504.06214 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27308","last_updated":"2026-07-29T17:49:04Z","snapshot_observed_at":"2026-08-05T10:53:49.344314Z","submitted_at":"2026-07-29T17:49:04Z","title":"ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution","version":1},"reference_index":208,"source":"arxiv_source","source_observed_at":"2026-08-01T09:52:09.146522Z"},"links":{"cited_paper":"/paper/2504.06214","citing_paper":"/paper/2607.27308"},"observation_digest":"sha256:c6fb12e727a59220a3271b01ccbb4432d2185bd7fd60d530c2142c97112b7aa7","observation_id":"8532207a-9dd6-4a16-8ac1-feafc98ff1d3","resolution":{"observed_at":"2026-08-01T09:52:09.146522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.06214/citation-record","integrity":"/paper/2504.06214/integrity","json":"/paper/2504.06214/citation-record.json","paper":"/paper/2504.06214"},"outbound":[],"paper":{"arxiv_id":"2504.06214","last_updated":"2025-04-08T16:58:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T16:07:26.746636Z","submitted_at":"2025-04-08T16:58:58Z","title":"From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models"},"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 7 inbound Pith citation observations for arXiv:2504.06214."}