{"as_of":"2026-08-18T19:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70710b168de98d1502a93e4f4f42576e1f33b9c9022e67214af841ac241091d6","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:17:29.326487Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-16T11:17:29.326487Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.18574","last_updated":"2025-06-11T11:06:08Z","snapshot_observed_at":"2026-08-17T08:53:04.431268Z","submitted_at":"2025-04-22T16:15:19Z","title":"Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T11:17:29.326487Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2504.18574"},"observation_digest":"sha256:a596d5eaa310eeaba57c76282a61cdf9e7bf0b05cda6828631db1a1adfc99422","observation_id":"a9cda434-3e22-4b3e-baa3-d41ef015c255","resolution":{"observed_at":"2026-08-16T11:17:29.326487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-06T19:14:22.426273Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06203","last_updated":"2025-07-10T16:43:36Z","snapshot_observed_at":"2026-08-07T04:57:37.201438Z","submitted_at":"2025-07-08T17:29:07Z","title":"A Survey on Latent Reasoning","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:22.426273Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2507.06203"},"observation_digest":"sha256:4dec89ac6acb3439b2ef47a251ccf47d4dc8f89e2aa1b060264ae9d1d4c524d0","observation_id":"7e4daf09-9bf0-491f-a2e0-9bc9cc77ec6a","resolution":{"observed_at":"2026-08-06T19:14:22.426273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2510.04595","last_updated":"2026-04-12T08:00:47Z","snapshot_observed_at":"2026-08-14T20:23:58.130176Z","submitted_at":"2025-10-06T08:49:04Z","title":"SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-18T09:44:53.290259Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2510.04595"},"observation_digest":"sha256:1836d452bdbc21e4631dd8dfe4ba3494784f869b5ec6083c0945c9f9230889be","observation_id":"800aec82-b73b-4414-b839-fcd4fd96f25c","resolution":{"observed_at":"2026-05-18T09:46:12.467820Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2601.21503","last_updated":"2026-01-29T10:21:28Z","snapshot_observed_at":"2026-08-11T13:08:03.612802Z","submitted_at":"2026-01-29T10:21:28Z","title":"MAR: Efficient Large Language Models via Module-aware Architecture Refinement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-16T10:12:35.951089Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2601.21503"},"observation_digest":"sha256:c5628adc0ccbea1e257005451415bb121e5d714962088f7357c456aae960b62b","observation_id":"2fd88c32-d7b5-4ba0-bdaa-eeb02765e86c","resolution":{"observed_at":"2026-05-16T10:12:43.033292Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2604.14191","last_updated":"2026-04-01T09:23:08Z","snapshot_observed_at":"2026-08-17T23:27:38.968469Z","submitted_at":"2026-04-01T09:23:08Z","title":"Attention to Mamba: A Recipe for Cross-Architecture Distillation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-13T23:07:41.022051Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2604.14191"},"observation_digest":"sha256:ebd40e2ec0cc70bac9d3103adae4dbcecf6ae9c712788f7af021a22fb5d62e80","observation_id":"a9698443-5585-427f-a1f2-fe0528554420","resolution":{"observed_at":"2026-05-13T23:08:24.958595Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2604.24715","last_updated":"2026-04-27T17:23:37Z","snapshot_observed_at":"2026-08-15T05:13:53.597573Z","submitted_at":"2026-04-27T17:23:37Z","title":"Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T03:39:37.485602Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2604.24715"},"observation_digest":"sha256:06953102f226b928b9ac14ed6fc1d8abf1cf3d40dcc8794f993d10a0f3169336","observation_id":"a8ca6c25-0b43-4ad1-be26-b79114689e71","resolution":{"observed_at":"2026-05-11T22:01:10.875858Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2606.07604","last_updated":"2026-05-29T09:40:38Z","snapshot_observed_at":"2026-08-03T04:00:50.272221Z","submitted_at":"2026-05-29T09:40:38Z","title":"Contribution Weights: A Geometrical Analysis of Self-Attention Transformers","version":1},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-06-28T23:29:02.457697Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2606.07604"},"observation_digest":"sha256:5a29990df900f32772e82a8a03ee7ee9c19c6763b5ca77a86dd653c63a1a8b75","observation_id":"43ceba5a-1c6b-4f3d-ab50-5ab46c922b4a","resolution":{"observed_at":"2026-06-28T23:32:46.696626Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2606.30562","last_updated":"2026-06-29T17:02:34Z","snapshot_observed_at":"2026-08-14T09:50:47.227901Z","submitted_at":"2026-06-29T17:02:34Z","title":"Morphing into Hybrid Attention Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T05:56:51.447893Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2606.30562"},"observation_digest":"sha256:e21efa3e00c1c00f371370d7af5b549d8013ddb6ec566ab557203f073b6af5b3","observation_id":"fe18b65a-dc66-4d42-b0c0-5e322cdb8030","resolution":{"observed_at":"2026-06-30T08:44:27.964101Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":"2502.14458","doi":"10.48550/arxiv.2502.14458","metadata_source":"pith","pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458","venue":"cs.LG","work_id":"114086d3-6e40-4b5c-b2a9-6d0819e2bfac","year":2025},"citing_paper":{"arxiv_id":"2607.07706","last_updated":"2026-07-08T17:59:09Z","snapshot_observed_at":"2026-08-16T13:03:27.231988Z","submitted_at":"2026-07-08T17:59:09Z","title":"The Key to Going Linear: Analysis-Driven Transformer Linearization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-09T01:44:40.722957Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2607.07706"},"observation_digest":"sha256:f21bf713a581d8235c191924dd3deb2f7c196cc166395f03ef5089ee69505788","observation_id":"d39b3710-a3e0-4a4b-adc2-27a39eb5882b","resolution":{"observed_at":"2026-07-09T01:45:50.801202Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14458","snapshot_observed_at":"2026-08-01T02:44:00.629498Z","title":"Llamba: Scaling distilled recurrent models for efficient language processing.arXiv preprint arXiv:2502.14458, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25357","last_updated":"2026-07-28T07:04:32Z","snapshot_observed_at":"2026-08-14T19:14:38.532525Z","submitted_at":"2026-07-28T07:04:32Z","title":"Raven: High-Recall Sequence Modeling with Sparse Memory Routing","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-01T02:44:00.629498Z"},"links":{"cited_paper":"/paper/2502.14458","citing_paper":"/paper/2607.25357"},"observation_digest":"sha256:44bd2e28f0296c5f5dc4c0136a4237e71b75d6972a3971450340ea41874b4209","observation_id":"e6133297-6749-42d7-bb0d-11fb83897860","resolution":{"observed_at":"2026-08-01T02:44:00.629498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.14458/citation-record","integrity":"/paper/2502.14458/integrity","json":"/paper/2502.14458/citation-record.json","paper":"/paper/2502.14458"},"outbound":[],"paper":{"arxiv_id":"2502.14458","last_updated":"2025-02-23T13:02:09Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T12:56:42.870263Z","submitted_at":"2025-02-20T11:18:39Z","title":"Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.14458."}