{"as_of":"2026-08-12T09:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:71304e55736ab8a5d4fbfb8a3455e9a3d464545428528c61b0dc059121b4ff6d","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":"2026-08-12T06:34:41.77262+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":"2026-08-04T10:36:29.477444Z","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":"2026-07-11T03:07:52.453229Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":"2506.19935","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-07-11T03:07:52.453229Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.arXiv preprint arXiv:2506.19935","venue":"cs.LG","work_id":"de99a665-5830-4922-8d2c-f76aedabc7bf","year":2025},"citing_paper":{"arxiv_id":"2510.09885","last_updated":"2026-06-09T20:01:06Z","snapshot_observed_at":"2026-08-09T01:39:03.126187Z","submitted_at":"2025-10-10T21:43:50Z","title":"Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs","version":5},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-18T07:21:54.004830Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2510.09885"},"observation_digest":"sha256:9eec20fa12d3309bc116dc3f5475e6bd661925158ec6c9326fc5ce87b6fb680c","observation_id":"734d04c6-042d-4d57-a68e-bb3ba9071a35","resolution":{"observed_at":"2026-05-18T07:22:27.823087Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-08-04T10:36:29.477444Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.arXiv preprint arXiv:2506.19935,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.09885","last_updated":"2026-06-09T20:01:06Z","snapshot_observed_at":"2026-08-09T01:39:03.126187Z","submitted_at":"2025-10-10T21:43:50Z","title":"Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs","version":6},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T10:36:29.477444Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2510.09885"},"observation_digest":"sha256:81b0facc6c61cd45215f05776d46c8036302c04e4ac9c79eea1564ff0bcbbad1","observation_id":"4a42cd66-af08-4b4a-873c-86025b970d1d","resolution":{"observed_at":"2026-08-04T10:36:29.477444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":"2506.19935","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-07-11T03:07:52.453229Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.arXiv preprint arXiv:2506.19935","venue":"cs.LG","work_id":"de99a665-5830-4922-8d2c-f76aedabc7bf","year":2025},"citing_paper":{"arxiv_id":"2512.14067","last_updated":"2026-04-29T20:52:08Z","snapshot_observed_at":"2026-07-06T22:39:08.850289Z","submitted_at":"2025-12-16T04:12:17Z","title":"Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T22:29:08.669964Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2512.14067"},"observation_digest":"sha256:470a85f1e40624ed73137ff784aabf937e9fb7071551cfc3582dd98bfc743fed","observation_id":"62ca988f-1c48-488a-8d9a-474bcd3cd084","resolution":{"observed_at":"2026-05-16T22:31:19.340170Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":"2506.19935","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-07-11T03:07:52.453229Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.arXiv preprint arXiv:2506.19935","venue":"cs.LG","work_id":"de99a665-5830-4922-8d2c-f76aedabc7bf","year":2025},"citing_paper":{"arxiv_id":"2607.01775","last_updated":"2026-07-02T06:45:43Z","snapshot_observed_at":"2026-08-05T08:13:48.395073Z","submitted_at":"2026-07-02T06:45:43Z","title":"Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-07-03T17:30:39.458521Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2607.01775"},"observation_digest":"sha256:473d2fc02564b7e69034e8c81ef0e709bb3ca2dce3fa8d0fc20e401c2b4c8ba0","observation_id":"25e6e6de-18e3-4888-bf32-ccd25690653d","resolution":{"observed_at":"2026-07-03T17:38:43.670613Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":"2506.19935","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-07-11T03:07:52.453229Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.arXiv preprint arXiv:2506.19935","venue":"cs.LG","work_id":"de99a665-5830-4922-8d2c-f76aedabc7bf","year":2025},"citing_paper":{"arxiv_id":"2607.05722","last_updated":"2026-07-07T01:09:54Z","snapshot_observed_at":"2026-08-06T13:45:31.949136Z","submitted_at":"2026-07-07T01:09:54Z","title":"Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T03:04:12.500342Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2607.05722"},"observation_digest":"sha256:1eaa733f1e90d3abe9eb4429f8b10e34fcf1798e48957e054d41e0089d6b870f","observation_id":"fa9819f8-5cd2-4e2e-a43e-56c75313f540","resolution":{"observed_at":"2026-07-11T03:07:52.501391Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-08-02T05:17:47.735042Z","title":"Any-order gpt as masked diffusion model: Decoupling formulation and architecture.ArXiv preprint, abs/2506.19935,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13431","last_updated":"2026-07-15T04:23:22Z","snapshot_observed_at":"2026-08-07T01:50:04.940823Z","submitted_at":"2026-07-15T04:23:22Z","title":"Discrete Diffusion Models: A Unified Framework from Tokenization to Generation","version":1},"reference_index":186,"source":"pdf_text","source_observed_at":"2026-08-02T05:17:47.735042Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2607.13431"},"observation_digest":"sha256:ebd67c71d5ec46d3bd0dff219bc571aada77db1caf2dab6639ac2dddc8350ce2","observation_id":"9319a5fe-0146-44eb-99a0-0ed727a83987","resolution":{"observed_at":"2026-08-02T05:17:47.735042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.19935","snapshot_observed_at":"2026-08-01T20:54:15.037119Z","title":"arXiv preprint arXiv:2506.19935 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16528","last_updated":"2026-07-17T22:09:01Z","snapshot_observed_at":"2026-08-07T07:19:10.753414Z","submitted_at":"2026-07-17T22:09:01Z","title":"Hierarchical Domain Generalization","version":1},"reference_index":130,"source":"arxiv_source","source_observed_at":"2026-08-01T20:54:15.037119Z"},"links":{"cited_paper":"/paper/2506.19935","citing_paper":"/paper/2607.16528"},"observation_digest":"sha256:2ba8b25572d9e2fa1dc7b27c9845bbd9865d48c656cb4e2c8bf62f0e44ae48cc","observation_id":"564846fe-7fbb-417d-bb3f-e49b81286caf","resolution":{"observed_at":"2026-08-01T20:54:15.037119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.19935/citation-record","integrity":"/paper/2506.19935/integrity","json":"/paper/2506.19935/citation-record.json","paper":"/paper/2506.19935"},"outbound":[],"paper":{"arxiv_id":"2506.19935","last_updated":"2025-06-24T18:22:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T05:15:47.853248Z","submitted_at":"2025-06-24T18:22:25Z","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2506.19935."}