{"as_of":"2026-08-18T14:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7668647d948304cdf22c6803369e90fe576a01ead14c15e96204fba73e8ee13e","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:28:53.318991Z","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":"2026-07-04T09:19:43.459373Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.10450","last_updated":"2024-06-06T04:47:52Z","snapshot_observed_at":"2026-08-18T09:05:30.028260Z","submitted_at":"2024-02-16T04:55:09Z","title":"PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10450","snapshot_observed_at":"2026-08-16T11:28:53.318991Z","title":"Prise: Learning temporal action abstractions as a sequence compression problem,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15561","last_updated":"2025-04-22T03:30:38Z","snapshot_observed_at":"2026-08-18T09:25:13.425952Z","submitted_at":"2025-04-22T03:30:38Z","title":"SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:28:53.318991Z"},"links":{"cited_paper":"/paper/2402.10450","citing_paper":"/paper/2504.15561"},"observation_digest":"sha256:8e8813b2af810ecb1ccc16588dcd9e8f2c5ec2410798a492aa8f58ec81ada25b","observation_id":"c2418d1f-fa76-451b-9bde-e96480a15d79","resolution":{"observed_at":"2026-08-16T11:28:53.318991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10450","last_updated":"2024-06-06T04:47:52Z","snapshot_observed_at":"2026-08-18T09:05:30.028260Z","submitted_at":"2024-02-16T04:55:09Z","title":"PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10450","snapshot_observed_at":"2026-08-16T04:30:19.459769Z","title":"Zheng, C.-A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01179","last_updated":"2025-05-02T10:47:21Z","snapshot_observed_at":"2026-08-18T13:00:26.959464Z","submitted_at":"2025-05-02T10:47:21Z","title":"Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:30:19.459769Z"},"links":{"cited_paper":"/paper/2402.10450","citing_paper":"/paper/2505.01179"},"observation_digest":"sha256:65ce08193099a79828e937d9cc99f082a676434d87debfe946ec47871426217f","observation_id":"1d87bc01-57ad-4f6e-90af-0b01d55701ed","resolution":{"observed_at":"2026-08-16T04:30:19.459769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10450","last_updated":"2024-06-06T04:47:52Z","snapshot_observed_at":"2026-08-18T09:05:30.028260Z","submitted_at":"2024-02-16T04:55:09Z","title":"PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control","version":3},"cited_work":{"arxiv_id":"2402.10450","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.10450","snapshot_observed_at":"2026-07-04T09:19:43.459373Z","title":"Prise: Llm-style sequence compression for learning temporal action abstractions in control","venue":null,"work_id":"91f1d302-5057-4f52-b75a-4329b9776c33","year":2024},"citing_paper":{"arxiv_id":"2605.18597","last_updated":"2026-05-19T03:38:49Z","snapshot_observed_at":"2026-08-15T04:49:15.862375Z","submitted_at":"2026-05-18T16:07:44Z","title":"Latent Action Reparameterization for Efficient Agent Inference","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-20T10:45:20.306945Z"},"links":{"cited_paper":"/paper/2402.10450","citing_paper":"/paper/2605.18597"},"observation_digest":"sha256:1fabde36c9f40b3bcf211010078861e8f6416478acb5e26d9f76be1d3335a46c","observation_id":"78c0aef2-ad4e-47d9-a342-f6eb01ac21fe","resolution":{"observed_at":"2026-05-20T10:48:12.907631Z","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":"2402.10450","last_updated":"2024-06-06T04:47:52Z","snapshot_observed_at":"2026-08-18T09:05:30.028260Z","submitted_at":"2024-02-16T04:55:09Z","title":"PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control","version":3},"cited_work":{"arxiv_id":"2402.10450","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.10450","snapshot_observed_at":"2026-07-04T09:19:43.459373Z","title":"Prise: Llm-style sequence compression for learning temporal action abstractions in control","venue":null,"work_id":"91f1d302-5057-4f52-b75a-4329b9776c33","year":2024},"citing_paper":{"arxiv_id":"2606.22480","last_updated":"2026-06-21T12:49:46Z","snapshot_observed_at":"2026-08-08T11:39:41.619777Z","submitted_at":"2026-06-21T12:49:46Z","title":"ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T10:14:42.111428Z"},"links":{"cited_paper":"/paper/2402.10450","citing_paper":"/paper/2606.22480"},"observation_digest":"sha256:ee1b550d9896b9c86207fff987b93812982d50be92e044e907474c1df11b4851","observation_id":"61ec87bf-1abf-4403-9962-3624e42a60e2","resolution":{"observed_at":"2026-07-04T09:19:43.461390Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2402.10450/citation-record","integrity":"/paper/2402.10450/integrity","json":"/paper/2402.10450/citation-record.json","paper":"/paper/2402.10450"},"outbound":[],"paper":{"arxiv_id":"2402.10450","last_updated":"2024-06-06T04:47:52Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T09:05:30.028260Z","submitted_at":"2024-02-16T04:55:09Z","title":"PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control"},"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 4 inbound Pith citation observations for arXiv:2402.10450."}