{"as_of":"2026-08-10T20:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8e95f42b8c4863f2b74c8e5ae4fd4586d3e91f916cd5fa4e99f6d7b1e21f5cf","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:49:10.708258Z","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":"2026-07-04T13:39:51.098907Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-08-07T13:49:10.708258Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20888","last_updated":"2025-06-27T07:59:43Z","snapshot_observed_at":"2026-08-07T13:42:55.596808Z","submitted_at":"2025-05-27T08:32:51Z","title":"EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T13:49:10.708258Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2505.20888"},"observation_digest":"sha256:570e9aac2b32448b2bec987830d5d2ce3260e96bcc2f1b93e6ef6be4d766ed19","observation_id":"2ed727d5-12e6-49e6-92af-818187f58d14","resolution":{"observed_at":"2026-08-07T13:49:10.708258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-08-06T17:53:42.595083Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09662","last_updated":"2025-07-13T14:51:59Z","snapshot_observed_at":"2026-08-07T01:15:50.475193Z","submitted_at":"2025-07-13T14:51:59Z","title":"Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T17:53:42.595083Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2507.09662"},"observation_digest":"sha256:720cedf514826c9da93c3c17f4795c0a75f476eeb9cd8aa561d74c01a44f156f","observation_id":"09dbf26a-3bd2-49b6-ab64-a4838098c80e","resolution":{"observed_at":"2026-08-06T17:53:42.595083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":"2505.10937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-07-04T13:39:51.098907Z","title":"Reasoning with omnithought: A large cot dataset with verbosity and cognitive difficulty annotations","venue":null,"work_id":"68e06b40-ac53-4de0-a3e4-9edfbe3cca81","year":2025},"citing_paper":{"arxiv_id":"2604.10480","last_updated":"2026-04-12T06:24:07Z","snapshot_observed_at":"2026-07-06T22:59:05.519456Z","submitted_at":"2026-04-12T06:24:07Z","title":"Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T16:14:38.371700Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2604.10480"},"observation_digest":"sha256:ac733368bd12198cf0970d4965fdc2c4b2da73cbec4635177ccd772a18a3bad5","observation_id":"30f5debd-16f7-48e5-b6fe-258d82b8d823","resolution":{"observed_at":"2026-05-11T09:06:00.618733Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":"2505.10937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-07-04T13:39:51.098907Z","title":"Reasoning with omnithought: A large cot dataset with verbosity and cognitive difficulty annotations","venue":null,"work_id":"68e06b40-ac53-4de0-a3e4-9edfbe3cca81","year":2025},"citing_paper":{"arxiv_id":"2605.11629","last_updated":"2026-05-12T06:54:57Z","snapshot_observed_at":"2026-08-02T15:55:41.442659Z","submitted_at":"2026-05-12T06:54:57Z","title":"OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-13T01:25:14.540277Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2605.11629"},"observation_digest":"sha256:16b3a3fcf5619704f4c741be06fc3d00637cba9ed8ffb07815354c67f53c9b28","observation_id":"cbef96ba-ebea-488a-9800-9c8636338435","resolution":{"observed_at":"2026-05-13T01:27:01.893709Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":"2505.10937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-07-04T13:39:51.098907Z","title":"Reasoning with omnithought: A large cot dataset with verbosity and cognitive difficulty annotations","venue":null,"work_id":"68e06b40-ac53-4de0-a3e4-9edfbe3cca81","year":2025},"citing_paper":{"arxiv_id":"2606.01249","last_updated":"2026-06-17T04:44:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-31T14:04:51Z","title":"Trust Region On-Policy Distillation","version":3},"reference_index":294,"source":"arxiv_source","source_observed_at":"2026-06-28T17:38:50.313305Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2606.01249"},"observation_digest":"sha256:55743428c6c942559f6dc66c3582b56cd33ac7977d1f956cd0d8cfc56e9decad","observation_id":"be277dde-7d56-4e1c-b4fc-dcd75d03cff5","resolution":{"observed_at":"2026-07-01T20:56:13.700727Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations","version":1},"cited_work":{"arxiv_id":"2505.10937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.10937","snapshot_observed_at":"2026-07-04T13:39:51.098907Z","title":"Reasoning with omnithought: A large cot dataset with verbosity and cognitive difficulty annotations","venue":null,"work_id":"68e06b40-ac53-4de0-a3e4-9edfbe3cca81","year":2025},"citing_paper":{"arxiv_id":"2606.26671","last_updated":"2026-06-25T07:03:25Z","snapshot_observed_at":"2026-08-08T21:58:25.958687Z","submitted_at":"2026-06-25T07:03:25Z","title":"NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T05:00:23.510590Z"},"links":{"cited_paper":"/paper/2505.10937","citing_paper":"/paper/2606.26671"},"observation_digest":"sha256:fb0a9239f712bb0f59104c8a17ec5ea640c3ef889509b7adc4545ca7d29757aa","observation_id":"91e1d022-2a47-4ae6-955b-726c99dcdeed","resolution":{"observed_at":"2026-07-04T13:39:51.100542Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.10937/citation-record","integrity":"/paper/2505.10937/integrity","json":"/paper/2505.10937/citation-record.json","paper":"/paper/2505.10937"},"outbound":[],"paper":{"arxiv_id":"2505.10937","last_updated":"2025-05-16T07:15:30Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T15:44:49.004743Z","submitted_at":"2025-05-16T07:15:30Z","title":"Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2505.10937."}