{"as_of":"2026-08-08T05:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c790b233c6acc4663b00fe867158f2786f4de6e710a313b090ab423daad8f9e","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:42:13.128277Z","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":"2026-05-20T06:43:05.908126Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":"2504.21659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2504.21659 , year=","venue":null,"work_id":"b860715b-a29f-4b3f-88c6-a4c7e33d5b1d","year":2025},"citing_paper":{"arxiv_id":"2503.16419","last_updated":"2025-08-21T19:14:40Z","snapshot_observed_at":"2026-08-07T04:27:23.738927Z","submitted_at":"2025-03-20T17:59:38Z","title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","version":4},"reference_index":126,"source":"pdf_text","source_observed_at":"2026-05-14T01:29:56.480020Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2503.16419"},"observation_digest":"sha256:e55a6cdcf1593970a505df43556fad3257362195af194f4ee7819fb4cd09ef77","observation_id":"5ee1de2d-fb97-4afc-95a1-da8bec5a4802","resolution":{"observed_at":"2026-05-14T01:29:57.541153Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-08-07T14:42:13.128277Z","title":"Adar1: From long-cot to hybrid-cot via bi-level adaptive reasoning optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17941","last_updated":"2025-05-23T14:17:56Z","snapshot_observed_at":"2026-08-07T21:18:55.260268Z","submitted_at":"2025-05-23T14:17:56Z","title":"VeriThinker: Learning to Verify Makes Reasoning Model Efficient","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:42:13.128277Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2505.17941"},"observation_digest":"sha256:b92da5e6ca5f127bdd87400da72658829d0739b8f1d6952e0369c39ca63e5c3c","observation_id":"a9e77dd1-3aa5-4f47-b355-91ecccc2515c","resolution":{"observed_at":"2026-08-07T14:42:13.128277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-08-07T14:12:21.637161Z","title":"Adar1: From long-cot to hybrid-cot via bi-level adaptive reasoning optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.19716","last_updated":"2025-05-26T09:04:44Z","snapshot_observed_at":"2026-08-07T14:05:21.709761Z","submitted_at":"2025-05-26T09:04:44Z","title":"Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:12:21.637161Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2505.19716"},"observation_digest":"sha256:bc0ec3297dd776e37be117a21e9a9646b4a06e77d469554918b665b7d7fbf3c2","observation_id":"8b8a7f06-fb79-4d11-822e-3dbd180aeaee","resolution":{"observed_at":"2026-08-07T14:12:21.637161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-08-07T00:16:35.851356Z","title":"Adar1: From long-cot to hybrid-cot via bi-level adaptive reasoning optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14755","last_updated":"2025-09-11T02:13:24Z","snapshot_observed_at":"2026-08-07T00:07:24.304900Z","submitted_at":"2025-06-17T17:50:16Z","title":"Optimizing Length Compression in Large Reasoning Models","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T00:16:35.851356Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2506.14755"},"observation_digest":"sha256:bdf2b8c7cb8c0ef839c84760da0ca6a94a6b85d848a76db3354ce8db4922ea1d","observation_id":"1e88d015-97c9-42d1-abe1-132fa0ae08f6","resolution":{"observed_at":"2026-08-07T00:16:35.851356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-08-06T19:56:10.895596Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04348","last_updated":"2025-07-17T13:07:41Z","snapshot_observed_at":"2026-08-06T19:47:23.963088Z","submitted_at":"2025-07-06T11:21:47Z","title":"SmartThinker: Learning to Compress and Preserve Reasoning by Step-Level Length Control","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T19:56:10.895596Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2507.04348"},"observation_digest":"sha256:d7c6fca6df72199c1836815d9932bbda6a7acebda154f126b12a1cf31d500926","observation_id":"00c8ce7c-3dd3-42e8-b0ed-539d688e4a89","resolution":{"observed_at":"2026-08-06T19:56:10.895596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-08-06T17:54:17.403889Z","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":124,"source":"arxiv_source","source_observed_at":"2026-08-06T17:54:17.403889Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2507.09662"},"observation_digest":"sha256:2a1336ddb564c21d926ee67adf6277124e8b364b39a56a215632fa2bba1e53aa","observation_id":"ec397cf8-4ff5-4eb7-a38a-e745357bfe37","resolution":{"observed_at":"2026-08-06T17:54:17.403889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":"2504.21659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2504.21659 , year=","venue":null,"work_id":"b860715b-a29f-4b3f-88c6-a4c7e33d5b1d","year":2025},"citing_paper":{"arxiv_id":"2605.06165","last_updated":"2026-05-07T12:51:49Z","snapshot_observed_at":"2026-07-06T23:18:41.400741Z","submitted_at":"2026-05-07T12:51:49Z","title":"Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost","version":1},"reference_index":228,"source":"arxiv_source","source_observed_at":"2026-05-08T10:19:08.451445Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2605.06165"},"observation_digest":"sha256:a4ff22e0dca81200433b385d4d611ca80dab8da10457d9400f635e3533d11041","observation_id":"654af3aa-6f2c-41fa-996d-4700f77a7272","resolution":{"observed_at":"2026-05-11T20:06:10.012852Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization","version":2},"cited_work":{"arxiv_id":"2504.21659","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.21659","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2504.21659 , year=","venue":null,"work_id":"b860715b-a29f-4b3f-88c6-a4c7e33d5b1d","year":2025},"citing_paper":{"arxiv_id":"2605.19358","last_updated":"2026-05-19T04:41:51Z","snapshot_observed_at":"2026-07-06T23:30:06.876413Z","submitted_at":"2026-05-19T04:41:51Z","title":"Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-20T06:40:06.103206Z"},"links":{"cited_paper":"/paper/2504.21659","citing_paper":"/paper/2605.19358"},"observation_digest":"sha256:3f5abddc389baa56f3a2ebd4eca0c42f74eee379453db416804c6dc05b04b037","observation_id":"534beaef-e6af-40f5-af47-b2c56d89a322","resolution":{"observed_at":"2026-05-20T06:43:05.910044Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.21659/citation-record","integrity":"/paper/2504.21659/integrity","json":"/paper/2504.21659/citation-record.json","paper":"/paper/2504.21659"},"outbound":[],"paper":{"arxiv_id":"2504.21659","last_updated":"2025-05-21T11:59:38Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T15:57:54.877503Z","submitted_at":"2025-04-30T14:01:45Z","title":"Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2504.21659."}