{"as_of":"2026-08-08T11:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:472e0fe65820e1a1dd34dbc20553d0a25e7d4eed4a36173fda778aaf6f030515","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:47:53.635173Z","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":"2026-06-29T18:13:49.137814Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-08-06T18:47:53.635173Z","title":"& You, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.07421","last_updated":"2025-07-10T04:31:01Z","snapshot_observed_at":"2026-08-08T07:57:00.707301Z","submitted_at":"2025-07-10T04:31:01Z","title":"SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T18:47:53.635173Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2507.07421"},"observation_digest":"sha256:dc9a97ddc9318e6d4a481c85c0986f17408e6888bb95ab3cbe7722d80e37f54a","observation_id":"b3b7a993-388b-4473-bbb2-ac1b8c2be770","resolution":{"observed_at":"2026-08-06T18:47:53.635173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":"2505.13508","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-06-29T18:13:49.137814Z","title":"Time-r1: Towards comprehensive temporal reasoning in llms","venue":null,"work_id":"40bad83e-bb5e-44a9-8aa8-dd4ac9417265","year":2025},"citing_paper":{"arxiv_id":"2509.05489","last_updated":"2026-04-16T20:13:16Z","snapshot_observed_at":"2026-07-06T22:24:51.648354Z","submitted_at":"2025-09-05T20:39:43Z","title":"Self-Aligned Reward: Towards Effective and Efficient Reasoners","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-18T18:27:23.076544Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2509.05489"},"observation_digest":"sha256:cd96cc1bf95ab4333f16f59203df03c02c9906030694a0b62e62dac129965354","observation_id":"92990183-3bcd-4f6b-ba83-a8aba7472d2c","resolution":{"observed_at":"2026-05-18T18:31:44.459695Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-08-04T13:54:16.406981Z","title":", Han , P","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24563","last_updated":"2026-07-15T12:54:15Z","snapshot_observed_at":"2026-08-05T23:40:26.873053Z","submitted_at":"2025-09-29T10:16:05Z","title":"NeMo: Needle in a Montage for Video-Language Understanding","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:16.406981Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2509.24563"},"observation_digest":"sha256:d8a34e6e48e63570a89d1c873d2763dee120a13aa114b0af89f33f0177a488df","observation_id":"0754fe08-0436-4776-8cc8-44b7bc4c06ed","resolution":{"observed_at":"2026-08-04T13:54:16.406981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-08-03T03:12:06.372058Z","title":"Jun Liu, Chaoyun Zhang, Jiaxu Qian, Minghua Ma, Si Qin, Chetan Bansal, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.08868","last_updated":"2026-05-31T17:53:00Z","snapshot_observed_at":"2026-08-07T02:44:19.674675Z","submitted_at":"2026-02-09T16:30:13Z","title":"AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T03:12:06.372058Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2602.08868"},"observation_digest":"sha256:1ece333623e0a14c49c105e3a576a05181fd0edcb0d4c9933d6b67a9ec514bda","observation_id":"aaa00cd6-5240-4417-bc9d-7c8c7cb1ee0b","resolution":{"observed_at":"2026-08-03T03:12:06.372058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":"2505.13508","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-06-29T18:13:49.137814Z","title":"Time-r1: Towards comprehensive temporal reasoning in llms","venue":null,"work_id":"40bad83e-bb5e-44a9-8aa8-dd4ac9417265","year":2025},"citing_paper":{"arxiv_id":"2604.18576","last_updated":"2026-07-12T18:34:47Z","snapshot_observed_at":"2026-07-16T23:18:42.403825Z","submitted_at":"2026-04-20T17:57:51Z","title":"Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-10T04:14:17.395227Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2604.18576"},"observation_digest":"sha256:810a53eac3add81cc968a04b92d20387d6da3ea58c0e7e9be88745ae5ba28e58","observation_id":"39659f3b-8ecd-4068-8bbf-e2a59a86216c","resolution":{"observed_at":"2026-05-11T12:06:01.497920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-07-14T19:33:20.775008Z","title":"Time-R1 : Towards comprehensive temporal reasoning in LLMs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.18576","last_updated":"2026-07-12T18:34:47Z","snapshot_observed_at":"2026-07-16T23:18:42.403825Z","submitted_at":"2026-04-20T17:57:51Z","title":"Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-07-14T19:33:20.775008Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2604.18576"},"observation_digest":"sha256:3fbe0112eb819f0983ee9679e2431e91f303bffd9c7c6fcd426b6ec58ef17aec","observation_id":"a4067175-b618-44f7-b6b5-505a4cc3eec4","resolution":{"observed_at":"2026-07-14T19:33:20.775008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":"2505.13508","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-06-29T18:13:49.137814Z","title":"Time-r1: Towards comprehensive temporal reasoning in llms","venue":null,"work_id":"40bad83e-bb5e-44a9-8aa8-dd4ac9417265","year":2025},"citing_paper":{"arxiv_id":"2604.23051","last_updated":"2026-04-24T22:44:21Z","snapshot_observed_at":"2026-07-06T23:09:19.041332Z","submitted_at":"2026-04-24T22:44:21Z","title":"Evaluating Temporal Consistency in Multi-Turn Language Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-08T11:37:10.330674Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2604.23051"},"observation_digest":"sha256:696ccc33f7ca5077a30501feff39ed62e12c2a5a2eb56df77edd98cc50beb6ed","observation_id":"67369dd0-ffe6-482d-9450-7b623277c354","resolution":{"observed_at":"2026-05-11T19:36:12.948453Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":"2505.13508","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-06-29T18:13:49.137814Z","title":"Time-r1: Towards comprehensive temporal reasoning in llms","venue":null,"work_id":"40bad83e-bb5e-44a9-8aa8-dd4ac9417265","year":2025},"citing_paper":{"arxiv_id":"2605.03762","last_updated":"2026-05-05T13:50:50Z","snapshot_observed_at":"2026-07-06T23:16:40.201701Z","submitted_at":"2026-05-05T13:50:50Z","title":"OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T16:29:51.187292Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2605.03762"},"observation_digest":"sha256:50f012f4f44a366f298bba2baf01b0d3d919cb1b081e0b4cb9e6fd7160c6be8a","observation_id":"2aa2c1b5-f1eb-4b78-8bfb-790b96f0ea52","resolution":{"observed_at":"2026-05-11T23:41:16.866553Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":"2505.13508","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-06-29T18:13:49.137814Z","title":"Time-r1: Towards comprehensive temporal reasoning in llms","venue":null,"work_id":"40bad83e-bb5e-44a9-8aa8-dd4ac9417265","year":2025},"citing_paper":{"arxiv_id":"2605.27066","last_updated":"2026-05-26T14:16:27Z","snapshot_observed_at":"2026-08-02T08:50:22.873323Z","submitted_at":"2026-05-26T14:16:27Z","title":"Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T18:05:06.382333Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2605.27066"},"observation_digest":"sha256:c32b90367b7cedc29a5f07aadc36b365762a86077c248fd3314c19a0dc2c12cd","observation_id":"88377007-8272-4ce7-be2c-7c38aa848f2d","resolution":{"observed_at":"2026-06-29T18:13:49.139704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-07-11T19:34:49.358453Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.04383","last_updated":"2026-07-29T12:04:35Z","snapshot_observed_at":"2026-08-07T00:50:01.916500Z","submitted_at":"2026-07-05T16:22:14Z","title":"Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-11T19:34:49.358453Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2607.04383"},"observation_digest":"sha256:4eafdf31d4c9ef28f47734dae6d9e2d3e2f7f8878dccbccc28931d8e5c4e0fa6","observation_id":"fd587128-9dee-4641-812e-8d0a53abd11f","resolution":{"observed_at":"2026-07-11T19:34:49.358453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13508","snapshot_observed_at":"2026-08-02T08:43:34.967740Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.04383","last_updated":"2026-07-29T12:04:35Z","snapshot_observed_at":"2026-08-07T00:50:01.916500Z","submitted_at":"2026-07-05T16:22:14Z","title":"Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-02T08:43:34.967740Z"},"links":{"cited_paper":"/paper/2505.13508","citing_paper":"/paper/2607.04383"},"observation_digest":"sha256:8502811ce5538f4cd6ccdbc90ef5ffb2a0f33d40982106de2c65b0d2e24f3022","observation_id":"52719cf2-9811-4163-9e29-f0c614db0851","resolution":{"observed_at":"2026-08-02T08:43:34.967740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.13508/citation-record","integrity":"/paper/2505.13508/integrity","json":"/paper/2505.13508/citation-record.json","paper":"/paper/2505.13508"},"outbound":[],"paper":{"arxiv_id":"2505.13508","last_updated":"2025-06-03T05:30:14Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T15:44:39.854986Z","submitted_at":"2025-05-16T13:46:28Z","title":"Time-R1: Towards Comprehensive Temporal Reasoning in LLMs"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2505.13508."}