{"as_of":"2026-08-18T10:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7f204e1786c5271db2a63ccb0d75153d0694c572d96b9d9916ae29a515782fdd","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T17:42:40.539599Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T00:33:43.887384Z","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":"2026-08-07T22:39:55.318647Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11967","snapshot_observed_at":"2026-08-10T00:33:43.887384Z","title":"A hybrid attention framework for fake news detection with large language models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2501.18154","last_updated":"2025-01-30T05:39:01Z","snapshot_observed_at":"2026-08-16T17:19:44.390960Z","submitted_at":"2025-01-30T05:39:01Z","title":"Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T00:33:43.887384Z"},"links":{"cited_paper":"/paper/2501.11967","citing_paper":"/paper/2501.18154"},"observation_digest":"sha256:bede82cfa628f72d2330b769f3cb8d419c48db79f09e4b8d34ad44cd66069ff7","observation_id":"bf162bd8-4f4c-4750-8726-b7815e6ba43b","resolution":{"observed_at":"2026-08-10T00:33:43.887384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"cited_work":{"arxiv_id":"2501.11967","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.11967","snapshot_observed_at":"2026-08-07T22:39:55.318647Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","venue":"cs.CL","work_id":"ba1b3c24-c54e-453f-9a79-650b8b50a93f","year":2025},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-14T01:56:10.598971Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.081095Z"},"links":{"cited_paper":"/paper/2501.11967","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:695b4e54b78fae16f1fce19eb81ef2c263c7ce0e07e0e55749cb38a90ee1f35b","observation_id":"dd34e57d-c717-4e8b-8aaf-b686a0835bac","resolution":{"observed_at":"2026-08-07T22:39:55.323497Z","resolver_source":"local_arxiv","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/2501.11967/citation-record","integrity":"/paper/2501.11967/integrity","json":"/paper/2501.11967/citation-record.json","paper":"/paper/2501.11967"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.740095Z","title":null,"venue":null,"work_id":"4e3c043f-5226-42ce-a337-b9cc2e56f311","year":2019},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.482151Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:5224c3d76c511ffc2c6b555f70f990febc61c2326c372dd1fb6dbf428cfc7d28","observation_id":"e3e05c2c-593a-48b7-9fdb-0f71978e62a1","resolution":{"observed_at":"2026-08-10T17:42:40.745605Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.724540Z","title":"Zhou and R","venue":null,"work_id":"18d97227-c341-481b-b65d-aae370346f71","year":2020},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.487779Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:b1ca540f03683041f6d422f9ef3e8eba3e38e1943e63090df4614165b17a9695","observation_id":"b9f9c11d-15d7-4d44-9089-23ceac22efa0","resolution":{"observed_at":"2026-08-10T17:42:40.729272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.708652Z","title":null,"venue":null,"work_id":"e44cb9c6-b8e6-4a98-960d-f302ea833b68","year":2024},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.492742Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:b0d569100013a16be3d25f3fab69472b4e65c5fd0a43f5b48c77aea7c61bdf69","observation_id":"e5b71bea-f663-4588-acc9-95679b7d11b3","resolution":{"observed_at":"2026-08-10T17:42:40.713684Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-10T17:42:40.504211Z","title":"Liu, ``Roberta: A robustly optimized bert pretraining approach,'' arXiv preprint arXiv:1907.11692, vol","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.504211Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:6c15285e6f48058a8937cbbbef809c8cee219f9b9f52f730f1de5b3a17d1d214","observation_id":"61280261-55a2-4d6c-be5a-4c3d21281c42","resolution":{"observed_at":"2026-08-10T17:42:40.504211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.691574Z","title":null,"venue":null,"work_id":"9b0cb136-80d6-448e-ab46-a7f0363fb5f8","year":2016},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.509608Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:a8122ef63f46b64ef791fb1961bd4da0f01475f011d813b36fe04fd29d8b2f4b","observation_id":"bb301e1c-5aa8-490d-a816-9a3368ad7973","resolution":{"observed_at":"2026-08-10T17:42:40.696924Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.674898Z","title":"Holstein, B","venue":null,"work_id":"985c61fd-22ef-4c2b-afa6-1491d5e7f7a4","year":2018},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.515369Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:75c53cbd20ab2cd1dadd9a022f19c97230ddb42fa1ed7a0e61a3343246ad0063","observation_id":"e34b4f6c-1ace-4c8e-9da2-b36f4727faf0","resolution":{"observed_at":"2026-08-10T17:42:40.679849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.658987Z","title":"Reich and J","venue":null,"work_id":"56f1c86b-9a5b-4721-820b-d0489e1383eb","year":2019},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.520373Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:523b9d229abe979fdd042d6f82d9e04ed1f9dae8211763280fdfcb3cc51c2452","observation_id":"1a3a360b-b4bc-4dd8-902c-b6c1ae87fa1c","resolution":{"observed_at":"2026-08-10T17:42:40.664008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.642809Z","title":"Chen and C","venue":null,"work_id":"073f2546-427f-4c14-882f-e0a75a380029","year":2016},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.525202Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:5af8ba178a319792c065fe0569e4a42044185f62272245a6736a8f503a9adaa2","observation_id":"d484681b-199b-4c3e-83ad-cd172e725aa9","resolution":{"observed_at":"2026-08-10T17:42:40.647937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.626700Z","title":null,"venue":null,"work_id":"d0ee7111-48ed-4423-b197-9a7402b58b3c","year":2021},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.530062Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:04b0537b6d806f7e864cdcb9b7c8ff58157dac56995186ed15a180c392989c89","observation_id":"42bbbd37-8c5b-4889-ada0-46285632aa49","resolution":{"observed_at":"2026-08-10T17:42:40.631581Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:42:40.607576Z","title":"Jiang and Z","venue":null,"work_id":"2ee0c8ef-4077-4af5-890a-f19f7eb586b5","year":2020},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.534791Z"},"links":{"citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:9a555c7ae0daf53568749ced5929e1b9d7356093afda4bcac277e22fc6afe99f","observation_id":"616edbca-c8b5-4cef-94fe-c90440d16dca","resolution":{"observed_at":"2026-08-10T17:42:40.614892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-10T17:42:40.539599Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T17:42:40.539599Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2501.11967"},"observation_digest":"sha256:ea8248ab3044d77232931f7e204eb2f0ccf5fbd1bce5e4b8327e2efa47a99f16","observation_id":"359dfaa5-5c61-4dc4-a943-f907130857e8","resolution":{"observed_at":"2026-08-10T17:42:40.539599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-18T01:21:55.361378Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":11},"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 11 of 11 outbound references and 2 inbound Pith citation observations for arXiv:2501.11967."}