{"as_of":"2026-08-10T20:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:166a2d6f575cc98955fa415543239145fc7a152e545bfc9f02d2d876f3021033","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T23:22:39.222267Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T07:09:30.417971Z","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":"2026-06-29T18:43:50.534673Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"cited_work":{"arxiv_id":"2602.14161","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.14161","snapshot_observed_at":"2026-07-21T02:20:35.156854Z","title":"When benchmarks lie: Evaluating malicious prompt classifiers under true distribu- tion shift","venue":null,"work_id":"3db61a52-da0d-460f-8848-d7b07c35f945","year":2026},"citing_paper":{"arxiv_id":"2605.20241","last_updated":"2026-05-18T00:12:24Z","snapshot_observed_at":"2026-07-06T23:30:53.900592Z","submitted_at":"2026-05-18T00:12:24Z","title":"Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T08:56:10.736240Z"},"links":{"cited_paper":"/paper/2602.14161","citing_paper":"/paper/2605.20241"},"observation_digest":"sha256:9067aea97a489e290c7242c45e1fdce5823dddb9f3224dfa88b6e379c26e99c4","observation_id":"aca06d7a-b362-4148-8dfd-1c1afc97d3e2","resolution":{"observed_at":"2026-07-21T02:20:35.156854Z","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":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"cited_work":{"arxiv_id":"2602.14161","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.14161","snapshot_observed_at":"2026-07-21T02:20:35.156854Z","title":"When benchmarks lie: Evaluating malicious prompt classifiers under true distribu- tion shift","venue":null,"work_id":"3db61a52-da0d-460f-8848-d7b07c35f945","year":2026},"citing_paper":{"arxiv_id":"2605.26999","last_updated":"2026-05-26T13:19:25Z","snapshot_observed_at":"2026-08-08T09:09:26.193483Z","submitted_at":"2026-05-26T13:19:25Z","title":"Prompt Injection Detection is Regime-Dependent: A Deployment-Aware Evaluation with Interpretable Structural Signals","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T18:41:03.566306Z"},"links":{"cited_paper":"/paper/2602.14161","citing_paper":"/paper/2605.26999"},"observation_digest":"sha256:39b13d3951c16b7607b803d509bc0169cf23b79605b7440d2917f8656df7db3e","observation_id":"a5e3e63c-772b-48c1-9df4-4a376a89965b","resolution":{"observed_at":"2026-07-21T02:20:35.156854Z","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":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.14161","snapshot_observed_at":"2026-08-02T07:09:30.417971Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.13075","last_updated":"2026-07-12T20:37:19Z","snapshot_observed_at":"2026-08-09T10:59:34.877020Z","submitted_at":"2026-07-12T20:37:19Z","title":"The Entanglement Wall: Activation-Space Probes as Risk Detectors, Not Context Adjudicators","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-02T07:09:30.417971Z"},"links":{"cited_paper":"/paper/2602.14161","citing_paper":"/paper/2607.13075"},"observation_digest":"sha256:9087fdfaa664fc1e23269f6d14e08d13383348a18123d12104923bcc3a906549","observation_id":"d797654d-db01-467c-bf4d-d05a795ca3ab","resolution":{"observed_at":"2026-08-02T07:09:30.417971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2602.14161/citation-record","integrity":"/paper/2602.14161/integrity","json":"/paper/2602.14161/citation-record.json","paper":"/paper/2602.14161"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:38.431988Z","title":"Unveiling decision-making in LLMs for text classification: Extraction of influential and interpretable concepts with sparse autoencoders.arXiv preprint arXiv:2506.23951,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.431988Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:a49332d1cbe16001772cb5c15d57ec93f6c2511f29dc7854aa348f5825dfce20","observation_id":"e76383d9-2896-4b36-8446-a6c8bea3a9e1","resolution":{"observed_at":"2026-08-02T23:22:38.431988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05147","last_updated":"2024-08-19T07:51:05Z","snapshot_observed_at":"2026-08-04T11:44:14.524984Z","submitted_at":"2024-08-09T16:06:42Z","title":"Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05147","snapshot_observed_at":"2026-08-02T23:22:38.478153Z","title":"Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.arXiv preprint arXiv:2408.05147,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.478153Z"},"links":{"cited_paper":"/paper/2408.05147","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:a9fdd801d79f61c1a3d29e515155b3f8fe39516b12aa8a307ddf31e7e46e0aab","observation_id":"92470775-d594-4e06-8be6-992f70fb6e7e","resolution":{"observed_at":"2026-08-02T23:22:38.478153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04249","last_updated":"2024-02-27T04:43:08Z","snapshot_observed_at":"2026-07-06T17:26:23.067923Z","submitted_at":"2024-02-06T18:59:08Z","title":"HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04249","snapshot_observed_at":"2026-08-02T23:22:38.615073Z","title":"HarmBench: A standardized evaluation framework for automated red teaming and robust refusal.arXiv preprint arXiv:2402.04249,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.615073Z"},"links":{"cited_paper":"/paper/2402.04249","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:65d61c9725ba1996b11719e7528e8798761f8921bdbde1453d5a4a7a93d7499b","observation_id":"b12eca75-ceb7-4ca5-9602-10cae427576a","resolution":{"observed_at":"2026-08-02T23:22:38.615073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18837","last_updated":"2025-01-31T01:09:32Z","snapshot_observed_at":"2026-08-08T23:58:18.293467Z","submitted_at":"2025-01-31T01:09:32Z","title":"Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18837","snapshot_observed_at":"2026-08-02T23:22:38.767419Z","title":"Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.767419Z"},"links":{"cited_paper":"/paper/2501.18837","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:bebf191108e4ac0f9d517cb1f27e2dc99cc8e4cbc230d5fa7f6f34d51c26296d","observation_id":"ab54ac8e-75a1-4d4a-899d-0e9ddf54c45d","resolution":{"observed_at":"2026-08-02T23:22:38.767419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14197","last_updated":"2025-01-27T08:51:16Z","snapshot_observed_at":"2026-07-06T17:06:45.522731Z","submitted_at":"2023-12-21T01:08:39Z","title":"Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.14197","snapshot_observed_at":"2026-08-02T23:22:38.844788Z","title":"Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.844788Z"},"links":{"cited_paper":"/paper/2312.14197","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:c02062cfbfc04dd9d580f69f2c3bd85f7462ca100db87b44c278a6cd62bd44a5","observation_id":"538c7225-0d0a-4c6b-ac48-d2634878d9c3","resolution":{"observed_at":"2026-08-02T23:22:38.844788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02160","last_updated":"2023-05-03T14:48:27Z","snapshot_observed_at":"2026-07-06T15:22:47.702437Z","submitted_at":"2023-05-03T14:48:27Z","title":"Explaining Language Models' Predictions with High-Impact Concepts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02160","snapshot_observed_at":"2026-08-02T23:22:38.945796Z","title":"Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.945796Z"},"links":{"cited_paper":"/paper/2305.02160","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:bf8fd1d30d24df4ec1f8f37a616f215b1a413eddef6e56ea86596ebc7e9c7eb2","observation_id":"68e4120d-125c-4479-88e5-69c031c421b3","resolution":{"observed_at":"2026-08-02T23:22:38.945796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04313","last_updated":"2024-07-12T16:51:07Z","snapshot_observed_at":"2026-08-10T19:22:05.434817Z","submitted_at":"2024-06-06T17:57:04Z","title":"Improving Alignment and Robustness with Circuit Breakers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04313","snapshot_observed_at":"2026-08-02T23:22:39.082604Z","title":"12 APPENDIX A DATASETDETAILS Table 6: Dataset overview","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:39.082604Z"},"links":{"cited_paper":"/paper/2406.04313","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:89994d264ce0de078fda1f37155be155cc73eb3e22ec14044e8309afa77243e6","observation_id":"eb5e9b94-fdc5-4e7c-997e-9570e765d67c","resolution":{"observed_at":"2026-08-02T23:22:39.082604Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:39.136959Z","title":"Subject: {subject}Body:{body}","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:39.136959Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:72c65e1323cd292e8a2362a60507caece80069b73af216f6639e2341dea9dc6e","observation_id":"eff26982-06f1-4e0f-9a47-8496a3d756c8","resolution":{"observed_at":"2026-08-02T23:22:39.136959Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:39.222267Z","title":"system” role, so we prepend system message content to the first user message. The model generates a classification (“safe","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:39.222267Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:bd3eaaabad282062b2adb3aa58e704f5053b8c046ead74c0424e2db1ee6c8192","observation_id":"76f18ec2-a151-4a22-890f-add5dfdf4f43","resolution":{"observed_at":"2026-08-02T23:22:39.222267Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:38.703390Z","title":"Baturay Saglam, Paul Kassianik, Blaine Nelson, Sajana Weerawardhena, Yaron Singer, and Amin Kar- basi","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.703390Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:c67b23e0ce493992a85f778f1c1316379cfd77bea77a5831e7d275d5b699b22f","observation_id":"e0b1212b-14c2-415f-b0ce-d1aeb815e209","resolution":{"observed_at":"2026-08-02T23:22:38.703390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.16681","last_updated":"2025-02-23T18:54:15Z","snapshot_observed_at":"2026-08-10T14:11:45.391025Z","submitted_at":"2025-02-23T18:54:15Z","title":"Are Sparse Autoencoders Useful? A Case Study in Sparse Probing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.16681","snapshot_observed_at":"2026-08-02T23:22:38.310667Z","title":"Are sparse autoencoders useful? a case study in sparse probing.arXiv preprint arXiv:2502.16681,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.310667Z"},"links":{"cited_paper":"/paper/2502.16681","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:eb354cc93a620c5ff63a9834d17f8b318c826b91f2c4685ba4355c6cb9a4bf65","observation_id":"1793219f-8e9a-45c2-92fe-72f3cf5535db","resolution":{"observed_at":"2026-08-02T23:22:38.310667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06824","last_updated":"2024-08-19T01:18:41Z","snapshot_observed_at":"2026-07-06T16:30:37.867641Z","submitted_at":"2023-10-10T17:54:39Z","title":"The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06824","snapshot_observed_at":"2026-08-02T23:22:38.536363Z","title":"The geometry of truth: Emergent linear structure in large language model representations of true/false datasets.arXiv preprint arXiv:2310.06824,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.536363Z"},"links":{"cited_paper":"/paper/2310.06824","citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:e410a7b0e673ba17b7a7f65b9b10cbe229cdecb58551f923ba551c986420817b","observation_id":"d33871a0-14f6-4f9b-ad3e-adb08c095d09","resolution":{"observed_at":"2026-08-02T23:22:38.536363Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:38.137371Z","title":"Sparse autoencoder features for classifications and transferability","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.137371Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:6a97b1364c771faff449b3de7fd8f79619161aec1ab7b2b9f7a7e2608cbb397a","observation_id":"1b5281b6-515d-4abc-ac12-33eaebfb0e99","resolution":{"observed_at":"2026-08-02T23:22:38.137371Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T23:22:38.040897Z","title":"DeepMind Mechanistic Interpretability Team","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift","version":2},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-02T23:22:38.040897Z"},"links":{"citing_paper":"/paper/2602.14161"},"observation_digest":"sha256:ca8211772e043a4b25ebf8dcdec997dd8c258a7c8c0d84c04f2dc81c3867b5cd","observation_id":"a95826a6-5dd0-495a-a9f9-2bd572221255","resolution":{"observed_at":"2026-08-02T23:22:38.040897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.14161","last_updated":"2026-07-19T13:10:02Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T04:45:44.090544Z","submitted_at":"2026-02-15T14:21:43Z","title":"When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":14},"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 14 of 14 outbound references and 3 inbound Pith citation observations for arXiv:2602.14161."}