{"as_of":"2026-08-08T21:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f3dc423bdf66a226f22bd821f8f72b7dda667d6df3d4c89ea0daca54e6293dd1","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T17:39:44.013978Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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":"2026-05-15T05:57:19.399363Z","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":"2026-05-15T05:59:48.908825Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"cited_work":{"arxiv_id":"2603.23171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2603.23171","snapshot_observed_at":"2026-07-30T02:04:38.827270Z","title":"Aremu, D","venue":null,"work_id":"fec98221-9502-4fb7-a3fe-9ad67b99d006","year":null},"citing_paper":{"arxiv_id":"2605.13095","last_updated":"2026-05-14T13:23:50Z","snapshot_observed_at":"2026-08-03T03:51:16.576619Z","submitted_at":"2026-05-13T07:10:04Z","title":"Watermarking Should Be Treated as a Monitoring Primitive","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-14T19:10:59.937855Z"},"links":{"cited_paper":"/paper/2603.23171","citing_paper":"/paper/2605.13095"},"observation_digest":"sha256:27d75b8a0485e5536b4f8aeb09086899e49acf625c6e3eb205d296c51cec73e7","observation_id":"a946a793-65b3-4915-9170-80454a373aa6","resolution":{"observed_at":"2026-07-30T02:04:38.827270Z","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":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"cited_work":{"arxiv_id":"2603.23171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2603.23171","snapshot_observed_at":"2026-07-30T02:04:38.827270Z","title":"Aremu, D","venue":null,"work_id":"fec98221-9502-4fb7-a3fe-9ad67b99d006","year":null},"citing_paper":{"arxiv_id":"2605.13095","last_updated":"2026-05-14T13:23:50Z","snapshot_observed_at":"2026-08-03T03:51:16.576619Z","submitted_at":"2026-05-13T07:10:04Z","title":"Watermarking Should Be Treated as a Monitoring Primitive","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T05:57:19.399363Z"},"links":{"cited_paper":"/paper/2603.23171","citing_paper":"/paper/2605.13095"},"observation_digest":"sha256:d36ec884c0bedb6b312251015a4696ec498114fdabc409f12d93a4608607f047","observation_id":"54f1da3f-0259-4820-aa44-f130864553fe","resolution":{"observed_at":"2026-07-30T02:04:38.827270Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2603.23171/citation-record","integrity":"/paper/2603.23171/integrity","json":"/paper/2603.23171/citation-record.json","paper":"/paper/2603.23171"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-02T17:39:41.154929Z","title":"Gpt-4 technical report.arXiv preprint arXiv:2303.08774,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.154929Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:ede88580b4a63a242d3ae25f100dc17e5e9c0bfc12957e26e9dd79d4423afd63","observation_id":"80302a1d-188c-4a7d-b4a5-ca56e33b47e1","resolution":{"observed_at":"2026-08-02T17:39:41.154929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-02T17:39:41.568606Z","title":"Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.568606Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:f2b2b79e7ccc3c3cfbc0ca0cf0fef745f605c5c578f2d5d45b4c3a70d0ef5395","observation_id":"4d129cdd-2b53-43af-909f-e0d256dd8e59","resolution":{"observed_at":"2026-08-02T17:39:41.568606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09250","last_updated":"2026-05-22T03:11:22Z","snapshot_observed_at":"2026-08-01T08:01:02.690476Z","submitted_at":"2024-06-13T15:55:04Z","title":"MirrorCheck: Efficient Adversarial Defense for Vision-Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09250","snapshot_observed_at":"2026-08-02T17:39:41.825588Z","title":"Mirrorcheck: Efficient adversarial defense for vision- language models.arXiv preprint arXiv:2406.09250,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.825588Z"},"links":{"cited_paper":"/paper/2406.09250","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:8bad1f4dfeb3f0e897d49cc0746531ab250940073b4d8e53f33048e870aefeb8","observation_id":"168ce311-8d49-4ef1-9c82-cc66d1aa94dc","resolution":{"observed_at":"2026-08-02T17:39:41.825588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-08-02T17:39:41.949795Z","title":"Measuring mathematical problem solving with the math dataset.arXiv preprint arXiv:2103.03874,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.949795Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:ec026407dc9926f9ebe36b60090dfb50ffa60ff9f6e9cda330658ec2b2445a8a","observation_id":"2130c3f7-ec79-428c-a265-1d11bb15dfc9","resolution":{"observed_at":"2026-08-02T17:39:41.949795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06674","last_updated":"2023-12-07T19:40:50Z","snapshot_observed_at":"2026-07-06T17:00:00.321552Z","submitted_at":"2023-12-07T19:40:50Z","title":"Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06674","snapshot_observed_at":"2026-08-02T17:39:42.084298Z","title":"Llama guard: LLM-based input-output safeguard for human-AI conversations.arXiv preprint arXiv:2312.06674,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.084298Z"},"links":{"cited_paper":"/paper/2312.06674","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:78bbb86e0ff424a2397cd381fb9d5084243e6dfe3c88b9c8a4735503b5709219","observation_id":"c1a959fb-3477-4dfc-a69c-75e4b35d5cf3","resolution":{"observed_at":"2026-08-02T17:39:42.084298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14744","last_updated":"2025-06-23T06:11:32Z","snapshot_observed_at":"2026-08-07T18:01:22.772663Z","submitted_at":"2025-02-20T17:14:34Z","title":"HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14744","snapshot_observed_at":"2026-08-02T17:39:42.343655Z","title":"Hiddendetect: Detecting jailbreak attacks against large vision-language models via monitoring hidden states.arXiv preprint arXiv:2502.14744, 3(5),","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.343655Z"},"links":{"cited_paper":"/paper/2502.14744","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:f524db8815cc30bd7d490d91d0ddc3a53a820d9eb28f82f842d2823ed0104f65","observation_id":"2e4e9bf4-0386-432b-8f8c-e4ab437f8c22","resolution":{"observed_at":"2026-08-02T17:39:42.343655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03191","last_updated":"2024-11-28T13:43:50Z","snapshot_observed_at":"2026-08-04T19:12:56.970085Z","submitted_at":"2023-11-06T15:29:30Z","title":"DeepInception: Hypnotize Large Language Model to Be Jailbreaker","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.03191","snapshot_observed_at":"2026-08-02T17:39:42.537821Z","title":"Deepincep- tion: Hypnotize large language model to be jailbreaker.arXiv preprint arXiv:2311.03191,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.537821Z"},"links":{"cited_paper":"/paper/2311.03191","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:8d7d6c76978c2ae9dfbf9d385a7b33588d3419020a2dbf285759816dccc10a73","observation_id":"6aafcc01-92ce-4356-a137-8f3106c88bbb","resolution":{"observed_at":"2026-08-02T17:39:42.537821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00629","last_updated":"2024-11-26T11:59:17Z","snapshot_observed_at":"2026-08-04T00:35:46.251606Z","submitted_at":"2024-03-31T09:50:39Z","title":"Against The Achilles' Heel: A Survey on Red Teaming for Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00629","snapshot_observed_at":"2026-08-02T17:39:42.714332Z","title":"Against the achilles’ heel: A survey on red teaming for generative models.arXiv preprint, arXiv:2404.00629,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.714332Z"},"links":{"cited_paper":"/paper/2404.00629","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:698b9f5f6fe7323086b13a40addbf73c94b583c373a7ed121b32b586621a4e3f","observation_id":"cdd5f30b-c4b5-425a-ac88-4e8af75bfd13","resolution":{"observed_at":"2026-08-02T17:39:42.714332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04451","last_updated":"2024-03-20T21:34:56Z","snapshot_observed_at":"2026-08-06T02:57:30.438059Z","submitted_at":"2023-10-03T19:44:37Z","title":"AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04451","snapshot_observed_at":"2026-08-02T17:39:42.780056Z","title":"Autodan: Generating stealthy jailbreak prompts on aligned large language models.arXiv preprint arXiv:2310.04451,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.780056Z"},"links":{"cited_paper":"/paper/2310.04451","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:7f83d69cb840b42389648fd168d456bda10b584ab826c5688556c53ab31c1325","observation_id":"4bd3435b-6a83-4460-a7b4-e1128aa18ced","resolution":{"observed_at":"2026-08-02T17:39:42.780056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-02T17:39:42.876621Z","title":"Subhabrata Majumdar, Brian Pendleton, and Abhishek Gupta","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.876621Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:d7cf7c1cd86ccaf7032ecaf394c4f0ccca1e2b480d495c86fa381f3b927912d0","observation_id":"2cdf4dad-04a2-40a0-9eef-ad3ff45a8632","resolution":{"observed_at":"2026-08-02T17:39:42.876621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01263","last_updated":"2024-04-01T11:50:35Z","snapshot_observed_at":"2026-08-03T00:58:55.865010Z","submitted_at":"2023-08-02T16:30:40Z","title":"XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01263","snapshot_observed_at":"2026-08-02T17:39:43.019845Z","title":"Under review","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.019845Z"},"links":{"cited_paper":"/paper/2308.01263","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:32cb4c42d3845f8efb02f56c989062808f8a30fe3771ede0d5501335676a2767","observation_id":"443d7cf7-31b2-4a71-9d84-65be7c98f303","resolution":{"observed_at":"2026-08-02T17:39:43.019845Z","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":"2026-07-06T20:28:47.519113Z","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":"2026-08-02T17:39:43.163305Z","title":"Constitutional classifiers: Defending against universal jailbreaks across thousands of hours of red teaming.arXiv preprint arXiv:2501.18837,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.163305Z"},"links":{"cited_paper":"/paper/2501.18837","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:03abbabd9aa4cff4f732a8a93f1580faf9c5f3f14730f752237b02ad8c7f5352","observation_id":"f7f84e7b-ee6e-4ae1-ad62-eb45eb2fefd5","resolution":{"observed_at":"2026-08-02T17:39:43.163305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09261","last_updated":"2022-10-17T17:08:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-10-17T17:08:26Z","title":"Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.09261","snapshot_observed_at":"2026-08-02T17:39:43.299576Z","title":"Challenging big-bench tasks and whether chain-of-thought can solve them.arXiv preprint arXiv:2210.09261,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.299576Z"},"links":{"cited_paper":"/paper/2210.09261","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:73b73dadbfe70d8fdf6f368ab4c1ad28346367fbc1e09fb388d6d1dd685018ff","observation_id":"e8786eb1-1888-4eff-be47-7eddf6def933","resolution":{"observed_at":"2026-08-02T17:39:43.299576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01574","last_updated":"2024-11-06T02:54:00Z","snapshot_observed_at":"2026-08-06T00:29:17.674418Z","submitted_at":"2024-06-03T17:53:00Z","title":"MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01574","snapshot_observed_at":"2026-08-02T17:39:43.379650Z","title":"Mmlu-pro: A more ro- bust and challenging multi-task language understanding benchmark.arXiv preprint arXiv:2406.01574,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.379650Z"},"links":{"cited_paper":"/paper/2406.01574","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:a545a47991ea825c2e8ae2977a53c6c7ecc43eea3225032662b4f3dce010675f","observation_id":"80efb0bd-9825-4256-92e4-a6d350c0c80c","resolution":{"observed_at":"2026-08-02T17:39:43.379650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.14276","last_updated":"2025-10-16T04:00:18Z","snapshot_observed_at":"2026-07-06T22:32:47.087967Z","submitted_at":"2025-10-16T04:00:18Z","title":"Qwen3Guard Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.14276","snapshot_observed_at":"2026-08-02T17:39:43.471239Z","title":"Qwen3guard technical report.arXiv preprint arXiv:2510.14276,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.471239Z"},"links":{"cited_paper":"/paper/2510.14276","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:62a403eefc8244189cda3cdf24bf883f7b293fe32ae8368be71feacd3b40b6ba","observation_id":"089d5599-51c5-4e85-863e-53799a233b26","resolution":{"observed_at":"2026-08-02T17:39:43.471239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18479","last_updated":"2025-06-12T17:58:20Z","snapshot_observed_at":"2026-08-06T22:39:32.303708Z","submitted_at":"2024-11-27T16:22:33Z","title":"SoK: Watermarking for AI-Generated Content","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18479","snapshot_observed_at":"2026-08-02T17:39:43.550309Z","title":"Provable robust watermarking for AI-generated text","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.550309Z"},"links":{"cited_paper":"/paper/2411.18479","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:f3b5f34d5c1589e6a3616b245d4ab742dfdadf93b6dbd04e0a7410bb421f430d","observation_id":"25cfcb93-92fa-4f6b-849e-c6e6a8d85b96","resolution":{"observed_at":"2026-08-02T17:39:43.550309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07911","last_updated":"2023-11-14T05:13:55Z","snapshot_observed_at":"2026-07-06T16:47:08.877195Z","submitted_at":"2023-11-14T05:13:55Z","title":"Instruction-Following Evaluation for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07911","snapshot_observed_at":"2026-08-02T17:39:43.658628Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.658628Z"},"links":{"cited_paper":"/paper/2311.07911","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:5739b61bedfcfaacd1edcf6a61690a067d2166b31f78abb880a5219534a04e1d","observation_id":"9d3951d5-eaf9-4f24-ab2a-e4036b427202","resolution":{"observed_at":"2026-08-02T17:39:43.658628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01405","last_updated":"2025-03-03T06:14:14Z","snapshot_observed_at":"2026-07-06T16:26:38.284922Z","submitted_at":"2023-10-02T17:59:07Z","title":"Representation Engineering: A Top-Down Approach to AI Transparency","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01405","snapshot_observed_at":"2026-08-02T17:39:43.766293Z","title":"Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.766293Z"},"links":{"cited_paper":"/paper/2310.01405","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:8aeefd240982449d78f456b77961dc00ac0652b451856fda1f84303d3b868097","observation_id":"c1e5f8ce-f3d7-45ee-be63-6b742e14bc9d","resolution":{"observed_at":"2026-08-02T17:39:43.766293Z","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":"2026-08-02T17:39:43.884154Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:43.884154Z"},"links":{"citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:0ec2a96ca76c40729e62f78464a67bfb9ab52711f2ba2c9bf113ca77597f4fdf","observation_id":"b4f38a60-5ee7-47a6-a6f4-c4d7d2608618","resolution":{"observed_at":"2026-08-02T17:39:43.884154Z","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":"2026-08-02T17:39:44.013978Z","title":"The attacker then issues the translated prompt to the model and, for evaluation, translates the answer back into English","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:44.013978Z"},"links":{"citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:f0bfb690416da6b283e9887fbc657342f3129cf7c0404199d088ef5e4c357246","observation_id":"c05d3460-5916-4c79-a27e-666356ee0d6b","resolution":{"observed_at":"2026-08-02T17:39:44.013978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02440","last_updated":"2025-05-21T04:37:27Z","snapshot_observed_at":"2026-07-06T19:26:58.835479Z","submitted_at":"2024-10-03T12:37:39Z","title":"Optimizing Adaptive Attacks against Watermarks for Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02440","snapshot_observed_at":"2026-08-02T17:39:41.676660Z","title":"Optimizing adaptive attacks against watermarks for language models.arXiv preprint arXiv:2410.02440,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.676660Z"},"links":{"cited_paper":"/paper/2410.02440","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:d8d37db0b6ebcb4043651f5626791a7cc19239fa3d0d5e618224f6379987ecea","observation_id":"816375f6-b7aa-484b-9df8-095c7c70de76","resolution":{"observed_at":"2026-08-02T17:39:41.676660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09565","last_updated":"2025-02-08T23:58:36Z","snapshot_observed_at":"2026-07-06T20:06:09.333397Z","submitted_at":"2024-12-12T18:49:53Z","title":"Obfuscated Activations Bypass LLM Latent-Space Defenses","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09565","snapshot_observed_at":"2026-08-02T17:39:41.404497Z","title":"Obfuscated activations bypass LLM latent-space defenses.arXiv preprint arXiv:2412.09565,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.404497Z"},"links":{"cited_paper":"/paper/2412.09565","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:0484ea6ac179a3f57476e1353f7ed70a79439b585baeb8ef3f1b02c7d854de92","observation_id":"24a9af66-5191-471a-93b5-341c475e8be1","resolution":{"observed_at":"2026-08-02T17:39:41.404497Z","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":"2026-08-02T17:39:42.200858Z","title":"Beavertails: Towards improved safety alignment of llm via a human-preference dataset.arXiv preprint arXiv:2307.04657,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.200858Z"},"links":{"citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:0716d3bdcecc7df8fa663606af59a14121770350d2809f799725d8021315727d","observation_id":"1da578b5-3c97-45b9-a11b-dcc30e0382f2","resolution":{"observed_at":"2026-08-02T17:39:42.200858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.07871","last_updated":"2026-05-11T08:00:17Z","snapshot_observed_at":"2026-08-02T20:18:48.475092Z","submitted_at":"2025-07-10T15:52:32Z","title":"Mitigating Watermark Forgery in Generative Models via Randomized Key Selection","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.07871","snapshot_observed_at":"2026-08-02T17:39:41.297826Z","title":"On the reliability of large language models to misinformed and demographically informed prompts.AI Magazine, 46(1):e12208, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:41.297826Z"},"links":{"cited_paper":"/paper/2507.07871","citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:541de24bcef3ef939d9c2e003f00b711171f6b72c8aba6f016223b601f477d86","observation_id":"9303b4d7-e24e-4ab1-9ff5-947e0386b362","resolution":{"observed_at":"2026-08-02T17:39:41.297826Z","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":"2026-08-02T17:39:42.434758Z","title":"ISBN 9798400720406","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T17:39:42.434758Z"},"links":{"citing_paper":"/paper/2603.23171"},"observation_digest":"sha256:06b6e73fc4d9f0f617c4c4e524477036fa6fd82090c1f885b77fee2f49301dee","observation_id":"0a045218-3b2d-4b1f-a4e2-4c2ded9bac89","resolution":{"observed_at":"2026-08-02T17:39:42.434758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.23171","last_updated":"2026-07-29T15:06:33Z","latest_version":3,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T17:14:23.529795Z","submitted_at":"2026-03-24T13:13:23Z","title":"Adaptively Robust LLM Monitoring via Activation Watermarking"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":25},"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 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2603.23171."}