{"as_of":"2026-08-14T13:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ee530a4713f1b9e61e1ae7cf92a793d64abd95ff5d8bc4ea1da4db60dffcaf69","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:48:55.479301Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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":"2026-08-04T15:22:55.705722Z","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":"2026-05-12T08:06:28.393138Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"cited_work":{"arxiv_id":"2508.15842","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.15842","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Changyue Wang, Weihang Su, Qingyao Ai, and Yiqun Liu","venue":null,"work_id":"c8d7146b-ebe0-498e-b15e-3fceeb36be8a","year":null},"citing_paper":{"arxiv_id":"2605.08346","last_updated":"2026-05-08T18:00:58Z","snapshot_observed_at":"2026-08-11T12:55:05.433763Z","submitted_at":"2026-05-08T18:00:58Z","title":"Sanity Checks for Long-Form Hallucination Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-12T01:19:19.238980Z"},"links":{"cited_paper":"/paper/2508.15842","citing_paper":"/paper/2605.08346"},"observation_digest":"sha256:7a7cdae7513aeda468d8b12d5f463fcee912a925faab67c0802a2ed3727be558","observation_id":"0dbbe5fe-f866-44ac-9748-ce8c1ef9db00","resolution":{"observed_at":"2026-05-12T08:06:28.402436Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.15842","snapshot_observed_at":"2026-08-04T15:22:55.705722Z","title":"Lexical hints of accuracy in LLM reasoning chains.arXiv preprint arXiv:2508.15842, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02089","last_updated":"2026-08-03T11:48:39Z","snapshot_observed_at":"2026-08-14T06:16:12.008084Z","submitted_at":"2026-08-03T11:48:39Z","title":"How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T15:22:55.705722Z"},"links":{"cited_paper":"/paper/2508.15842","citing_paper":"/paper/2608.02089"},"observation_digest":"sha256:c938b779eab7589e065281f5d005c607fec0b3b14aadac8e5d671d626ab1a26f","observation_id":"7fa0ea57-4e87-4d6d-ab0a-7fa9a9957c7a","resolution":{"observed_at":"2026-08-04T15:22:55.705722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.15842/citation-record","integrity":"/paper/2508.15842/integrity","json":"/paper/2508.15842/citation-record.json","paper":"/paper/2508.15842"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.12173","last_updated":"2023-05-05T14:26:17Z","snapshot_observed_at":"2026-08-12T14:45:22.117576Z","submitted_at":"2023-02-23T17:14:38Z","title":"Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12173","snapshot_observed_at":"2026-08-05T18:48:50.786671Z","title":"Not what you’ve signed up for: Compromising real-world llm-integrated appli- cations with indirect prompt injection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:50.786671Z"},"links":{"cited_paper":"/paper/2302.12173","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:d6cfb06ead38bdbf6508de45acfba054e41c9942dbd6887cccf4dfc7d9a9cdc6","observation_id":"8cddd00c-28e2-4870-81b2-422d8464ed48","resolution":{"observed_at":"2026-08-05T18:48:50.786671Z","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":"2026-08-13T04:57:11.531306Z","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":"2026-08-05T18:48:50.863975Z","title":"Benchmarking and defending against indi- rect prompt injection attacks on large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:50.863975Z"},"links":{"cited_paper":"/paper/2312.14197","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:c7a4498e7d005a070119c225c356deb6191732c0ba146e95bd392fd91cc7f2df","observation_id":"1bc81062-1a6c-4fe0-9237-9da26b29fbe2","resolution":{"observed_at":"2026-08-05T18:48:50.863975Z","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":"2026-08-05T18:48:59.860373Z","title":"Goodfellow, Jonathon Shlens, and Chris- tian Szegedy","venue":null,"work_id":"709bce5a-30ad-4e8d-9be1-e00ccc120c42","year":2015},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:50.990382Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:3400aced1131afbf34ebf61b91ae9974bad14585b63c10e2c809d07aaa732763","observation_id":"5ed479fd-84da-4618-bb7c-dd9fd1fdd78c","resolution":{"observed_at":"2026-08-05T18:48:59.951981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:48:51.146384Z","title":"Towards evaluating the robustness of neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.146384Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:95e33e6608ecb46be06e77b6a1f537c17a7836b6db05ec623fa63baadb5f410c","observation_id":"dc0f4cea-8784-4782-8bee-980daaa94640","resolution":{"observed_at":"2026-08-05T18:48:51.146384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15043","last_updated":"2023-12-20T20:48:57Z","snapshot_observed_at":"2026-08-12T09:06:50.363435Z","submitted_at":"2023-07-27T17:49:12Z","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15043","snapshot_observed_at":"2026-08-05T18:48:51.313157Z","title":"Zico Kolter, and Matt Fredrik- son","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.313157Z"},"links":{"cited_paper":"/paper/2307.15043","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:ec2436890485d5fe58bd699fcb117743a079b1f92d179eb6f4c25ed23b3e88c2","observation_id":"9dc8fd3f-6326-45ba-9e50-df1e29c58a14","resolution":{"observed_at":"2026-08-05T18:48:51.313157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.13548","last_updated":"2025-05-10T07:10:46Z","snapshot_observed_at":"2026-08-14T11:14:11.780984Z","submitted_at":"2023-10-20T14:46:48Z","title":"Towards Understanding Sycophancy in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.13548","snapshot_observed_at":"2026-08-05T18:48:51.499083Z","title":"Towards understanding sycophancy in lan- guage models.arXiv preprint arXiv:2310.13548, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.499083Z"},"links":{"cited_paper":"/paper/2310.13548","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:563694ac426318dce07da45db847ef97456ac0fb6a51475387dd7d4ce0e64610","observation_id":"42bea56d-127a-4cba-8ef5-c45f5814a891","resolution":{"observed_at":"2026-08-05T18:48:51.499083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11817","last_updated":"2025-02-13T08:11:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-22T10:26:14Z","title":"Hallucination is Inevitable: An Innate Limitation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11817","snapshot_observed_at":"2026-08-05T18:48:51.655868Z","title":"Hallucination is inevitable: An innate limita- tion of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.655868Z"},"links":{"cited_paper":"/paper/2401.11817","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:92f8f7e0b9cfb7c265d4dbd0e8324f98b83ec39f7a22290ae9e93ed591655d7f","observation_id":"2ea971e8-edf2-41a6-8a66-1a51494c4db2","resolution":{"observed_at":"2026-08-05T18:48:51.655868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05499","last_updated":"2025-12-29T02:25:27Z","snapshot_observed_at":"2026-07-06T15:40:27.639368Z","submitted_at":"2023-06-08T18:43:11Z","title":"Prompt Injection attack against LLM-integrated Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05499","snapshot_observed_at":"2026-08-05T18:48:51.798763Z","title":"Prompt injection attack against LLM-integrated applications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.798763Z"},"links":{"cited_paper":"/paper/2306.05499","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:fc2a3cec01c0c104ab136e800e93948937989108bb74c46a771eac16508de5ce","observation_id":"a5c2612b-25f9-491e-9fee-c933b5ec96a5","resolution":{"observed_at":"2026-08-05T18:48:51.798763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04957","last_updated":"2024-03-07T23:46:20Z","snapshot_observed_at":"2026-08-13T00:59:41.384742Z","submitted_at":"2024-03-07T23:46:20Z","title":"Automatic and Universal Prompt Injection Attacks against Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04957","snapshot_observed_at":"2026-08-05T18:48:51.940993Z","title":"Automatic and universal prompt injection attacks against large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:51.940993Z"},"links":{"cited_paper":"/paper/2403.04957","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:2697827d9f12a8450cc55f520b10df5a519759dc31c38b328c789cfa01808728","observation_id":"d7ec56af-1696-4abd-a490-5b82e13facb2","resolution":{"observed_at":"2026-08-05T18:48:51.940993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06565","last_updated":"2016-07-25T17:23:29Z","snapshot_observed_at":"2026-07-06T05:00:46.434335Z","submitted_at":"2016-06-21T13:37:05Z","title":"Concrete Problems in AI Safety","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.06565","snapshot_observed_at":"2026-08-05T18:48:52.069538Z","title":"Concrete problems in AI safety","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.069538Z"},"links":{"cited_paper":"/paper/1606.06565","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:0126ccfcdf0f6f02e9cdc283163a72aa94b9e6b7d7901e2027884b20b342ec99","observation_id":"4eb29adb-1642-424c-9233-eb9ab704962b","resolution":{"observed_at":"2026-08-05T18:48:52.069538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08575","last_updated":"2018-10-19T16:30:48Z","snapshot_observed_at":"2026-08-04T21:33:05.702276Z","submitted_at":"2018-10-19T16:30:48Z","title":"Supervising strong learners by amplifying weak experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08575","snapshot_observed_at":"2026-08-05T18:48:52.218451Z","title":"Supervising strong learners by amplifying weak experts","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.218451Z"},"links":{"cited_paper":"/paper/1810.08575","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:74557b44d1a1aad164d4c5808daa3af8e6da1d5d5afe0dcc2d8054b94c1c452b","observation_id":"517855bc-add8-4741-8a6b-641a7a3ed0ae","resolution":{"observed_at":"2026-08-05T18:48:52.218451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08073","last_updated":"2022-12-15T06:19:23Z","snapshot_observed_at":"2026-08-02T04:53:58.766070Z","submitted_at":"2022-12-15T06:19:23Z","title":"Constitutional AI: Harmlessness from AI Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08073","snapshot_observed_at":"2026-08-05T18:48:52.361776Z","title":"Constitutional AI: Harmlessness from AI feed- back","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.361776Z"},"links":{"cited_paper":"/paper/2212.08073","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:4824e09d610bdf04775539b609d04e40833347bc45cb8f16d03943b8bae7ee72","observation_id":"2df94f2f-fa6a-46d1-9824-df4cd846188b","resolution":{"observed_at":"2026-08-05T18:48:52.361776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.02155","last_updated":"2022-03-04T07:04:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-03-04T07:04:42Z","title":"Training language models to follow instructions with human feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.02155","snapshot_observed_at":"2026-08-05T18:48:52.469472Z","title":"Training language models to follow instructions with hu- man feedback.arXiv preprint arXiv:2203.02155, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.469472Z"},"links":{"cited_paper":"/paper/2203.02155","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:eaeb9bf2096514cca615e3e75a04283b237ee5277b554a01d6a807111a515a8e","observation_id":"a1ae48c1-88f8-4a02-bb1e-8c8525e6dc68","resolution":{"observed_at":"2026-08-05T18:48:52.469472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03741","last_updated":"2023-02-17T17:00:34Z","snapshot_observed_at":"2026-08-12T12:29:44.507445Z","submitted_at":"2017-06-12T17:23:59Z","title":"Deep reinforcement learning from human preferences","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03741","snapshot_observed_at":"2026-08-05T18:48:52.582529Z","title":"Deep reinforcement learning from human prefer- ences","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.582529Z"},"links":{"cited_paper":"/paper/1706.03741","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:68f6895ab8d03ec7c2bdefe4dc80807e8fd9a66d7ba3061d8df5c178e1c087a9","observation_id":"1d27f6f6-0010-4c70-a380-7c5d1f095c51","resolution":{"observed_at":"2026-08-05T18:48:52.582529Z","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":"2026-08-05T18:48:52.724623Z","title":"Thinking, Fast and Slow","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.724623Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:682805e835f9c6c72b84ccc1de17f88c49ea8b79791a5dc19315885dd5f2eaf2","observation_id":"879f3845-db6c-4c66-934f-8dd57cd4e66e","resolution":{"observed_at":"2026-08-05T18:48:52.724623Z","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":"2026-08-05T18:48:59.499928Z","title":"Judgment under uncertainty: Heuristics and biases","venue":null,"work_id":"37b6886e-c4c2-452b-a6f4-8b4838cf8cd2","year":1974},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.848858Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:0902661305ddc7d401e9c78752a2d8187837405af0ea75248af73702238ad016","observation_id":"cb0f190a-8d74-4599-9404-c84acb9f2407","resolution":{"observed_at":"2026-08-05T18:48:59.666249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12171","last_updated":"2024-03-18T18:39:53Z","snapshot_observed_at":"2026-08-13T00:50:55.815580Z","submitted_at":"2024-03-18T18:39:53Z","title":"EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12171","snapshot_observed_at":"2026-08-05T18:48:52.986090Z","title":"EasyJailbreak: A unified framework for jailbreaking large language mod- els","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:52.986090Z"},"links":{"cited_paper":"/paper/2403.12171","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:b41711e2a532d974e57704a9aa990c473ee6190094f3a43ceda34af844f01ce5","observation_id":"ac56889a-884f-4308-8961-c30920db1152","resolution":{"observed_at":"2026-08-05T18:48:52.986090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07867","last_updated":"2024-08-13T01:55:06Z","snapshot_observed_at":"2026-08-13T04:20:22.513606Z","submitted_at":"2024-02-12T18:28:36Z","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07867","snapshot_observed_at":"2026-08-05T18:48:53.146867Z","title":"Poisonedrag: Knowledge cor- ruption attacks to retrieval-augmented genera- tion of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.146867Z"},"links":{"cited_paper":"/paper/2402.07867","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:eeddd5a3338bc98fbe067dcd025a4399c3a16be7c1b84e2d5f45907a29613fb3","observation_id":"1a0c9a77-6560-4646-af5b-272626ed5d7a","resolution":{"observed_at":"2026-08-05T18:48:53.146867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13401","last_updated":"2024-07-07T05:03:53Z","snapshot_observed_at":"2026-08-13T00:02:27.152550Z","submitted_at":"2024-05-22T07:21:32Z","title":"TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13401","snapshot_observed_at":"2026-08-05T18:48:53.336959Z","title":"Trojanrag: Retrieval-augmented generation can be backdoor driver in large lan- guage models.arXiv preprint arXiv:2405.13401, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.336959Z"},"links":{"cited_paper":"/paper/2405.13401","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:63646c7dc26187c910c66f677b0ad15166a1bad9f840200ac64538a82df593ec","observation_id":"9afb61da-6c6e-4e21-9c9e-2fb3924da373","resolution":{"observed_at":"2026-08-05T18:48:53.336959Z","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":"2026-08-05T18:48:59.170612Z","title":"OWASP top 10 for large language model applications 2025","venue":null,"work_id":"2d83dfa9-fe80-4cac-b162-c3463bc95132","year":2025},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.477038Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:f36583e3841829d3e0e7c71a13eaa276dd2ed7fe53591a4203a8d4cbc9555393","observation_id":"f41a1c87-17d8-4fe7-8dbe-42ada4b068ab","resolution":{"observed_at":"2026-08-05T18:48:59.326415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:58.879250Z","title":"MITRE ATLAS (adver- sarial threat landscape for artificial-intelligence systems), 2021","venue":null,"work_id":"b8af86b4-0882-472f-9a27-cb6f79366eb2","year":2021},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.635262Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:c577f787b6726286a1aac448f8c90d7a745632a0690280e1c802ad0c5aedb226","observation_id":"d20cec10-060b-499b-bc34-648554ce6558","resolution":{"observed_at":"2026-08-05T18:48:59.043242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:58.607311Z","title":"Artificial intelligence risk man- agement framework (AI RMF 1.0)","venue":null,"work_id":"a0fa8133-0dbb-4759-bafa-237b3cf74789","year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.754057Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:57694e740cf3093ac605c04af6eae8e8951f883aa9b90bccdc5dd91b6dae5c10","observation_id":"b5b8127f-f1b2-4441-a567-01b7d05251d3","resolution":{"observed_at":"2026-08-05T18:48:58.719160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:58.295712Z","title":"Information technology—artificial intelligence— guidance on risk management, 2023","venue":null,"work_id":"10124053-f38b-4e48-8075-49b1815abad4","year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.890287Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:b666673c5cea20682265926b0c407f2a7548f71b512a51b576c28654cbfbf2a3","observation_id":"6661c7c7-9dc1-4c99-899e-338d35a160e9","resolution":{"observed_at":"2026-08-05T18:48:58.423304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.07858","last_updated":"2022-11-22T19:12:57Z","snapshot_observed_at":"2026-08-13T10:17:00.791443Z","submitted_at":"2022-08-23T23:37:14Z","title":"Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.07858","snapshot_observed_at":"2026-08-05T18:48:53.995402Z","title":"Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:53.995402Z"},"links":{"cited_paper":"/paper/2209.07858","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:784bf0e22bb855101df14d70cb644d669a7f4e9b773530f814a1b40ef2c6d9b8","observation_id":"49ccff31-0c15-4bab-98d9-4f370195e362","resolution":{"observed_at":"2026-08-05T18:48:53.995402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05566","last_updated":"2024-01-17T20:26:01Z","snapshot_observed_at":"2026-08-12T12:19:52.685961Z","submitted_at":"2024-01-10T22:14:35Z","title":"Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05566","snapshot_observed_at":"2026-08-05T18:48:54.113119Z","title":"Ziegler, et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.113119Z"},"links":{"cited_paper":"/paper/2401.05566","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:6dcc6fcdb872ddc6dcac9f8f54e3684ebd1e6aa723159946adfefec0c4f2c362","observation_id":"363659ad-1e53-4862-a1de-9b89b37fa061","resolution":{"observed_at":"2026-08-05T18:48:54.113119Z","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":"2026-08-05T18:48:58.049775Z","title":null,"venue":null,"work_id":"09d97f28-b6fe-4460-90d0-252d4eb39336","year":1999},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.263071Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:69495991a6b8080021de9f8016e31fda9d1aa1ee6131ea1d566daa02e3f20d84","observation_id":"2efe275a-1be1-4064-ba2d-490e5cb9ba6e","resolution":{"observed_at":"2026-08-05T18:48:58.181584Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:57.738051Z","title":"De- veloping trustworthy artificial intelligence: In- sights from research on interpersonal, human- automation, and human-AI trust","venue":null,"work_id":"2a4c850c-4a95-488e-8191-90fe415a4e0c","year":2024},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.394167Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:cf0c487e23e3ccfc834cd152697a4e0c771d2c499079b6209c1db4b8abca4ce1","observation_id":"df091262-2b34-4612-a296-c11eea7c22ab","resolution":{"observed_at":"2026-08-05T18:48:57.905727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:57.443301Z","title":"Bartz, Karen S","venue":null,"work_id":"b1f31eb4-28b4-4ab9-8c93-7442ecabd5ee","year":2025},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.525631Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:ddd25ee1031b8ab35c25db3df9944dc3dbfb43ee2ece18ec8b97ee5e21140bfe","observation_id":"db113349-b86e-4fa4-8e03-037e0f9746ea","resolution":{"observed_at":"2026-08-05T18:48:57.606364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05232","last_updated":"2024-11-19T12:42:45Z","snapshot_observed_at":"2026-07-06T16:45:07.733095Z","submitted_at":"2023-11-09T09:25:37Z","title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05232","snapshot_observed_at":"2026-08-05T18:48:54.676287Z","title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.676287Z"},"links":{"cited_paper":"/paper/2311.05232","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:e322544ae24fcbef1e1e30354c67e4976d26b1df10550b57c4930f840fca76b7","observation_id":"057089c1-1ec1-4573-9577-a8b38f678c7a","resolution":{"observed_at":"2026-08-05T18:48:54.676287Z","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":"2026-08-05T18:48:57.184107Z","title":"Prospect theory: An analysis of decision under risk","venue":null,"work_id":"3b818e5d-f970-4ccd-a090-7faf037ef679","year":1979},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.860134Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:1abab0ee6758b05d44dae9e006c874b2ab10f5a11c5ea75a04318e7f82570d0c","observation_id":"7d73111e-a77d-4051-beac-d08c7a0af318","resolution":{"observed_at":"2026-08-05T18:48:57.290609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:56.945436Z","title":"Cialdini","venue":null,"work_id":"d50fd870-bdfa-4f23-b109-03d17c09e4e1","year":2021},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:54.972582Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:45d72601ec395729b2a3cbed8bd06ce5a22dae874733d4ff918c71487f756df3","observation_id":"d452841b-2e53-4fde-9d34-49a2cc79b726","resolution":{"observed_at":"2026-08-05T18:48:57.075557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.03714","last_updated":"2025-07-23T19:59:08Z","snapshot_observed_at":"2026-08-12T15:50:08.589635Z","submitted_at":"2025-07-23T19:59:08Z","title":"\"Think First, Verify Always\": Training Humans to Face AI Risks","version":1},"cited_work":{"arxiv_id":"2508.03714","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.03714","snapshot_observed_at":"2026-08-05T18:48:55.892486Z","title":"\"Think First, Verify Always\": Training Humans to Face AI Risks","venue":"cs.HC","work_id":"ae128c60-840a-4f27-a858-36459e5363e5","year":2025},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:55.133357Z"},"links":{"cited_paper":"/paper/2508.03714","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:f01dc6796d2b0d0b5d55044538a16a98ddd65cf573e423b6868b8e22ad53d741","observation_id":"8f6a53b5-275d-47a2-9db7-69caaa45928c","resolution":{"observed_at":"2026-08-05T18:48:55.982994Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:56.592005Z","title":"O’Reilly Media, 2005","venue":null,"work_id":"7d6bc9d4-4711-4beb-a7d6-5141e0924a1e","year":2005},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:55.255996Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:e4af6fad604f86f3aa32a4f34721f842aa7564efb866ff0feb96e3a56866b594","observation_id":"0977b82f-fa52-49ef-83d3-80b7898c5f42","resolution":{"observed_at":"2026-08-05T18:48:56.745007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-05T18:48:56.342821Z","title":null,"venue":null,"work_id":"7494287a-940e-4936-88ea-b67c54e99b30","year":2021},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:55.352603Z"},"links":{"citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:b23ecdf6fe82dea39800f8f71f3894528ff76e0c5ff172ecf9d53017828bfc7b","observation_id":"610e1a58-b872-4d84-b057-22402d221150","resolution":{"observed_at":"2026-08-05T18:48:56.433758Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10033","last_updated":"2025-08-09T15:46:30Z","snapshot_observed_at":"2026-08-05T22:23:51.227935Z","submitted_at":"2025-08-09T15:46:30Z","title":"Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7","version":1},"cited_work":{"arxiv_id":"2508.10033","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.10033","snapshot_observed_at":"2026-08-05T18:48:55.644261Z","title":"Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7","venue":"cs.CR","work_id":"a2fd6eb4-4b4b-4d52-9460-09413fc3f648","year":2025},"citing_paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T18:48:55.479301Z"},"links":{"cited_paper":"/paper/2508.10033","citing_paper":"/paper/2508.15842"},"observation_digest":"sha256:af99e715b6ab110f37b65168fcc1f06dc89edcf1b8eb3fdd5b8daefbdd872059","observation_id":"69d219e6-fa59-48bb-93bd-65d5e1b9cb5b","resolution":{"observed_at":"2026-08-05T18:48:55.775129Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.15842","last_updated":"2025-08-19T18:20:38Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T19:02:07.711955Z","submitted_at":"2025-08-19T18:20:38Z","title":"Lexical Hints of Accuracy in LLM Reasoning Chains"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":2,"verified_fuzzy":11},"total_outbound_references":35},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 2 inbound Pith citation observations for arXiv:2508.15842."}