{"as_of":"2026-08-10T18:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1385811125d4daf49e22eb1934a1858056674acc6a839bdc51ac72b7732e7248","coverage":[{"denominator":116,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T20:03:41.786161Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.08912/citation-record","integrity":"/paper/2509.08912/integrity","json":"/paper/2509.08912/citation-record.json","paper":"/paper/2509.08912"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:03:33.190329Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.190329Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:eb20fa1d469dbd9a564dc891882f0ee6d5080852525d8f3a19347a4f88a73f24","observation_id":"6f66d6a2-ebfc-401c-9424-1bb63a03897e","resolution":{"observed_at":"2026-08-04T20:03:33.190329Z","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":"2026-08-04T20:03:33.303852Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.303852Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:95395cfe4f0f92fb7dfae0e5a5cad144ac4091465502791efbaa5a90404d4dda","observation_id":"21642f5d-ea54-4127-95cc-c280a424fb22","resolution":{"observed_at":"2026-08-04T20:03:33.303852Z","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":"2026-08-04T20:03:33.348788Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.348788Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:e0f4b8fde5c7d5333e8cdbaf3b3323bd6f46f41f1eab1bb9967a70dbb7e5daeb","observation_id":"44915870-36f3-4b2c-92fd-06cc6841ed2e","resolution":{"observed_at":"2026-08-04T20:03:33.348788Z","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":"2026-08-04T20:03:33.420917Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.420917Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:c2a4c3f344bf492f8a434d261947b94c7bb53ef8a4153aea51b5db950470a3c1","observation_id":"590c1797-79b8-4124-a562-cee8b78c0b25","resolution":{"observed_at":"2026-08-04T20:03:33.420917Z","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":"2026-08-04T20:03:33.540633Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.540633Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:0a488c6716fec94413af82da06f73c544020d76551a387bcf2e76d2deb5b0090","observation_id":"96141a7c-a6e5-4ced-9d46-9f25d944fe4d","resolution":{"observed_at":"2026-08-04T20:03:33.540633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06800","last_updated":"2024-10-11T17:16:30Z","snapshot_observed_at":"2026-08-09T10:42:19.218151Z","submitted_at":"2024-05-10T20:23:46Z","title":"LLM-Generated Black-box Explanations Can Be Adversarially Helpful","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06800","snapshot_observed_at":"2026-08-04T20:03:33.662631Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.662631Z"},"links":{"cited_paper":"/paper/2405.06800","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:09246d928ca6d671ac467504a1d443532fcaa0d4e3b37d42546798c220e628d0","observation_id":"81b3d2bb-8c72-4e3b-bba7-ab3936670dc5","resolution":{"observed_at":"2026-08-04T20:03:33.662631Z","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":"2026-08-04T20:03:33.788702Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.788702Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:8933f791ee4177ae8400fe0ee7665f98d87ac001c689c2fb65097c05a424f6d1","observation_id":"c456c8a8-94c6-4c15-b93b-0c99c35e7039","resolution":{"observed_at":"2026-08-04T20:03:33.788702Z","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":"2026-08-04T20:03:33.877585Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.877585Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f4c88faa97e01041496bb55d272fa9d6e5fe04d2220da9b0ed79487d20f34dff","observation_id":"3dd81439-283f-4f0f-812f-4215610d3885","resolution":{"observed_at":"2026-08-04T20:03:33.877585Z","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":"2026-08-04T20:03:33.995299Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:33.995299Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:de5c6055ef206b406ae14eab47f434a4309db71d464717d0b02ec311f0fec858","observation_id":"bb130ccf-0587-4418-8e8f-bd0ab9f712f1","resolution":{"observed_at":"2026-08-04T20:03:33.995299Z","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":"2026-08-04T20:03:34.180983Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.180983Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:e36da262360bb38d8860b0e46010345df009b950391dbea9e9ffbae81ec1d422","observation_id":"537ada0e-5849-4c4a-8977-42ad2b65c4e1","resolution":{"observed_at":"2026-08-04T20:03:34.180983Z","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":"2026-08-04T20:03:34.342131Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.342131Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:3216a22e96094f95383b3f2f295b98c2873edc43300bfdc5e6a31bc18c334251","observation_id":"791f62dc-c0bf-4918-a97a-cdbf497674fb","resolution":{"observed_at":"2026-08-04T20:03:34.342131Z","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":"2026-08-04T20:03:34.503234Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.503234Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b19ca8a0926c4fe161d03432bc6d0c57d515860fc0498c430e3cbbd204a72d80","observation_id":"00019c9d-9a71-46a4-a88f-c01bb314f084","resolution":{"observed_at":"2026-08-04T20:03:34.503234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13926","last_updated":"2024-02-21T16:46:36Z","snapshot_observed_at":"2026-08-07T20:27:31.331983Z","submitted_at":"2024-02-21T16:46:36Z","title":"Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13926","snapshot_observed_at":"2026-08-04T20:03:34.617279Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.617279Z"},"links":{"cited_paper":"/paper/2402.13926","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:901c89922a00674fb4b25d9f7789991a59bf37b09be9345e1e461d0121ffaba8","observation_id":"29c1cd60-2446-4ef3-bb2a-8383c246b32c","resolution":{"observed_at":"2026-08-04T20:03:34.617279Z","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":"2026-08-04T20:03:34.727201Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.727201Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:cb2b40875bd98e4f11a4f80ff189dec3ef8a9624480ebef5cf617990497607bf","observation_id":"7ee98534-16fa-46ec-b774-4dba5a6ad099","resolution":{"observed_at":"2026-08-04T20:03:34.727201Z","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":"2026-08-04T20:03:34.865027Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.865027Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:6138e4ffdd20c711abb8bcc4a9ba2d42555fbe3aecde37073ac2511a38879131","observation_id":"620b4173-2ac5-459d-8119-718f4a1169d2","resolution":{"observed_at":"2026-08-04T20:03:34.865027Z","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":"2026-08-04T20:03:34.992196Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:34.992196Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:cdf6a01b371bb2e3a500233898f92234a420089e47c39ea6a426ebfad7a78f10","observation_id":"6eb6e4e7-f586-4269-bfcf-1213a1d68955","resolution":{"observed_at":"2026-08-04T20:03:34.992196Z","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":"2026-08-04T20:03:35.070293Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.070293Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:d20cc3e470ed48b21ae4d650511a8958dee111437fdb5186eea69728ce8ca8ea","observation_id":"a57479c4-73ca-4da6-9407-c74b85734801","resolution":{"observed_at":"2026-08-04T20:03:35.070293Z","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":"2026-08-04T20:03:35.160862Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.160862Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:a35c2858ae37dca7cef7e20c95ff5da3794d8d3a7f7ddf232cbf6134fd7402b3","observation_id":"7db1699b-6e5c-4f2a-adc0-6dc0522f4fcc","resolution":{"observed_at":"2026-08-04T20:03:35.160862Z","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":"2026-08-04T20:03:35.272393Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.272393Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:6a70c77d512341c74a63bbbea95588227ba4adc2454c1cb6e35834a29e47b385","observation_id":"786cce3c-7fd9-4729-999f-be200c6a054c","resolution":{"observed_at":"2026-08-04T20:03:35.272393Z","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":"2026-08-04T20:03:35.391442Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.391442Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:3dd7d1c16affc61b2078d8cf7c72576d3ba2e8a7682ea4fddecd3821bdc1c887","observation_id":"436f745c-3a59-4817-8825-254341e344cf","resolution":{"observed_at":"2026-08-04T20:03:35.391442Z","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":"2026-08-04T20:03:35.467310Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.467310Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:35b9938fe8f872ec82f370033c7a415fb02bd674386be2796848f4ce2e8cafac","observation_id":"b5ff1cc6-458f-49a2-8736-6d20cfb89afd","resolution":{"observed_at":"2026-08-04T20:03:35.467310Z","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":"2026-08-04T20:03:35.596217Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.596217Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:35313284a6c581c51445f9c7783dbb2f63551b4497ff5ba6a380e9120686012c","observation_id":"31683cf0-9890-4770-8c7e-20c0f932d9c3","resolution":{"observed_at":"2026-08-04T20:03:35.596217Z","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":"2026-08-04T20:03:35.687491Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.687491Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:206fbe36320c765a441ea59e75a30b9c8c18c4112d6d46d4085a0ab575e7c7b7","observation_id":"fa26dab9-f780-4288-8675-526840f275ed","resolution":{"observed_at":"2026-08-04T20:03:35.687491Z","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":"2026-08-04T20:03:35.796989Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.796989Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:048a2d6bff9728e28f606b97f744ce7147ee579f8d7bf3ec091be5f3100bfe46","observation_id":"315ea718-d661-4332-b38c-658d73b40f20","resolution":{"observed_at":"2026-08-04T20:03:35.796989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00061","last_updated":"2021-07-07T06:07:22Z","snapshot_observed_at":"2026-07-06T11:24:46.448222Z","submitted_at":"2021-06-30T19:00:25Z","title":"All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.00061","snapshot_observed_at":"2026-08-04T20:03:35.978854Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.978854Z"},"links":{"cited_paper":"/paper/2107.00061","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:c163ff9af7ef5278dd8526129ec06a2c223eeb15f7574e73dc874f3b65a0a75b","observation_id":"8c437b23-020b-4828-80ad-21f13f2a5799","resolution":{"observed_at":"2026-08-04T20:03:35.978854Z","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":"2026-08-04T20:03:35.890941Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:35.890941Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:99fbf417950cb8a29de6a1293d40e58d42ca7fb2f60e3237b5fff2eec2716ed0","observation_id":"f3f65b9d-ac98-43e5-ac80-402d26efa79c","resolution":{"observed_at":"2026-08-04T20:03:35.890941Z","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":"10.1111/hcre.12063","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Dibble, Tilo Hartmann, and Sarah F","venue":"Human Communication Research","work_id":"20053dd1-9fe6-4fbd-9bf4-47d57ef01c49","year":2016},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.208697Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:8fc665032911222f36e7f59719efa6f7ab2d2b5681b537e3b8d0d247df0dd65b","observation_id":"9acbf2ff-f71c-4d0a-8a70-1463ea12616c","resolution":{"observed_at":"2026-08-04T20:09:17.314353Z","resolver_source":"doi","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":"2304.05335","last_updated":"2023-04-11T16:53:54Z","snapshot_observed_at":"2026-08-08T22:33:30.357597Z","submitted_at":"2023-04-11T16:53:54Z","title":"Toxicity in ChatGPT: Analyzing Persona-assigned Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05335","snapshot_observed_at":"2026-08-04T20:03:36.085653Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.085653Z"},"links":{"cited_paper":"/paper/2304.05335","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:c46a8c60ff3cbce9eef6d8f07bc4042deb3de90eb6ed135efaa834f8979456a9","observation_id":"f66dce05-8dae-467b-b1d9-8818193bc4dc","resolution":{"observed_at":"2026-08-04T20:03:36.085653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08608","last_updated":"2017-03-02T19:32:10Z","snapshot_observed_at":"2026-08-07T08:20:39.814613Z","submitted_at":"2017-02-28T02:19:20Z","title":"Towards A Rigorous Science of Interpretable Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08608","snapshot_observed_at":"2026-08-04T20:03:36.506787Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.506787Z"},"links":{"cited_paper":"/paper/1702.08608","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:2a2a831b7652b1e432889a7d9cfeee2324c6ccda966207acc5e85fa20d881ca9","observation_id":"8e6906b2-ea0f-42b8-afc4-43245413e7eb","resolution":{"observed_at":"2026-08-04T20:03:36.506787Z","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":"2026-08-04T20:03:36.299730Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.299730Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:8221082e93d20e9071a93a0df77ef13204642184c4600e556a4b1e9d038a16ab","observation_id":"816fdf55-4a64-48a0-96ae-9d05b399fdbc","resolution":{"observed_at":"2026-08-04T20:03:36.299730Z","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":"2026-08-04T20:03:36.753398Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.753398Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f002d1a4819cf88020ed090aa28b98320d942e89b6f9e2e7542c1ccc5d91ca26","observation_id":"d0418aaa-b9ef-4f7c-8c1d-3e5e2fc5b5b6","resolution":{"observed_at":"2026-08-04T20:03:36.753398Z","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":"10.1145/3392845","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Feuston, Alex S","venue":"Proceedings of the ACM on Human-Computer Interaction","work_id":"18102a66-6b09-43ba-9de2-0ec80686f49b","year":2020},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.864265Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:7b0f9801d007b2e360e18173c5bf08e52972c1d39ae8556d2bef0a3a7b9672c5","observation_id":"4fc3210c-b066-4ece-87ad-628a7208bb8d","resolution":{"observed_at":"2026-08-04T20:09:17.285056Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:03:36.635150Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.635150Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:45088b598603fdb8559e0f524ce2b6c82f36f5a9dd7d6aecfa75929fff1a4625","observation_id":"728e0017-1acc-45c5-ba70-3e744bc2b186","resolution":{"observed_at":"2026-08-04T20:03:36.635150Z","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":"2026-08-04T20:03:37.043007Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.043007Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:72888ec7250bf934692b8fb8ec63009b9f5cbc5386f0b9af6eb50d79042bd9bf","observation_id":"4b83b838-12f1-4794-9201-c72705381f1f","resolution":{"observed_at":"2026-08-04T20:03:37.043007Z","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":"2026-08-04T20:03:37.134037Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.134037Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:0af8ebb61bd45ce696ac614fc1b130f9c3399b0fcba0aaecb66c4f8e9e33d176","observation_id":"28cffac3-6387-4007-999b-fd55096c1455","resolution":{"observed_at":"2026-08-04T20:03:37.134037Z","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":"2026-08-04T20:03:36.951390Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:36.951390Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:e6aa6d9d33ea847d0465bec6f2d5602d5171ebe6275239b2f1d5e9b7e5266151","observation_id":"54f6f6e5-058c-4a62-b235-bd4e3e3a1d2d","resolution":{"observed_at":"2026-08-04T20:03:36.951390Z","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":"2026-08-04T20:03:37.293081Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.293081Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:031204a0fece86f60eafcb30c712f7bab2e7abb58b961106f557c71096ea2146","observation_id":"117efd84-cf8f-4eff-84a2-e07bc635cb44","resolution":{"observed_at":"2026-08-04T20:03:37.293081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04246","last_updated":"2023-01-10T23:42:30Z","snapshot_observed_at":"2026-08-10T16:10:21.882538Z","submitted_at":"2023-01-10T23:42:30Z","title":"Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04246","snapshot_observed_at":"2026-08-04T20:03:37.388902Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.388902Z"},"links":{"cited_paper":"/paper/2301.04246","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:9bc24894fe34fc1e70fbc8eefd4c4159c0f186b692053913fcd13ab9c59972b5","observation_id":"4cab010c-f0f3-4132-abdc-46ce4e19ac9b","resolution":{"observed_at":"2026-08-04T20:03:37.388902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.11462","last_updated":"2020-09-25T20:22:26Z","snapshot_observed_at":"2026-08-09T05:10:26.091710Z","submitted_at":"2020-09-24T03:17:19Z","title":"RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.11462","snapshot_observed_at":"2026-08-04T20:03:37.208830Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.208830Z"},"links":{"cited_paper":"/paper/2009.11462","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:49ab756c6f60955584862c8252152d73f74a4e3fc9d527641a66ec33a04f5eab","observation_id":"6432c9ae-020f-40d1-89fd-cf5b1cd8af91","resolution":{"observed_at":"2026-08-04T20:03:37.208830Z","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":"2026-08-04T20:03:37.560657Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.560657Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:110d39223519313b6c9cbfe65ece1e7612bbe8e777ee6ce1bbcc62e498a3147d","observation_id":"753a3711-d33a-47d3-8c83-b350661c64db","resolution":{"observed_at":"2026-08-04T20:03:37.560657Z","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":"2026-08-04T20:03:37.601649Z","title":"Don’t Forget the Teachers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.601649Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:819ae7e4a7a635e5202006db574cb5574dc58c63c10bdb907cda7fa160b5f0f2","observation_id":"7cbba56e-09e2-47d9-89cd-28e34b868b47","resolution":{"observed_at":"2026-08-04T20:03:37.601649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05794","last_updated":"2022-03-11T08:35:15Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T08:35:15Z","title":"BERTopic: Neural topic modeling with a class-based TF-IDF procedure","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05794","snapshot_observed_at":"2026-08-04T20:03:37.467477Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.467477Z"},"links":{"cited_paper":"/paper/2203.05794","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:33e5f84db162bd93391a0963b3ea43f7b4fa5d9f4418a5ceec0ab911fe676cd3","observation_id":"6e049201-dc12-420c-abff-56aefd656173","resolution":{"observed_at":"2026-08-04T20:03:37.467477Z","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":"2026-08-04T20:03:37.746747Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.746747Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:fb5e1c8a2c6ad8025c38e98dd941d949551517a4eda9ddbec8ff6a9f9b3d2571","observation_id":"e4dc2bbe-c48d-432a-874b-202e1e6861fe","resolution":{"observed_at":"2026-08-04T20:03:37.746747Z","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":"2026-08-04T20:03:37.833482Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.833482Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:479c73aed69dd2ee9a273a743cb5687543bdb019b1df974537523330fcaf8c77","observation_id":"133b3fa0-9d9c-4d83-9323-c5b8aa735286","resolution":{"observed_at":"2026-08-04T20:03:37.833482Z","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":"2026-08-04T20:03:37.673864Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.673864Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b5051cf9b2dd91edab728133b8466b849bd678528a17da72c53f47e75716e8c6","observation_id":"b1e22762-30f1-4f23-8449-b06ee407271c","resolution":{"observed_at":"2026-08-04T20:03:37.673864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10186","last_updated":"2019-05-08T18:05:56Z","snapshot_observed_at":"2026-07-06T07:35:40.542174Z","submitted_at":"2019-02-26T19:59:15Z","title":"Attention is not Explanation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10186","snapshot_observed_at":"2026-08-04T20:03:37.996578Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.996578Z"},"links":{"cited_paper":"/paper/1902.10186","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:86ede9cd47fb5c70ccb690741041421dcec43488b398155cdbb5583be57ff375","observation_id":"53894ab3-1bf5-43c7-be10-2e3618e63299","resolution":{"observed_at":"2026-08-04T20:03:37.996578Z","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":"2026-08-04T20:03:38.094253Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.094253Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:2536b697cde1d7696d9f1857107754a5514777159d1778085702a612e960e003","observation_id":"2306bbe6-1b75-4be2-b425-9034b49ef7ce","resolution":{"observed_at":"2026-08-04T20:03:38.094253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11370","last_updated":"2024-05-20T19:23:52Z","snapshot_observed_at":"2026-08-05T21:05:47.042698Z","submitted_at":"2024-04-17T13:30:45Z","title":"Characterizing and modeling harms from interactions with design patterns in AI interfaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11370","snapshot_observed_at":"2026-08-04T20:03:37.918421Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:37.918421Z"},"links":{"cited_paper":"/paper/2404.11370","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:342ac486272cb61bda39e6d3fec218f31c0c666f1d77fe6bad6154b858688d52","observation_id":"6bb9488b-ae73-4673-bbac-b9b9de3b1ebf","resolution":{"observed_at":"2026-08-04T20:03:37.918421Z","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":"2026-08-04T20:03:38.284379Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.284379Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:25897239dd81cb69174af879bd7ab8388ba720d76a3b63f9413eabedc11fb497","observation_id":"79cb6c57-52d3-4c21-a084-81a8c952175d","resolution":{"observed_at":"2026-08-04T20:03:38.284379Z","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":"2026-08-04T20:03:38.461474Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.461474Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:1b30b93616f9534edb13bee65b3087687a3159820363e1f583b1b159f140910a","observation_id":"7f52be8a-48c4-4f3b-bc4d-e87da74b1fa8","resolution":{"observed_at":"2026-08-04T20:03:38.461474Z","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":"2026-08-04T20:03:38.171454Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.171454Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:e8057c9e1f2e605a921016a2f30938b72ccbfc340abde0b47fbad2aa4c90782a","observation_id":"914c6634-b26d-464c-968b-96fd6a0f0bb2","resolution":{"observed_at":"2026-08-04T20:03:38.171454Z","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":"2026-08-04T20:03:38.642092Z","title":"I’m Not Sure, But","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.642092Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:028ceaff2adeef500144c06d82802c0bb6f392c5cf7dd411d28f4d38e31033bf","observation_id":"712a32f6-0159-4e0e-ad5a-6463c42644a8","resolution":{"observed_at":"2026-08-04T20:03:38.642092Z","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":"2026-08-04T20:03:38.369201Z","title":"InFindings of the Association for Computational Linguistics: EMNLP 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.369201Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:34d9fc5f67cd7205c5ced7bb32da0b7652ee86be67b57744c1b5d16ff19f851a","observation_id":"e4427357-32a0-476a-86a0-da8475af571c","resolution":{"observed_at":"2026-08-04T20:03:38.369201Z","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":"2026-08-04T20:03:38.822959Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.822959Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:5dc7611488bcf7d7427ccf5687035fe5cf3572b0961b42d8ae404815672d4420","observation_id":"56644d67-713a-465d-8dc4-ae9b2e8d3cb0","resolution":{"observed_at":"2026-08-04T20:03:38.822959Z","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":"2026-08-04T20:03:38.558624Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.558624Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:79e4e4a4dbad059f6378ba3ca2284d2cda2d181380e29a1cda87d389ff3d5fd8","observation_id":"31b70391-e90b-4c46-8445-b5c1483009b7","resolution":{"observed_at":"2026-08-04T20:03:38.558624Z","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":"2026-08-04T20:03:39.079769Z","title":"If the machine is as good as me, then what use am I?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.079769Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f4f9c35b9475d4ff567f179e98921d4dc663d6a28d95fe215e9842bbb0c7bfea","observation_id":"848710c1-37d4-4298-a015-53edaca45896","resolution":{"observed_at":"2026-08-04T20:03:39.079769Z","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":"2026-08-04T20:03:38.732297Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.732297Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:24a4e0b6722640c0b9059ecfc4a9c17c82f51660cee39eb0bde8f167866c3193","observation_id":"9008f93f-727e-4398-9c6a-1196ac1007a5","resolution":{"observed_at":"2026-08-04T20:03:38.732297Z","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":"2026-08-04T20:03:39.237299Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.237299Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:fcb7820480ee8b9b381f4ca7a244ad17bfd011b07ad1098f61bd216547bc3882","observation_id":"9a4c4791-9733-43c5-b495-81e73f9abfe9","resolution":{"observed_at":"2026-08-04T20:03:39.237299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16273","last_updated":"2025-08-04T20:39:19Z","snapshot_observed_at":"2026-08-07T16:00:05.749609Z","submitted_at":"2025-04-22T21:11:47Z","title":"From Promising Capability to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16273","snapshot_observed_at":"2026-08-04T20:03:39.320592Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.320592Z"},"links":{"cited_paper":"/paper/2504.16273","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:58071495d3170c7b132f2a6cde5ed795f80d4577272e6827bec30f9708c8d898","observation_id":"0183669f-c079-460f-b6eb-26681efb02ca","resolution":{"observed_at":"2026-08-04T20:03:39.320592Z","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":"2026-08-04T20:03:38.989730Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:38.989730Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:cd2def15d7760ac9b94c25caea8cccc3add8751e97bc95765896242b7b2b12f9","observation_id":"a68349c0-3431-485f-a20c-48eefb79b4af","resolution":{"observed_at":"2026-08-04T20:03:38.989730Z","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":"2026-08-04T20:03:39.541391Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.541391Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:78fe3e9bb309e91331dc362f158679c1236474d04c8a9025b2a7ca2fe25bd2cf","observation_id":"bb8ade93-1276-4c51-a73b-0956822c6503","resolution":{"observed_at":"2026-08-04T20:03:39.541391Z","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":"2026-08-04T20:03:39.143553Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.143553Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:16f8eeb6d3092298e05528fab7f01f99e76ca63990d3635a47da031f27efdea6","observation_id":"2858c3e1-4ca4-4273-978c-7e3a929cd751","resolution":{"observed_at":"2026-08-04T20:03:39.143553Z","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":"2026-08-04T20:03:39.730005Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.730005Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:1d5a1883f98a58aa175d078c1d641b66d7abc94af31be807fdc76e2595b76dab","observation_id":"e66e3264-a361-4f37-b758-3547353f8737","resolution":{"observed_at":"2026-08-04T20:03:39.730005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22073","last_updated":"2025-06-02T19:08:46Z","snapshot_observed_at":"2026-07-06T21:31:59.333003Z","submitted_at":"2025-05-28T07:54:06Z","title":"A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22073","snapshot_observed_at":"2026-08-04T20:03:39.912894Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.912894Z"},"links":{"cited_paper":"/paper/2505.22073","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b6ef0647f086d392315d6beb09fa3abf2c55cf1567e90b47b321783af1bf003b","observation_id":"5fdecb15-c392-4668-8f45-af50c280b052","resolution":{"observed_at":"2026-08-04T20:03:39.912894Z","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":"2026-08-04T20:03:39.405253Z","title":"Cha, Shashank Ojha, and Daniel Kusbit","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.405253Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:5dbd0768326737695f49afcfd7ad9ceb9353d60042165f3ced4557ebb9d3497d","observation_id":"7a475bb1-d5f3-4c05-b055-a70ed03622b2","resolution":{"observed_at":"2026-08-04T20:03:39.405253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.01941","last_updated":"2023-08-08T01:41:22Z","snapshot_observed_at":"2026-08-04T23:34:00.991258Z","submitted_at":"2023-06-02T22:51:26Z","title":"AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.01941","snapshot_observed_at":"2026-08-04T20:03:40.076275Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.076275Z"},"links":{"cited_paper":"/paper/2306.01941","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:124431010e92e25afa6cab60333900378bb4fdafd01c0d7cc72cb38d51eb6afc","observation_id":"7f8fd8d8-a1d0-438d-bad0-e58f49ef5f69","resolution":{"observed_at":"2026-08-04T20:03:40.076275Z","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":"2026-08-04T20:03:40.153410Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.153410Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f5b88865b552ac047bfa02c3360c13b4da9ed5bf044ff3699231f10c0b53a3be","observation_id":"17306c25-fdae-427b-8c87-07f145ef590d","resolution":{"observed_at":"2026-08-04T20:03:40.153410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.01608","last_updated":"2026-05-15T21:06:13Z","snapshot_observed_at":"2026-07-06T22:06:58.031276Z","submitted_at":"2025-08-03T06:04:33Z","title":"From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.01608","snapshot_observed_at":"2026-08-04T20:03:39.642932Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.642932Z"},"links":{"cited_paper":"/paper/2508.01608","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:5869ab19b9a70b69e95a512118ca406e6bea2219c6633b3b6033b67e5ef92a7d","observation_id":"6df13a73-e2cf-4476-b840-9719b0eb93ea","resolution":{"observed_at":"2026-08-04T20:03:39.642932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10430","last_updated":"2025-04-14T17:20:34Z","snapshot_observed_at":"2026-08-07T16:05:47.962526Z","submitted_at":"2025-04-14T17:20:34Z","title":"LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10430","snapshot_observed_at":"2026-08-04T20:03:40.360991Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.360991Z"},"links":{"cited_paper":"/paper/2504.10430","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:3edbb7f8e4cd5b8418c6f07884990d2dd869e06a042e2450712fd718aa039b7d","observation_id":"869f3e44-62da-4319-96d2-87cbed42d3a5","resolution":{"observed_at":"2026-08-04T20:03:40.360991Z","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":"2026-08-04T20:03:40.444266Z","title":"How advertiser-friendly is my video?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.444266Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:7135d790e1e4586cefd632dd33e50044dfbeac2f02afc0912f5b29d66f5b77a6","observation_id":"b9171f49-d62c-4112-8235-9c881fbd2f86","resolution":{"observed_at":"2026-08-04T20:03:40.444266Z","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":"2026-08-04T20:03:40.524858Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.524858Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:e277be3fc421441a9000905736c4d3520d72e43157bf73b4e7de593fedb949e9","observation_id":"37c19556-0976-47ea-b669-c0f8e746275d","resolution":{"observed_at":"2026-08-04T20:03:40.524858Z","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":"2026-08-04T20:03:39.986548Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:39.986548Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b0c3b1d91b51b043826590b3013b643e47666136b45ed60e85fde3dc3fd811d6","observation_id":"a855d961-e38d-4695-b826-7ecca36275ff","resolution":{"observed_at":"2026-08-04T20:03:39.986548Z","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":"2026-08-04T20:03:40.650467Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.650467Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:ccae66eee3f586b86731ab011ed5685d559cc18d0a3ab435909b9962996fb8ad","observation_id":"044aabeb-5d87-40a5-b3ec-cb8580a40336","resolution":{"observed_at":"2026-08-04T20:03:40.650467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-04T20:03:40.683643Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.683643Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:5e43bc1a6fcd3d045d87053fef9b000dfc24ff817438dded7d6bc83ba2322f9a","observation_id":"af144f92-5b36-4d63-9607-151ae3f06e3f","resolution":{"observed_at":"2026-08-04T20:03:40.683643Z","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":"2026-08-04T20:03:40.208191Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.208191Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b781568b3c6af2271452b7227179a1371844cb0d40f61f3190752778f86e5670","observation_id":"072cf44c-f479-4bb9-bf2c-a1582b7ffda5","resolution":{"observed_at":"2026-08-04T20:03:40.208191Z","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":"2026-08-04T20:03:40.266944Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.266944Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f4b3f79dff5ddbcf559169c2c3dedcdf900f70b074f19ebc34a19184ad4cdb39","observation_id":"92cd1bc4-7dad-4f82-852e-9befc90b9190","resolution":{"observed_at":"2026-08-04T20:03:40.266944Z","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":"2026-08-04T20:03:40.830266Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.830266Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:fe1d534b076996587ef15f04e8a849862ab2494faa5d00702ff63d597232a095","observation_id":"c96da0f8-dd74-4d17-abb6-e394437c294e","resolution":{"observed_at":"2026-08-04T20:03:40.830266Z","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":"2026-08-04T20:03:40.875507Z","title":"2024.PRA W: The Python Reddit API Wrapper","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.875507Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b96bf58871e9d9810e0092d04c41e7bb9e3f27413725aff8d3146acd07919e87","observation_id":"ef89d0bb-3503-4bf7-a400-2843164a7193","resolution":{"observed_at":"2026-08-04T20:03:40.875507Z","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":"2026-08-04T20:03:40.931196Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.931196Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:7ed033be720fb4985896850f98e7192ba2b8692eb0e52aeaebcd77737b5497fe","observation_id":"748b8380-6c9a-46c3-aae0-08b103f60229","resolution":{"observed_at":"2026-08-04T20:03:40.931196Z","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":"2026-08-04T20:03:40.606673Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.606673Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:d6e8c6ae1f418d502f1c4a201085d791f9337d145d6fef80d6457ac061863169","observation_id":"d781164c-73f1-43a8-ad1a-c300dc762f7c","resolution":{"observed_at":"2026-08-04T20:03:40.606673Z","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":"2026-08-04T20:03:41.028326Z","title":"Why should i trust you?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.028326Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:1e4919091d36ae1d0432d82a34fad5379c2081cd1c59e4061670f8ce287177f9","observation_id":"8dc3b058-ab58-48f9-94f8-36540ab440d6","resolution":{"observed_at":"2026-08-04T20:03:41.028326Z","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":"2026-08-04T20:03:41.083531Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.083531Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:29b5752cbb973c467605393b155e6f23da9e7183ed6dabf3b65d2c536de1a200","observation_id":"46212dcf-1cba-4375-9040-462bd8ebf8de","resolution":{"observed_at":"2026-08-04T20:03:41.083531Z","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":"2026-08-04T20:03:40.732868Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.732868Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:7c722afcf0c533de43d9b0821b3db50bdfc91b005fd6b3fb5f92d0f436f387bd","observation_id":"b4b44ecb-8370-4d80-bd75-f58160af159a","resolution":{"observed_at":"2026-08-04T20:03:40.732868Z","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":"2026-08-04T20:03:40.785153Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.785153Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:84533b3c3bbf5bd0c92466d0bdc71df40c5e7ac7a402503f071d2a894bdcab71","observation_id":"5fd7114a-b5cd-42b1-9d3a-94b629a8eb8e","resolution":{"observed_at":"2026-08-04T20:03:40.785153Z","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":"2026-08-04T20:03:41.201488Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.201488Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:50eeb363ba12f9776d240c7ecabeb46dfca27109c92b27906d4382dbfe60c2cb","observation_id":"161ea619-9f8b-4b9d-84c9-54e29975b29b","resolution":{"observed_at":"2026-08-04T20:03:41.201488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04247","last_updated":"2025-07-21T18:59:21Z","snapshot_observed_at":"2026-08-09T23:56:11.727400Z","submitted_at":"2024-02-06T18:54:07Z","title":"Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04247","snapshot_observed_at":"2026-08-04T20:03:41.233789Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.233789Z"},"links":{"cited_paper":"/paper/2402.04247","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:c40fbaf9306ec3a53c4bd550dba595c7754dc6f264f30f59cdf03f6e33a6abe0","observation_id":"8b118f0a-f11f-4ee1-b76e-4776b1d86899","resolution":{"observed_at":"2026-08-04T20:03:41.233789Z","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":"2026-08-04T20:03:41.262271Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.262271Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:a81071d873af2e6adf8e78edeea647841ec0ebbb129786e2119b6bf4ef8bce92","observation_id":"be37c88d-1ce3-4471-bee2-3b6521ecdda4","resolution":{"observed_at":"2026-08-04T20:03:41.262271Z","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":"2026-08-04T20:03:40.983561Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:40.983561Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:df5ba6baaf5e3e5c58022c3ee6c325ae223e07713da752f98b61d2d05f048471","observation_id":"819456d1-8423-47c5-83d4-640b02cc2d2f","resolution":{"observed_at":"2026-08-04T20:03:40.983561Z","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":"2026-08-04T20:03:41.344907Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.344907Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:c93377207d22fdb252b2bd987cee21ab099eec66459084120b78df7076f7d25a","observation_id":"dab4bb81-157d-42d8-8a30-c18815c44777","resolution":{"observed_at":"2026-08-04T20:03:41.344907Z","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":"2026-08-04T20:03:41.393858Z","title":null,"venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.393858Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:a3057c02dc403bae65acd5999200cd6b341c7f796b15d5db1abfd4a6fb3caed7","observation_id":"1ff9e7a1-21d6-4ec5-a844-06bcf7816166","resolution":{"observed_at":"2026-08-04T20:03:41.393858Z","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":"2026-08-04T20:03:41.141291Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.141291Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:5fef4fc38301b781e0e563d9ebf427630c3d57a1eb83efcb51621cd3a20e6316","observation_id":"6befa2f2-bd92-4feb-a057-0514fb3e6b7a","resolution":{"observed_at":"2026-08-04T20:03:41.141291Z","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":"2026-08-04T20:03:41.172026Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.172026Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:28946fdea98038268b1d5df55d7456ff0f98478920d5dd26287a4a3437ec33f2","observation_id":"582dab19-9170-42c9-836b-17825544318b","resolution":{"observed_at":"2026-08-04T20:03:41.172026Z","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":"2026-08-04T20:03:41.483435Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.483435Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:fe704e9fba38fa8f9416bedf68254efcc6b4783e905f26a89726d9b4da89c371","observation_id":"48b4f305-9943-4d4a-b270-c3d2afdc319e","resolution":{"observed_at":"2026-08-04T20:03:41.483435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.01319","last_updated":"2024-08-02T15:14:53Z","snapshot_observed_at":"2026-08-10T16:01:55.237902Z","submitted_at":"2024-08-02T15:14:53Z","title":"A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.01319","snapshot_observed_at":"2026-08-04T20:03:41.564574Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.564574Z"},"links":{"cited_paper":"/paper/2408.01319","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:0dd826d13fc4bed56966cd0348e6ac370438ffd5cc0296a245519fc8c2d92bb2","observation_id":"ecf60b7e-919c-4b4f-a7d3-9b1be7634972","resolution":{"observed_at":"2026-08-04T20:03:41.564574Z","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":"2026-08-04T20:03:41.609101Z","title":"Do you trust me?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.609101Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:a84fa26819f0779f1fe86e799bd999eb8fbd5fd32b843802fb4bc6492b7e1d52","observation_id":"936ee9ce-b129-46fe-8f9e-7b97cc37cb09","resolution":{"observed_at":"2026-08-04T20:03:41.609101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04621","last_updated":"2024-06-06T06:10:00Z","snapshot_observed_at":"2026-08-09T03:52:31.887865Z","submitted_at":"2024-01-09T15:46:38Z","title":"DebugBench: Evaluating Debugging Capability of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04621","snapshot_observed_at":"2026-08-04T20:03:41.299432Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.299432Z"},"links":{"cited_paper":"/paper/2401.04621","citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:a18698fc45ec1d2f74d064599558189e7af7357204c2b171dbd8d89baabc778e","observation_id":"b8c8c4a5-4049-4f33-924b-7b85b2f13444","resolution":{"observed_at":"2026-08-04T20:03:41.299432Z","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":"2026-08-04T20:03:41.684459Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.684459Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:9ce36127b20dce0b424905fd62c29f2244cf1a013ccf9da1dd92f447951fd9fb","observation_id":"44c611bc-9f13-4d68-b956-2e8f5b0f32dc","resolution":{"observed_at":"2026-08-04T20:03:41.684459Z","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":"2026-08-04T20:03:41.786161Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.786161Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:4bbf9c3e628ffdd54b43f2c5c9d96c13604eba1d8733b725854cd7e333f27795","observation_id":"7ccf30f2-3f27-4833-b76c-360bb9ecd1cb","resolution":{"observed_at":"2026-08-04T20:03:41.786161Z","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":"2026-08-04T20:03:41.408152Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.408152Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:b8ec4e42d35a5e6aa6e17e87f5e0958ab83144dfbceb6503199160034b729b7f","observation_id":"189d285d-82b8-4925-bb04-c902da0fe7cc","resolution":{"observed_at":"2026-08-04T20:03:41.408152Z","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":"2026-08-04T20:03:41.425100Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\"","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-04T20:03:41.425100Z"},"links":{"citing_paper":"/paper/2509.08912"},"observation_digest":"sha256:f148c9e5318197b46f7d4b821efda893c6c94d7f03fd1800642cbf5ee3e56747","observation_id":"009107c5-d2e2-4491-b0a3-098b3d82a057","resolution":{"observed_at":"2026-08-04T20:03:41.425100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.08912","last_updated":"2025-09-10T18:16:46Z","latest_version":1,"primary_category":"cs.CY","snapshot_observed_at":"2026-08-07T20:13:30.269872Z","submitted_at":"2025-09-10T18:16:46Z","title":"Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs \"In the Wild\""},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":98,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":116},"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 100 of 116 outbound references and 0 inbound Pith citation observations for arXiv:2509.08912."}