{"as_of":"2026-08-07T09:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ed87974bbf624236fa8c798f27bfcf5ff110a5b54503a9e7c90bb5f457da36dc","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T04:50:11.457129Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2508.02997/citation-record","integrity":"/paper/2508.02997/integrity","json":"/paper/2508.02997/citation-record.json","paper":"/paper/2508.02997"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-02T19:23:53.535075Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-06T04:50:09.167346Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.167346Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:686978110c3269ee61f40bd28ef4c3ca4b4e39d3efd2b8bb7055f4662bcc81a3","observation_id":"1b60e0a0-ab38-455e-89a3-2c32b7e908d3","resolution":{"observed_at":"2026-08-06T04:50:09.167346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05128","last_updated":"2023-10-05T09:12:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-11T10:43:43Z","title":"Teaching Large Language Models to Self-Debug","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05128","snapshot_observed_at":"2026-08-06T04:50:09.233179Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.233179Z"},"links":{"cited_paper":"/paper/2304.05128","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:837e32c5da387582d1bb6886dc96486b8b9c146bdcfb457338795c971890f77e","observation_id":"625c3eb4-5062-4bab-8d07-44a6c839d1a8","resolution":{"observed_at":"2026-08-06T04:50:09.233179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T04:50:09.340276Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.340276Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:5687cc407ab355418de9d9270810ea5ffc252fcd764a5b3f8842c4404641872c","observation_id":"6219e041-5cb9-4b36-903a-353e4645a0f6","resolution":{"observed_at":"2026-08-06T04:50:09.340276Z","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":"2026-08-06T04:50:09.417530Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.417530Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:2432944f4ef992f5e3f03ba5a8d3bb6d873e8b2b2623fe8c4149a6a40bd0d500","observation_id":"b5451de2-b14f-4b1d-973f-b135398e3052","resolution":{"observed_at":"2026-08-06T04:50:09.417530Z","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":"2026-08-06T04:50:09.486827Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.486827Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:6597280d3a744397eec9ca466224c557b09f2ffcc5b0004277b664ecd7e3ccd4","observation_id":"2fc54e83-8c35-4f47-b74c-f90440e2a2f4","resolution":{"observed_at":"2026-08-06T04:50:09.486827Z","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":"2026-08-06T04:50:09.626530Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.626530Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:07e8be74fa92f57affbd450ff12a22deb487f72169fe417bd93ff4d3e5b61458","observation_id":"08edfa25-c01c-4812-a319-06b0b81273b9","resolution":{"observed_at":"2026-08-06T04:50:09.626530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01861","last_updated":"2023-08-14T09:07:00Z","snapshot_observed_at":"2026-08-03T22:16:12.497107Z","submitted_at":"2023-08-03T16:31:02Z","title":"ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01861","snapshot_observed_at":"2026-08-06T04:50:09.670033Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.670033Z"},"links":{"cited_paper":"/paper/2308.01861","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:e0a623a01bbff1d5e25b8253d0ee5deb57a3b844f82194328a83cedb35bd5e83","observation_id":"56f89262-56c3-4dc6-8d5f-6ce74f4bc4d0","resolution":{"observed_at":"2026-08-06T04:50:09.670033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05999","last_updated":"2023-04-09T14:31:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-12T16:25:26Z","title":"InCoder: A Generative Model for Code Infilling and Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05999","snapshot_observed_at":"2026-08-06T04:50:09.738567Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.738567Z"},"links":{"cited_paper":"/paper/2204.05999","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:703fcd933e368eb9be6a5aa97f5d9355b7ab82286a939e9244844be19d38eaf5","observation_id":"d253d973-85cf-4ae4-9af6-32510839fc5b","resolution":{"observed_at":"2026-08-06T04:50:09.738567Z","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":"2026-08-06T04:50:09.804523Z","title":"2024.Copilot","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.804523Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:868fb71e0c4ac36187a03602b01cd0294bd0f9caf48f85f9c94c866a6bc47d13","observation_id":"730f7699-356e-47ce-b280-176f9d036f8c","resolution":{"observed_at":"2026-08-06T04:50:09.804523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12950","last_updated":"2024-01-31T19:47:26Z","snapshot_observed_at":"2026-07-06T16:10:07.931347Z","submitted_at":"2023-08-24T17:39:13Z","title":"Code Llama: Open Foundation Models for Code","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12950","snapshot_observed_at":"2026-08-06T04:50:09.878397Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.878397Z"},"links":{"cited_paper":"/paper/2308.12950","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:fe9b9a440816f718c0de06f38cb19b97cad9d46cff84b2c750a014c44261e452","observation_id":"0a8776ff-9193-467e-8a94-33e38674ba64","resolution":{"observed_at":"2026-08-06T04:50:09.878397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14196","last_updated":"2024-01-26T09:23:11Z","snapshot_observed_at":"2026-08-06T22:40:28.707813Z","submitted_at":"2024-01-25T14:17:53Z","title":"DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14196","snapshot_observed_at":"2026-08-06T04:50:09.968783Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.968783Z"},"links":{"cited_paper":"/paper/2401.14196","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:3c6f4907a8fc098e1f2c4fa56f4c1221880e801ea972bc6d984653ec09a7ec29","observation_id":"c077f6bf-90b0-451f-8612-318b93611e64","resolution":{"observed_at":"2026-08-06T04:50:09.968783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.09938","last_updated":"2021-11-08T21:16:44Z","snapshot_observed_at":"2026-08-04T23:13:25.514661Z","submitted_at":"2021-05-20T17:58:42Z","title":"Measuring Coding Challenge Competence With APPS","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.09938","snapshot_observed_at":"2026-08-06T04:50:10.057478Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.057478Z"},"links":{"cited_paper":"/paper/2105.09938","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:709a96a240438341532a8b9779ddd7a69e5538acaff4eb4492661ab0f46361d9","observation_id":"c952b424-b620-4eef-b93d-bb4851a32b34","resolution":{"observed_at":"2026-08-06T04:50:10.057478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06770","last_updated":"2024-11-11T23:05:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T16:47:29Z","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06770","snapshot_observed_at":"2026-08-06T04:50:10.122298Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.122298Z"},"links":{"cited_paper":"/paper/2310.06770","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:e26b807fdef77b941db25bea9b288b4e77fbdbc1019e36928531eb35b0de03c6","observation_id":"68c2ba9f-832c-4508-bab7-f9af7d99fe18","resolution":{"observed_at":"2026-08-06T04:50:10.122298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.03004","last_updated":"2023-11-06T07:16:58Z","snapshot_observed_at":"2026-07-06T14:59:03.634181Z","submitted_at":"2023-03-06T10:08:51Z","title":"xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.03004","snapshot_observed_at":"2026-08-06T04:50:10.207313Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.207313Z"},"links":{"cited_paper":"/paper/2303.03004","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:eabb1622a6c1d3a12d35bbc0c4efb9d3961ca325adc1b8dbec3f65405bf52799","observation_id":"490a725b-39aa-4a54-9d87-eaad4a51d8d7","resolution":{"observed_at":"2026-08-06T04:50:10.207313Z","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":"2026-08-06T04:50:10.298089Z","title":"Gonzalez, Hao Zhang, and Ion Stoica","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.298089Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:c13caf2418724e9fe573fa6dc2548aa54ff53b4960de2e4f081b0d5af100699c","observation_id":"84bd3359-2ab3-458d-8d1c-c9915553bb4f","resolution":{"observed_at":"2026-08-06T04:50:10.298089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17428","snapshot_observed_at":"2026-08-06T04:50:10.360634Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.360634Z"},"links":{"cited_paper":"/paper/2405.17428","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:b05dc35b168e42874e47c5fc2df6cdc24c227b3ffacbc00a36fe2e96c82b0f7f","observation_id":"79ba02aa-58c1-4488-beba-11db4c01afe4","resolution":{"observed_at":"2026-08-06T04:50:10.360634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00599","last_updated":"2024-03-31T08:10:50Z","snapshot_observed_at":"2026-08-06T04:29:52.164901Z","submitted_at":"2024-03-31T08:10:50Z","title":"EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00599","snapshot_observed_at":"2026-08-06T04:50:10.449465Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.449465Z"},"links":{"cited_paper":"/paper/2404.00599","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:a3cce383ff63131f226e9fa162368cc1be15ef1a62889b57e47bfc33d7f2c550","observation_id":"2cf70bed-8811-4b51-adfc-0d11a8f73c86","resolution":{"observed_at":"2026-08-06T04:50:10.449465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03091","last_updated":"2023-10-04T01:13:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-05T17:59:41Z","title":"RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03091","snapshot_observed_at":"2026-08-06T04:50:10.516832Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.516832Z"},"links":{"cited_paper":"/paper/2306.03091","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:be738236812ff448fed904837a8ac91d6b8c51a00be7ed15bf9f18685178885b","observation_id":"6c477ef1-30ed-4a73-a47e-d6e507da45ba","resolution":{"observed_at":"2026-08-06T04:50:10.516832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19173","last_updated":"2024-02-29T13:53:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-29T13:53:35Z","title":"StarCoder 2 and The Stack v2: The Next Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19173","snapshot_observed_at":"2026-08-06T04:50:10.603590Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.603590Z"},"links":{"cited_paper":"/paper/2402.19173","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:e946e403c11f6ba1dbcf0a61a25f63ee6491dddbaafbda60de066a14bd6f46e8","observation_id":"af796a5f-f0b6-4d16-a43c-3e75e1bbdc95","resolution":{"observed_at":"2026-08-06T04:50:10.603590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04664","last_updated":"2021-03-16T08:28:37Z","snapshot_observed_at":"2026-07-06T10:39:42.676631Z","submitted_at":"2021-02-09T06:16:25Z","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04664","snapshot_observed_at":"2026-08-06T04:50:10.703106Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.703106Z"},"links":{"cited_paper":"/paper/2102.04664","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:1895ed399b3000f6ad4e0034df6b5f61f3f2552b2e892e127c3bf654df3626ed","observation_id":"2421d692-e67b-4b81-8f99-4b8f5bbfd266","resolution":{"observed_at":"2026-08-06T04:50:10.703106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.13474","last_updated":"2023-02-27T21:26:48Z","snapshot_observed_at":"2026-07-06T12:52:16.992962Z","submitted_at":"2022-03-25T06:55:15Z","title":"CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.13474","snapshot_observed_at":"2026-08-06T04:50:10.766003Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.766003Z"},"links":{"cited_paper":"/paper/2203.13474","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:efa5fd4235accb6ce101d09eb21db506ca009f20c355d5f77a77aef4ae765cee","observation_id":"a8ce4160-9a7d-4189-a22f-5673cf1da4c3","resolution":{"observed_at":"2026-08-06T04:50:10.766003Z","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":"2026-08-06T04:50:10.832893Z","title":"2024.ChatGPT","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.832893Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:9857bb328ef68b38c4075650a0f1d8d638b4890e4f3863ab20c5de6ee5c49e61","observation_id":"4bce0eea-3575-4527-a092-4cc9eec40ec0","resolution":{"observed_at":"2026-08-06T04:50:10.832893Z","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":"2026-08-06T04:50:10.903700Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.903700Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:6f3fd69883069144547b4fa5fcdbe09671d01156daf04bae6d3605d290e3a8ba","observation_id":"57688342-e41b-44d8-aea7-a6722e227c7c","resolution":{"observed_at":"2026-08-06T04:50:10.903700Z","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":"2026-08-06T04:50:10.978815Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:10.978815Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:58274de5e095d27d154bb424d8c7832080ef7587affd9beaafb3e3a5f05dfc92","observation_id":"1ce2af1a-ce6a-4c92-8489-12631c6fa70e","resolution":{"observed_at":"2026-08-06T04:50:10.978815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-06T04:50:11.075802Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.075802Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:c1fcc809d2e2751a3afc1492e3cadc75579f4bb04d84ea19800493765cd5c86c","observation_id":"8fcf28cc-cc02-4a9d-bda4-c861f9934d6d","resolution":{"observed_at":"2026-08-06T04:50:11.075802Z","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":"2026-08-06T04:50:11.134002Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.134002Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:ae8fb917c5fc73958a0869ef098a843b322448f0fb211364445e1369c356d6d0","observation_id":"25896465-a028-4832-9062-496ff4d7cdfb","resolution":{"observed_at":"2026-08-06T04:50:11.134002Z","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":"2026-08-06T04:50:11.193479Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.193479Z"},"links":{"citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:116b3201818ac32244c06228dac8c89bcf95a53e6caa9257961354a769eff7cc","observation_id":"c3fa5402-5d21-466b-a5f7-ef42c7783443","resolution":{"observed_at":"2026-08-06T04:50:11.193479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19287","last_updated":"2024-03-28T10:19:18Z","snapshot_observed_at":"2026-07-06T17:52:26.380307Z","submitted_at":"2024-03-28T10:19:18Z","title":"CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.19287","snapshot_observed_at":"2026-08-06T04:50:11.262325Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.262325Z"},"links":{"cited_paper":"/paper/2403.19287","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:d43003514c0721c9757f2a08c1d958b7c0443d52fae193a0630c0316888c724b","observation_id":"dd1d73c3-5a3d-4fa0-a07c-8ec66a9901ba","resolution":{"observed_at":"2026-08-06T04:50:11.262325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12570","last_updated":"2023-10-20T15:21:51Z","snapshot_observed_at":"2026-07-06T15:06:41.938212Z","submitted_at":"2023-03-22T13:54:46Z","title":"RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12570","snapshot_observed_at":"2026-08-06T04:50:11.390913Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.390913Z"},"links":{"cited_paper":"/paper/2303.12570","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:1241abcc583157e5fc06119796e26007c9f60e6e6b514d0944cbdc198935399d","observation_id":"d5c53be6-88cf-48d8-93e6-6d938ee7d5f4","resolution":{"observed_at":"2026-08-06T04:50:11.390913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07339","last_updated":"2024-08-09T06:16:55Z","snapshot_observed_at":"2026-07-06T17:15:22.551637Z","submitted_at":"2024-01-14T18:12:03Z","title":"CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07339","snapshot_observed_at":"2026-08-06T04:50:11.457129Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:11.457129Z"},"links":{"cited_paper":"/paper/2401.07339","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:4cf59a402129e4686650439b6f0ff794c2b25bbb3e5ebe956470a8786ec559f1","observation_id":"ffcd3222-ef42-4132-b689-7e504c7be5ed","resolution":{"observed_at":"2026-08-06T04:50:11.457129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.09043","last_updated":"2024-09-05T14:00:11Z","snapshot_observed_at":"2026-07-06T14:43:31.440090Z","submitted_at":"2023-01-22T02:59:59Z","title":"CodeScore: Evaluating Code Generation by Learning Code Execution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.09043","snapshot_observed_at":"2026-08-06T04:50:09.552297Z","title":"arXiv:2301.09043 (Jan","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T04:50:09.552297Z"},"links":{"cited_paper":"/paper/2301.09043","citing_paper":"/paper/2508.02997"},"observation_digest":"sha256:ecc5faf61894632485ed038b2cc303c68c1e8f705bdce69a72f66c1326e022bd","observation_id":"da2ac29a-b449-49e2-bb1c-a8162083970f","resolution":{"observed_at":"2026-08-06T04:50:09.552297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.02997","last_updated":"2025-08-27T21:24:30Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T04:49:51.019365Z","submitted_at":"2025-08-05T01:53:32Z","title":"CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":31},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.02997."}