{"as_of":"2026-08-08T10:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c4db46f83b259158806868785c8fc187e2d6373c8aabf400768adeea9bb77655","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:05:10.114257Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T05:29:52.758898Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T05:31:07.650808Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"cited_work":{"arxiv_id":"2507.01321","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.01321","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2507.01321 , year=","venue":null,"work_id":"a616b07d-99d9-41f0-9ab1-d707ee88c2d3","year":2025},"citing_paper":{"arxiv_id":"2604.04488","last_updated":"2026-04-06T07:27:04Z","snapshot_observed_at":"2026-08-05T13:21:08.142791Z","submitted_at":"2026-04-06T07:27:04Z","title":"A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T20:14:02.553313Z"},"links":{"cited_paper":"/paper/2507.01321","citing_paper":"/paper/2604.04488"},"observation_digest":"sha256:437c4e5267cc29a428146a97b7842ef3d794918da9731231e527ba150be72705","observation_id":"42a59bec-4e81-4d54-83a6-bdfcc13ff736","resolution":{"observed_at":"2026-05-10T22:05:49.264619Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"cited_work":{"arxiv_id":"2507.01321","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.01321","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2507.01321 , year=","venue":null,"work_id":"a616b07d-99d9-41f0-9ab1-d707ee88c2d3","year":2025},"citing_paper":{"arxiv_id":"2605.22365","last_updated":"2026-05-25T01:50:16Z","snapshot_observed_at":"2026-08-01T15:33:50.569569Z","submitted_at":"2026-05-21T11:58:46Z","title":"TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting","version":1},"reference_index":122,"source":"arxiv_source","source_observed_at":"2026-05-22T05:29:52.758898Z"},"links":{"cited_paper":"/paper/2507.01321","citing_paper":"/paper/2605.22365"},"observation_digest":"sha256:890739d1df8be4e478c2775052b22b247aee8e5667a04906fb2751d108da26a9","observation_id":"02bbecd6-08e6-4bba-85c1-a8ed865a5db2","resolution":{"observed_at":"2026-05-22T05:31:07.653492Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.01321/citation-record","integrity":"/paper/2507.01321/integrity","json":"/paper/2507.01321/citation-record.json","paper":"/paper/2507.01321"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2304.13276","last_updated":"2023-04-26T04:33:41Z","snapshot_observed_at":"2026-08-07T10:06:04.747785Z","submitted_at":"2023-04-26T04:33:41Z","title":"The Closeness of In-Context Learning and Weight Shifting for Softmax Regression","version":1},"cited_work":{"arxiv_id":"2304.13276","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.13276","snapshot_observed_at":"2026-08-06T21:05:10.302254Z","title":"The Closeness of In-Context Learning and Weight Shifting for Softmax Regression","venue":"cs.CL","work_id":"71d4566f-3f85-486a-b5f4-8445c1a0942b","year":2023},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.395244Z"},"links":{"cited_paper":"/paper/2304.13276","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:1389bcd5c3be842047537bcf4ffe362f8f97118cf91487400dfeabdd7278f243","observation_id":"b5416ae1-7151-4026-a1d8-1b7aa87c0ec0","resolution":{"observed_at":"2026-08-06T21:05:10.365168Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.16257","last_updated":"2024-03-24T18:33:15Z","snapshot_observed_at":"2026-08-02T13:50:10.728922Z","submitted_at":"2024-03-24T18:33:15Z","title":"Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16257","snapshot_observed_at":"2026-08-06T21:05:09.568138Z","title":"Unlearning backdoor threats: Enhancing backdoor defense in multimodal contrastive learning via local token unlearning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.568138Z"},"links":{"cited_paper":"/paper/2403.16257","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:03830accc3e8f8d8961cc76c445101622b9ef60230399c4df7a5ea91478c88e0","observation_id":"8fda8238-d536-42bc-b5bf-e3ddeb2b3706","resolution":{"observed_at":"2026-08-06T21:05:09.568138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15172","last_updated":"2023-12-23T05:51:40Z","snapshot_observed_at":"2026-08-07T10:04:56.035119Z","submitted_at":"2023-12-23T05:51:40Z","title":"Pre-trained Trojan Attacks for Visual Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15172","snapshot_observed_at":"2026-08-06T21:05:09.655796Z","title":"Pre-trained trojan attacks for visual recognition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.655796Z"},"links":{"cited_paper":"/paper/2312.15172","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:a0ecad46c991a862b1071f071e48f97cdd1d1ed416c737c85e1f96e7d65efddc","observation_id":"cadb30a9-e97c-4e14-a65f-081e82e6490a","resolution":{"observed_at":"2026-08-06T21:05:09.655796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:05:10.585529Z","title":"D., Ng, A","venue":null,"work_id":"41b3ab07-ceb2-45b0-bb18-084a325b78dc","year":2013},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.733172Z"},"links":{"citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:81bd11e143faa7a7fb57c62aa14d80e87f8bb50e1e123b1f3e4b0c7c650ff1d2","observation_id":"c44ba6ce-276e-4fbc-bd64-4e03806efe78","resolution":{"observed_at":"2026-08-06T21:05:10.663626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T21:05:09.800071Z","title":"V on Oswald, J., Niklasson, E., Randazzo, E., Sacramento, J., Mordvintsev, A., Zhmoginov, A., and Vladymyrov, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.800071Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:ac40e1f450d45112cd7385a331730b052f46999ef8ee6dd7bd18012cccbdcd88","observation_id":"cb99f3ff-b32c-448b-b4c5-5476950f04dc","resolution":{"observed_at":"2026-08-06T21:05:09.800071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.05244","last_updated":"2022-12-07T15:45:20Z","snapshot_observed_at":"2026-07-06T13:51:12.442343Z","submitted_at":"2022-09-12T13:37:06Z","title":"Universal Backdoor Attacks Detection via Adaptive Adversarial Probe","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.05244","snapshot_observed_at":"2026-08-06T21:05:09.921337Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.921337Z"},"links":{"cited_paper":"/paper/2209.05244","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:89e598f02997694c1dd2f22450ca042279878f6acb787b534b41f449417cf2a8","observation_id":"6074e046-2986-40e7-95b2-0188448af411","resolution":{"observed_at":"2026-08-06T21:05:09.921337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:05:10.445176Z","title":"Backdooring instruction- tuned large language models with virtual prompt injec- tion","venue":null,"work_id":"8ea65bec-4d8e-45e3-95b6-93d6b2fe8a73","year":2024},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:10.026693Z"},"links":{"citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:9c9e51d558ed80f9125e8388ab8d9e03f8d319927d4f72cab1d9f78e795bb268","observation_id":"c79dfe34-b94d-4272-b26f-7690fbe23ae4","resolution":{"observed_at":"2026-08-06T21:05:10.528394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05553","last_updated":"2024-07-01T10:27:42Z","snapshot_observed_at":"2026-07-06T18:11:57.100591Z","submitted_at":"2024-05-09T05:23:34Z","title":"Towards Robust Physical-world Backdoor Attacks on Lane Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05553","snapshot_observed_at":"2026-08-06T21:05:10.114257Z","title":"To- wards robust physical-world backdoor attacks on lane detection","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:10.114257Z"},"links":{"cited_paper":"/paper/2405.05553","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:a61f4bcd3460cd2909b295e276feb6088743d344a1d9d8c333e3cca9ecd11155","observation_id":"53f68876-968c-4e4b-9105-c03019e3f982","resolution":{"observed_at":"2026-08-06T21:05:10.114257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06745","last_updated":"2022-04-14T04:00:27Z","snapshot_observed_at":"2026-07-06T13:00:12.148951Z","submitted_at":"2022-04-14T04:00:27Z","title":"GPT-NeoX-20B: An Open-Source Autoregressive Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06745","snapshot_observed_at":"2026-08-06T21:05:09.164328Z","title":"If you use this software, please cite it using these metadata","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.164328Z"},"links":{"cited_paper":"/paper/2204.06745","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:4a3d1f24a7a2b3535ba5853a417dffc12425ef745aae5abdda25593dfd67f613","observation_id":"a940abab-913b-4266-895e-c9ba1acc9c89","resolution":{"observed_at":"2026-08-06T21:05:09.164328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:05:10.746399Z","title":"A survey on in- context learning","venue":null,"work_id":"37b86ed1-c33b-453e-8fd8-285453e39f46","year":2024},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.209138Z"},"links":{"citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:fe36917d8e6c6bd57fb007d5eb28c931ee36964cb13b45187d676de26ca67f81","observation_id":"d7a259a0-c9b8-4a6a-994f-f7ffbd1baab5","resolution":{"observed_at":"2026-08-06T21:05:10.866330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11473","last_updated":"2024-02-18T06:31:05Z","snapshot_observed_at":"2026-08-06T02:43:24.834998Z","submitted_at":"2024-02-18T06:31:05Z","title":"Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11473","snapshot_observed_at":"2026-08-06T21:05:09.469314Z","title":"Anti- backdoor learning: Training clean models on poisoned data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.469314Z"},"links":{"cited_paper":"/paper/2402.11473","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:8e4db76686c83808af80c7c732574d45abe44e3e6bdbbfd658aae84b28ec082b","observation_id":"2126c3dc-9a64-4c56-8ac6-1caa67e9e819","resolution":{"observed_at":"2026-08-06T21:05:09.469314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15968","last_updated":"2024-09-24T10:56:18Z","snapshot_observed_at":"2026-08-05T15:20:46.924851Z","submitted_at":"2024-09-24T10:56:18Z","title":"Adversarial Backdoor Defense in CLIP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15968","snapshot_observed_at":"2026-08-06T21:05:09.316305Z","title":"Adversarial backdoor defense in clip","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:09.316305Z"},"links":{"cited_paper":"/paper/2409.15968","citing_paper":"/paper/2507.01321"},"observation_digest":"sha256:3b837b243e451c0f2491024f906b544f5fe41cc26ec07513e335e29f3dcb8fbe","observation_id":"b6d379d5-d134-4c04-8dd0-a36dd7d8e393","resolution":{"observed_at":"2026-08-06T21:05:09.316305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.01321","last_updated":"2025-07-02T03:09:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T10:05:23.897142Z","submitted_at":"2025-07-02T03:09:20Z","title":"ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":3},"total_outbound_references":12},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2507.01321."}