{"as_of":"2026-08-08T15:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11381a00edd0f94be1e4fcd989df7a55052845e41f7fa9cd227021e974eaecd3","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":17,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:59:38.244178Z","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":"2026-07-04T09:59:45.002855Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2310.11324","last_updated":"2024-07-01T22:28:01Z","snapshot_observed_at":"2026-08-04T15:07:29.209129Z","submitted_at":"2023-10-17T15:03:30Z","title":"Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-17T01:59:51.326493Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2310.11324"},"observation_digest":"sha256:bb9e569df90890a0bd759bd6adaf669c8b91b0b4289b1c1db15af065fb97a864","observation_id":"fa491dbd-8986-483e-8de1-53b530d9f23e","resolution":{"observed_at":"2026-05-17T01:59:51.403617Z","resolver_source":"arxiv_id","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-07T14:59:38.244178Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16785","last_updated":"2025-05-22T15:28:25Z","snapshot_observed_at":"2026-08-07T14:52:56.103504Z","submitted_at":"2025-05-22T15:28:25Z","title":"CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T14:59:38.244178Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2505.16785"},"observation_digest":"sha256:c24a91caab9868b4dfd0cc8be8222f86aa16eda1b6616f941b71971bb83ad330","observation_id":"b363b654-5d11-4767-ab56-ffb7ec8526d7","resolution":{"observed_at":"2026-08-07T14:59:38.244178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2506.04390","last_updated":"2026-05-22T04:35:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-04T19:15:09Z","title":"Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T08:10:31.473565Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2506.04390"},"observation_digest":"sha256:b65f5e7bd5567374baf2e8bd95ea16399d4d3036ffcccd1f68c878ce3f26b468","observation_id":"973ecd88-25c3-48d8-bd5d-3f1d2c4e56b4","resolution":{"observed_at":"2026-05-25T08:15:34.132065Z","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T20:44:59.061295Z","title":"Smith, and Luke Zettlemoyer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01844","last_updated":"2025-07-02T15:58:51Z","snapshot_observed_at":"2026-08-08T14:54:47.103236Z","submitted_at":"2025-07-02T15:58:51Z","title":"Low-Perplexity LLM-Generated Sequences and Where To Find Them","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T20:44:59.061295Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.01844"},"observation_digest":"sha256:10b302ce7906a12047b1116cc738b0fdcc103adcae0c1927c145d2606826315c","observation_id":"c0996a94-20ab-416e-bf13-3c7d4809bebc","resolution":{"observed_at":"2026-08-06T20:44:59.061295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2507.05660","last_updated":"2026-05-21T16:31:21Z","snapshot_observed_at":"2026-07-29T18:54:03.959574Z","submitted_at":"2025-07-08T04:40:09Z","title":"Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T12:17:59.458633Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.05660"},"observation_digest":"sha256:736b32ca9129e3915bc21e77bbf7c328233974a72152a7e0bc029bc439dffe24","observation_id":"e677df19-40c9-4493-a1a5-ae63223a43ca","resolution":{"observed_at":"2026-05-22T12:21:31.178964Z","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T19:21:13.301322Z","title":"Demystifying prompts in language models via perplexity estimation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08021","last_updated":"2025-07-08T08:07:57Z","snapshot_observed_at":"2026-08-08T00:22:38.503906Z","submitted_at":"2025-07-08T08:07:57Z","title":"Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:13.301322Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.08021"},"observation_digest":"sha256:e941491d3efbd30d0a793b57693bdf008f7a36c9ffc7950fd2e9d3a1e50bbf59","observation_id":"1464b78d-3bcb-46c2-993a-bcfe979dc1b1","resolution":{"observed_at":"2026-08-06T19:21:13.301322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T17:56:59.267584Z","title":"Smith, and Luke Zettlemoyer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09509","last_updated":"2025-08-30T19:34:43Z","snapshot_observed_at":"2026-08-07T21:38:02.482605Z","submitted_at":"2025-07-13T06:33:12Z","title":"How Important is `Perfect' English for Machine Translation Prompts?","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T17:56:59.267584Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.09509"},"observation_digest":"sha256:92210a3778de3f549aa44013b37ca2a466d12e302048e2a9fabc452f83f6d9d7","observation_id":"022ea7e1-57c9-406b-b3b9-d06691611424","resolution":{"observed_at":"2026-08-06T17:56:59.267584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T16:24:30.557566Z","title":"Demystifying prompts in language models via perplexity estimation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13629","last_updated":"2025-07-18T03:41:18Z","snapshot_observed_at":"2026-08-07T10:03:57.005879Z","submitted_at":"2025-07-18T03:41:18Z","title":"Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques","version":1},"reference_index":195,"source":"pdf_text","source_observed_at":"2026-08-06T16:24:30.557566Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.13629"},"observation_digest":"sha256:c79c260343f333eec1119ea1b016679ad200aa71f9c61327add931451b4b432a","observation_id":"5cc7940e-03c1-4de9-9134-c38381fea81f","resolution":{"observed_at":"2026-08-06T16:24:30.557566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T10:43:45.719310Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.23554","last_updated":"2025-07-31T13:42:14Z","snapshot_observed_at":"2026-08-08T08:02:39.063220Z","submitted_at":"2025-07-31T13:42:14Z","title":"DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T10:43:45.719310Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2507.23554"},"observation_digest":"sha256:1bc736ed0edfc10492fec51ea4cfa0e6ab22eeca57fa71488a385207b4ff12b0","observation_id":"2ddd3a27-8f74-448f-a245-d26af26970d0","resolution":{"observed_at":"2026-08-06T10:43:45.719310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-06T05:32:12.205549Z","title":"Gonen, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.01605","last_updated":"2025-08-03T05:42:20Z","snapshot_observed_at":"2026-08-06T05:32:10.145767Z","submitted_at":"2025-08-03T05:42:20Z","title":"Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T05:32:12.205549Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2508.01605"},"observation_digest":"sha256:d4f79e095ec213e0bfd6eab0ee7bcce5cfeb273e37e6bcaa7df44e95f2e0c11f","observation_id":"2fff2e03-5f19-446d-8d9d-055967915801","resolution":{"observed_at":"2026-08-06T05:32:12.205549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-05T15:18:31.533223Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.20083","last_updated":"2026-07-19T08:24:06Z","snapshot_observed_at":"2026-08-06T12:59:49.846974Z","submitted_at":"2025-08-27T17:49:28Z","title":"DisarmRAG: Stealthy Retriever-Centric Poisoning to Disable Self-Correction in Retrieval-Augmented Generation (Extended Version)","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T15:18:31.533223Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2508.20083"},"observation_digest":"sha256:345f7d029ad08a28cbe857a63b5cc53cba7c7af45f41d7ad6adb706cb75ffd1c","observation_id":"e6e4c22f-6fcb-4a2d-b31b-6abcaf20cb44","resolution":{"observed_at":"2026-08-05T15:18:31.533223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-08-04T08:06:10.414127Z","title":"Demystifying prompts in language models via perplexity estimation.arXiv preprint arXiv:2212.04037, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.22963","last_updated":"2026-06-19T20:25:06Z","snapshot_observed_at":"2026-08-04T08:06:05.944327Z","submitted_at":"2025-10-27T03:37:41Z","title":"When Compression Becomes an Attack Surface: Black-Box Attacks on Prompt-Compressed LLM Agents","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T08:06:10.414127Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2510.22963"},"observation_digest":"sha256:a5da3e4aeb9842e555db6185c4082108cb2d8dc08062181bcddac89ea3c14ab4","observation_id":"98b8de10-f98f-4fd3-a1e8-976a8184df6a","resolution":{"observed_at":"2026-08-04T08:06:10.414127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2511.13502","last_updated":"2026-05-07T21:01:36Z","snapshot_observed_at":"2026-07-06T22:36:00.635873Z","submitted_at":"2025-11-17T15:39:54Z","title":"SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-17T22:20:53.561649Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2511.13502"},"observation_digest":"sha256:25a0290e2c85d1aba75fa5bdefc693342c97b2627a59be565edc04bbe866f2e9","observation_id":"aed7c814-562a-4573-9efc-8265289b64b0","resolution":{"observed_at":"2026-05-17T22:22:09.003824Z","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2602.04572","last_updated":"2026-04-30T17:34:23Z","snapshot_observed_at":"2026-08-05T11:34:25.184752Z","submitted_at":"2026-02-04T13:58:39Z","title":"From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-16T07:42:48.141279Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2602.04572"},"observation_digest":"sha256:86fdaa3be906c4d9f562d2c0899076c442d664f084a761a8b4de5483426b5eb3","observation_id":"6e930568-0cf5-4606-a277-78d728a2bc79","resolution":{"observed_at":"2026-05-16T07:47:33.212432Z","resolver_source":"arxiv_id","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2605.12814","last_updated":"2026-05-12T23:14:37Z","snapshot_observed_at":"2026-08-02T06:21:17.894895Z","submitted_at":"2026-05-12T23:14:37Z","title":"Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-14T19:17:04.738168Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2605.12814"},"observation_digest":"sha256:eb70387c52adcdecd24e7c236881ab62dd4d942a89512ee9260ef9d07622241f","observation_id":"6de3def1-7496-478b-ace9-595ccd6f2632","resolution":{"observed_at":"2026-05-14T19:17:49.942420Z","resolver_source":"arxiv_id","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2606.22873","last_updated":"2026-06-25T18:44:01Z","snapshot_observed_at":"2026-08-02T23:29:21.699637Z","submitted_at":"2026-06-22T05:37:43Z","title":"SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning","version":2},"reference_index":187,"source":"arxiv_source","source_observed_at":"2026-06-26T09:19:50.623741Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2606.22873"},"observation_digest":"sha256:d10a7f3553ee7a32c8bb7720d5bb38bc7c7b13e76b6fb9799711902555a03f3d","observation_id":"8e693294-61f2-4adb-a974-ec05af4acfc9","resolution":{"observed_at":"2026-07-04T09:59:45.004274Z","resolver_source":"arxiv_id","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":"2212.04037","last_updated":"2024-09-12T19:54:37Z","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation","version":2},"cited_work":{"arxiv_id":"2212.04037","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.04037","snapshot_observed_at":"2026-07-04T09:59:45.002855Z","title":"Demystifying prompts in language models via perplexity estimation","venue":null,"work_id":"e6af2d13-ea7c-4bf8-83b9-6b8e8729efcc","year":2022},"citing_paper":{"arxiv_id":"2606.22873","last_updated":"2026-06-25T18:44:01Z","snapshot_observed_at":"2026-08-02T23:29:21.699637Z","submitted_at":"2026-06-22T05:37:43Z","title":"SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning","version":3},"reference_index":186,"source":"arxiv_source","source_observed_at":"2026-06-29T01:18:19.195007Z"},"links":{"cited_paper":"/paper/2212.04037","citing_paper":"/paper/2606.22873"},"observation_digest":"sha256:db983f2c742e8cf4ce5dfac20799dda3209b20e07e483805c15aebec2ba9c5e7","observation_id":"210b8a92-797e-4b9d-ad08-875d4c03c995","resolution":{"observed_at":"2026-07-01T18:55:59.646841Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2212.04037/citation-record","integrity":"/paper/2212.04037/integrity","json":"/paper/2212.04037/citation-record.json","paper":"/paper/2212.04037"},"outbound":[],"paper":{"arxiv_id":"2212.04037","last_updated":"2024-09-12T19:54:37Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T11:18:39.827875Z","submitted_at":"2022-12-08T02:21:47Z","title":"Demystifying Prompts in Language Models via Perplexity Estimation"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2212.04037."}