{"as_of":"2026-08-05T07:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6cd621095e9aa373ed0b5d2207b2502b6caf98f8fbc6be9aec8cd4ee0a84a587","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T18:16:30.369786Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T23:14:54.909730Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-12T06:31:28.986900Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"cited_work":{"arxiv_id":"2604.02985","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.02985","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","venue":"cs.IR","work_id":"7770416a-9b36-45d4-a9a8-e444f5ab5e93","year":2026},"citing_paper":{"arxiv_id":"2605.09611","last_updated":"2026-05-10T15:48:38Z","snapshot_observed_at":"2026-07-06T23:21:39.966502Z","submitted_at":"2026-05-10T15:48:38Z","title":"Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-12T04:18:11.836537Z"},"links":{"cited_paper":"/paper/2604.02985","citing_paper":"/paper/2605.09611"},"observation_digest":"sha256:6f205303915fc46e89c68748905f38237a5657b0beab82f1911284ad0afedbb3","observation_id":"8675d93d-35d1-4324-a44f-73677f4eca8a","resolution":{"observed_at":"2026-05-12T06:26:24.190105Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"cited_work":{"arxiv_id":"2604.02985","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.02985","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","venue":"cs.IR","work_id":"7770416a-9b36-45d4-a9a8-e444f5ab5e93","year":2026},"citing_paper":{"arxiv_id":"2605.09990","last_updated":"2026-05-11T05:06:59Z","snapshot_observed_at":"2026-07-06T23:21:59.096464Z","submitted_at":"2026-05-11T05:06:59Z","title":"Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-12T04:11:02.387702Z"},"links":{"cited_paper":"/paper/2604.02985","citing_paper":"/paper/2605.09990"},"observation_digest":"sha256:8843292fe78b70396984bad372a4fe4d13ceb6421f8373c5de87e8188ff661ee","observation_id":"648fcaa2-331e-4c2c-8505-73091bc99a59","resolution":{"observed_at":"2026-05-12T06:31:28.994381Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.02985","snapshot_observed_at":"2026-08-01T23:14:54.909730Z","title":"Zongqian Li, Yixuan Su, and Nigel Collier","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15516","last_updated":"2026-07-17T00:03:47Z","snapshot_observed_at":"2026-08-01T23:14:52.546212Z","submitted_at":"2026-07-17T00:03:47Z","title":"Cache-Aware Prompt Compression:A Two-Tier Cost Model for LLM API Caching","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-01T23:14:54.909730Z"},"links":{"cited_paper":"/paper/2604.02985","citing_paper":"/paper/2607.15516"},"observation_digest":"sha256:40adf223062937773d3a053771abc5b065f5ac7cb499cb8aff9a36012d111dab","observation_id":"f79c36e8-5ca0-49ed-ae22-5db0b9c5880d","resolution":{"observed_at":"2026-08-01T23:14:54.909730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2604.02985/citation-record","integrity":"/paper/2604.02985/integrity","json":"/paper/2604.02985/citation-record.json","paper":"/paper/2604.02985"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.acl-long.172","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LongBench","venue":null,"work_id":"334e52dd-5eee-49da-94cd-26c0028afe5c","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:762a905108d07d8460c2e3105921783fad091185eed949424054a3d6418bec5c","observation_id":"28adab7b-4bb4-46c1-b55a-f03ff7e762c0","resolution":{"observed_at":"2026-05-13T18:18:05.621702Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:49:42.194817+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:49:42.194817+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7916.374139","doi":"10.5555/3737916.3741392","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"In: Pro- ceedings of the 38th International Conference on Neural Information Processing Systems","venue":null,"work_id":"740cc81d-9285-4754-952f-d84ff1cfc810","year":2025},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:0dedb87810496fb06dfd58a361f35c06cba7ca9ae1800a1e5c3d254f315d71d0","observation_id":"294b8e5b-28af-49a6-a275-001d2993b86a","resolution":{"observed_at":"2026-05-13T18:18:05.625102Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2023","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T20:30:06.904853Z","title":"Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, and Ji-Rong Wen","venue":null,"work_id":"8122370e-553b-4d6f-b1a8-98e24c010366","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:2e412535d99d9a1030169e0ddd3b97b44dbec3e33a403fba61a985864399f77e","observation_id":"6dfedbf5-2e3c-4b6c-9905-913c5cdc7f59","resolution":{"observed_at":"2026-05-13T18:18:05.632931Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-03T20:08:10.998972+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T20:08:10.998972+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":"2407.21783","doi":"10.1016/s0749-0720(15","metadata_source":"pith","pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"The Llama 3 Herd of Models","venue":"cs.AI","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:95350dc09a9e4f91463dcc517a5303215e9bda8a5879c311962fc1ab98499000","observation_id":"06e7ed54-3b81-4e9a-8b24-e2f82241ad59","resolution":{"observed_at":"2026-05-13T18:18:06.145521Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.findings-acl.306","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Extending context window of large language models via semantic compression","venue":null,"work_id":"d7323139-ea36-4214-8529-f25e939a12d2","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:5071de3c7a1985abeb74dbf0acd92daea213e67b0d22de8ae113c130118aec9e","observation_id":"fce97cce-7bf8-46bf-a2f0-20ed45f1ae59","resolution":{"observed_at":"2026-05-13T18:18:05.630303Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In: The Twelfth International Conference on Learning Representations (2024),https://openreview.net/forum? id=uREj4ZuGJE 14 C","venue":null,"work_id":"589c132b-7568-4131-84ba-17e8071aa3b8","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:8f351f1c62e0f255670223c93aec059905c834851bc5b7ee9ac2b65ccec2b6fa","observation_id":"bb3a37f7-6184-4295-a236-88ec6b99c30e","resolution":{"observed_at":"2026-05-14T04:26:38.008610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"824c11ce-3f9f-41fc-9ea0-aa528e4e8f03","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:1a4f877d75c2454666ed1082b16f5fa62f1668d949f06e5537be2ee777b683df","observation_id":"a709b686-b396-424a-81fd-a0bb5d898963","resolution":{"observed_at":"2026-05-14T04:26:38.012938Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In: Workshop on Efficient Systems for Foundation Models II @ ICML2024 (2024),https://openreview.net/forum? id=vs6CCDuK7l","venue":null,"work_id":"1884a5e2-0e3b-431d-8257-e31fab1f2f65","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:d22cfd19c375caad89623a55a6b00351c1106bacd2dcba1f78d3ba3429e592c2","observation_id":"b024978c-9d65-47f0-97a2-45b4c624ed1b","resolution":{"observed_at":"2026-05-14T04:26:38.018084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":"2310.06825","doi":"10.48550/arxiv.2310.06825","metadata_source":"pith","pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mistral 7B","venue":"cs.CL","work_id":"eb5e1305-ad11-4875-ad8d-ad8b8f697599","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:644dbaa2092b2f92fd9afa3c1512a6eff57aeb3421cc7ac1a8bc9c48487e86e9","observation_id":"2f50954e-3a7b-4f1b-82e5-be63799b5d37","resolution":{"observed_at":"2026-05-13T18:18:06.149281Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-02T03:08:12.282824+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T03:08:12.282824+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2023.emnlp-main.825","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LLMLingua: Com- pressing prompts for accelerated inference of large language models","venue":null,"work_id":"629b9d0e-ed05-4694-ba5a-e81b8d5751dc","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:ff9a1aa42309c8e8d84f61ac6304581732939d39badb887c5593a3dbdada2fdb","observation_id":"e092879b-010c-4e76-b4e6-2919daa85f4b","resolution":{"observed_at":"2026-05-13T18:18:05.616939Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:49:42.680868+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:49:42.680868+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:17:03.600265Z","title":"In: Zong, C., Xia, F., Li, W., Navigli, R","venue":null,"work_id":"8d675bdd-79ca-48d6-9163-fc17ce0e8ece","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:2b8324b49240e795bee867bdba5a11bf8cbdc681b8414c7fab691a1a03f80829","observation_id":"fc270d1e-9054-46e8-9e7b-cd5ef2c721cd","resolution":{"observed_at":"2026-05-13T18:18:05.603082Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.340342","doi":"10.1109/access.2024.3403426","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Discrete prompt compression with reinforcement learning","venue":"IEEE Access","work_id":"bdac689c-2f4d-4bd2-abd7-d61a8153790e","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:5f33b3aea897303ce090a1bc875ddfc63208460e8dde25e531cce51b1558b18b","observation_id":"ed8b035c-aa4f-4dc7-ad64-f7baf90776b5","resolution":{"observed_at":"2026-05-13T18:18:05.588591Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0006.361316","doi":"10.1145/3600006.3613163","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention , booktitle =","venue":null,"work_id":"1b10f2a9-a178-4d23-97fb-8db2354c7e6c","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:144a30f4974fec71e3291a803dc5311aac83eb2fa5b97e31a03175d24b2bdeca","observation_id":"889c6aa0-9592-4ea1-adbc-058a886ac5f4","resolution":{"observed_at":"2026-05-13T18:18:05.611089Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2023.emnlp-main.391","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (Dec 2023).https://doi.org/ 10.18653/v1/2023.emnlp-main.391","venue":null,"work_id":"6929d93f-92e7-4fc0-9681-7de781846d42","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:c3b1471dc1ac814d880aa55c8c123f721617cd780cc1d5a300a7986f7103bd65","observation_id":"e68f50bf-952f-414b-a0d8-abe9c5c5ca06","resolution":{"observed_at":"2026-05-13T18:18:05.613504Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:49:43.177548+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:49:43.177548+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2023.findings-emnlp.655","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Findings of the Association for Computational Linguistics: EMNLP 2023 (Dec 2023).https: //doi.org/10.18653/v1/2023.findings-emnlp.655","venue":null,"work_id":"2b5b65a0-3449-40d3-8388-af60f0c4a840","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:24cd56105b7d85b3bc9c4dc6b4fd4b0d8c6e44421e97cd2cd7765c2171abcd61","observation_id":"a32d290c-6929-4d83-abb1-2a667db96e6b","resolution":{"observed_at":"2026-05-13T18:18:05.591527Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6410.371455","doi":"10.1145/3696410.3714553","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Proceedings of the ACM on Web Conference 2025","venue":null,"work_id":"5a454917-158e-4b7b-8f83-a3fab9bf8bba","year":2025},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:44f306b168470df903762531156becc50be757b4381e807f794b933bd63b00cd","observation_id":"d3870341-3f76-4baf-989f-b65c4e757e1a","resolution":{"observed_at":"2026-05-13T18:18:05.597918Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-21T14:22:18.024922+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T14:22:18.024922+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7916.374092","doi":"10.5555/3737916.3740925","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"In: Proceedings of the 38th International Conference on Neural Information Processing Systems","venue":null,"work_id":"b201fec6-cf1f-47d5-a3a6-6c3b5e5752e0","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:7aee4227e242c197f1e5ade2cac21fc6cccadc5565c09eaa20524a308295faef","observation_id":"039af329-a585-4312-b9be-2376997d751f","resolution":{"observed_at":"2026-05-13T18:18:05.606325Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In: The Twelfth International Conference on Learning Representations (2024),https://openreview.net/forum? id=mqVgBbNCm9","venue":null,"work_id":"0ccc8275-cb31-464a-a504-2f4c0440c6ec","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:44d1b0aca423112a0b48be231812d92e898412f22c393f75a6835bcf77f0fa19","observation_id":"4967f847-cae6-4b96-8113-e5c5b2739600","resolution":{"observed_at":"2026-05-14T04:26:38.022648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.findings-acl.57","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Findings of the Association for Computational Linguistics: ACL 2024 (Aug 2024)","venue":null,"work_id":"bea6fec8-4155-4743-9076-7d7ecaf6b17d","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:15525cf729e3b6d05b4cf7d81536611d976ee68b865883cb984aadfed032894f","observation_id":"8b7f5455-abe6-440d-8c4a-4fb04726474d","resolution":{"observed_at":"2026-05-13T18:18:05.600651Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:49:42.443788+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:49:42.443788+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7779.367779","doi":"10.1145/3677779.3677791","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Proceedings of the International Conference on Modeling, Natural Language Processing and Machine Learning","venue":null,"work_id":"600510c7-8846-4afc-a384-6187b509dbfb","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:1b95bc246ea6cd22250dffff47c8e92d53edda36b1b42d77d54d60cd15f8bb81","observation_id":"aaefbc62-f481-4607-98e9-0afe246ea94d","resolution":{"observed_at":"2026-05-13T18:18:05.594744Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08377","last_updated":"2023-11-14T18:41:54Z","snapshot_observed_at":"2026-07-06T16:47:33.021833Z","submitted_at":"2023-11-14T18:41:54Z","title":"Learning to Filter Context for Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":"2311.08377","doi":"10.48550/arxiv.2311.08377","metadata_source":"pith","pith_arxiv_id":"2311.08377","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to filter context for retrieval-augmented generation","venue":"cs.CL","work_id":"71de4469-6482-4512-99f0-668b8bff5959","year":2023},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"cited_paper":"/paper/2311.08377","citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:4a3cc20ce65bd548a498d3dd1cf0c27f9c9fa400ee1c714b956e208865b45470","observation_id":"b2f4dcc1-5fdc-45aa-a1f3-fd409fbe5c17","resolution":{"observed_at":"2026-05-13T18:18:06.153973Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2020.emnlp-demos.6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T09:06:59.046980Z","title":"Transformers: State-of-the-Art Natural Language Processing","venue":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","work_id":"801601a0-1077-41ef-a947-ba81c0b7510f","year":2020},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:d9ec9ac59ccf47f9000252de0125205cde30a8fd8d03b4d9524cacf4ad286bef","observation_id":"6030a2cc-fcb2-4875-98f0-03e9fe969c6b","resolution":{"observed_at":"2026-05-13T18:18:05.627950Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:38:48.412582+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:38:48.412582+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In: The Twelfth International Conference on Learning Representations (2024),https://openreview.net/forum? id=mlJLVigNHp","venue":null,"work_id":"fc80333f-5f65-473a-acb6-2510fde59da1","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:2f8cca5e873de5a34db9ca8f09b20a7e24cb9619c0d64f1c26d5f45628df0479","observation_id":"ad49da04-d1a2-4ba0-a544-f3635ebd9eb6","resolution":{"observed_at":"2026-05-14T04:26:38.027167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14294","last_updated":"2024-07-19T04:47:36Z","snapshot_observed_at":"2026-07-06T18:03:47.096406Z","submitted_at":"2024-04-22T15:53:08Z","title":"A Survey on Efficient Inference for Large Language Models","version":3},"cited_work":{"arxiv_id":"2404.14294","doi":"10.48550/arxiv.2404.14294","metadata_source":"pith","pith_arxiv_id":"2404.14294","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A Survey on Efficient Inference for Large Language Models","venue":"cs.CL","work_id":"4e58b7d0-2870-4ddb-955f-798b34deb447","year":2024},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"cited_paper":"/paper/2404.14294","citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:975c093a3e715bd870a6b7311a1b7182352ef5ab08e1c1372627b9f239225639","observation_id":"847a7ab5-94ff-4be4-81a0-62f5544971ae","resolution":{"observed_at":"2026-05-15T02:39:33.856027Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-24T00:53:38.535897+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-24T00:53:38.535897+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3748304","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ACM Trans","venue":"ACM Transactions on Information Systems","work_id":"f0f4fb78-2112-48a4-a94d-aecf31d3ba65","year":2025},"citing_paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-13T18:16:30.369786Z"},"links":{"citing_paper":"/paper/2604.02985"},"observation_digest":"sha256:cd04ec302c8354217652c9027ec4504ee05fb245e55c98380f40f418d155417c","observation_id":"ce6a5940-5854-41ba-ba70-53a73b0aba63","resolution":{"observed_at":"2026-05-13T18:18:05.619283Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.02985","last_updated":"2026-04-03T11:41:53Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-07-06T22:52:15.263364Z","submitted_at":"2026-04-03T11:41:53Z","title":"Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":1,"verified_exact":12,"verified_fuzzy":4},"total_outbound_references":25},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 3 inbound Pith citation observations for arXiv:2604.02985."}