{"as_of":"2026-08-10T10:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c13065e1bd9d617ac47a3b1abfb9d42ff5cd1a8d7341431d0415dbbf2493f429","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:20:20.512431Z","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":"2026-07-01T15:25:47.298119Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-08-06T22:01:23.606728Z","title":"arXiv:2505.09142 [cs.DC] https://arxiv.org/abs/2505.09142","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22950","last_updated":"2025-06-28T16:52:29Z","snapshot_observed_at":"2026-08-09T21:07:16.042677Z","submitted_at":"2025-06-28T16:52:29Z","title":"Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T22:01:23.606728Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2506.22950"},"observation_digest":"sha256:7a0317d8942a4ce51566e70185665a7b4e9af1fbb7539109cd42619f015c5b89","observation_id":"3b90de7b-6217-4c90-9730-19029bdad7de","resolution":{"observed_at":"2026-08-06T22:01:23.606728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":"2505.09142","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-07-01T15:25:47.298119Z","title":"Elis: Efficient llm iterative scheduling system with response length predictor","venue":null,"work_id":"47be4987-d82d-4462-ba41-f9197979e2aa","year":2025},"citing_paper":{"arxiv_id":"2603.09002","last_updated":"2026-04-26T14:13:48Z","snapshot_observed_at":"2026-07-06T22:48:26.306802Z","submitted_at":"2026-03-09T22:46:27Z","title":"Security Considerations for Multi-agent Systems","version":2},"reference_index":244,"source":"pdf_text","source_observed_at":"2026-05-15T14:12:14.160789Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2603.09002"},"observation_digest":"sha256:57d98ee0213654f38fc7ea0692340c07ae231b8c99794f23b66697551c55d57b","observation_id":"ad3a4f47-acde-428e-9b18-5b33e0463842","resolution":{"observed_at":"2026-05-15T14:15:55.382900Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":"2505.09142","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-07-01T15:25:47.298119Z","title":"Elis: Efficient llm iterative scheduling system with response length predictor","venue":null,"work_id":"47be4987-d82d-4462-ba41-f9197979e2aa","year":2025},"citing_paper":{"arxiv_id":"2604.07931","last_updated":"2026-04-09T07:49:52Z","snapshot_observed_at":"2026-08-01T20:01:12.346839Z","submitted_at":"2026-04-09T07:49:52Z","title":"Robust Length Prediction: A Perspective from Heavy-Tailed Prompt-Conditioned Distributions","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-10T18:32:42.905852Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2604.07931"},"observation_digest":"sha256:33478c4f8b2adc46a4d6f0b5ed2e0c197fd9850619d97bba89a09f9aff2f3ca3","observation_id":"867ae737-3d11-41dd-be8d-83c6482c17a3","resolution":{"observed_at":"2026-05-11T00:25:50.678891Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":"2505.09142","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-07-01T15:25:47.298119Z","title":"Elis: Efficient llm iterative scheduling system with response length predictor","venue":null,"work_id":"47be4987-d82d-4462-ba41-f9197979e2aa","year":2025},"citing_paper":{"arxiv_id":"2606.30391","last_updated":"2026-06-29T14:44:24Z","snapshot_observed_at":"2026-08-04T10:56:44.243323Z","submitted_at":"2026-06-29T14:44:24Z","title":"Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T03:41:51.034169Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2606.30391"},"observation_digest":"sha256:4e724c80495bfeed7ebdc289277b204e43513877d24624bed83a79d7de18d39a","observation_id":"78348093-1f50-425f-8be2-c6466bbadb2f","resolution":{"observed_at":"2026-07-01T15:25:47.300094Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-08-01T12:00:24.304941Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26633","last_updated":"2026-07-29T08:58:04Z","snapshot_observed_at":"2026-08-07T13:12:18.376857Z","submitted_at":"2026-07-29T08:58:04Z","title":"NELSSA: A GPU-PNM Heterogeneous System for Mixed-Length LLM Serving via Length-based Request Placement","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T12:00:24.304941Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2607.26633"},"observation_digest":"sha256:d97f02f2bb1dfe67a08a007e377f615ba524983d245446f61d87e1e3be0670f0","observation_id":"c7550a55-0728-4e64-8104-1f6220c733bb","resolution":{"observed_at":"2026-08-01T12:00:24.304941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09142","snapshot_observed_at":"2026-08-07T14:20:20.512431Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.06135","last_updated":"2026-08-06T15:07:43Z","snapshot_observed_at":"2026-08-09T23:12:59.185361Z","submitted_at":"2026-08-06T15:07:43Z","title":"LLM Inference Under Bursty Workload Distribution: Modifying the WAIT Algorithm","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:20.512431Z"},"links":{"cited_paper":"/paper/2505.09142","citing_paper":"/paper/2608.06135"},"observation_digest":"sha256:2d79b3f83bd061ead36035116569a14a0857203dca00176799fb599954247d39","observation_id":"90f4b6cb-7d76-43b7-b905-543bd4002504","resolution":{"observed_at":"2026-08-07T14:20:20.512431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.09142/citation-record","integrity":"/paper/2505.09142/integrity","json":"/paper/2505.09142/citation-record.json","paper":"/paper/2505.09142"},"outbound":[],"paper":{"arxiv_id":"2505.09142","last_updated":"2025-05-14T04:50:00Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-07T15:45:32.695385Z","submitted_at":"2025-05-14T04:50:00Z","title":"ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2505.09142."}