{"as_of":"2026-08-09T19:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:64921cdad2fbe9c129dd313f814ba1c4d653399f448458e8c6e5797252a5f0e4","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:32:32.805859Z","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":"2026-05-21T19:25:31.176024Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-08-07T13:32:32.805859Z","title":"Seesaw: High-throughput llm inference via model re-sharding, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.21487","last_updated":"2025-05-27T17:54:07Z","snapshot_observed_at":"2026-08-07T13:24:37.243429Z","submitted_at":"2025-05-27T17:54:07Z","title":"Hardware-Efficient Attention for Fast Decoding","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T13:32:32.805859Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2505.21487"},"observation_digest":"sha256:73bcfc4d3ce62814e07c23752abd8470b766f462eed1e185f9194d58a8cdee95","observation_id":"97826bf4-382f-47e4-a23f-1c48ba6253a6","resolution":{"observed_at":"2026-08-07T13:32:32.805859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-08-07T04:27:40.300419Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.12094","last_updated":"2025-06-12T09:51:06Z","snapshot_observed_at":"2026-08-08T16:12:50.286008Z","submitted_at":"2025-06-12T09:51:06Z","title":"Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T04:27:40.300419Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2506.12094"},"observation_digest":"sha256:ba26b6b20f8ede5ec7cee7b2f7d7447030f504f87ea1ec541f62a87012d976be","observation_id":"a2843cd6-6086-4013-a65e-50a35eefc970","resolution":{"observed_at":"2026-08-07T04:27:40.300419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-08-05T13:52:30.186309Z","title":"Seesaw: High-throughput llm inference via model re-sharding","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00217","last_updated":"2025-08-29T20:01:35Z","snapshot_observed_at":"2026-08-08T23:54:20.135229Z","submitted_at":"2025-08-29T20:01:35Z","title":"Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T13:52:30.186309Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2509.00217"},"observation_digest":"sha256:53159ea24a763a7758c76b52e80bb29afc5a4bfb9caf21d850e3ff3b4689b83a","observation_id":"f90f5743-a83d-4467-9673-c90090343477","resolution":{"observed_at":"2026-08-05T13:52:30.186309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":"2503.06433","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hazy Re- search Blog","venue":null,"work_id":"f1f52ea9-133b-4b6d-93e0-dc848f88a0e0","year":2025},"citing_paper":{"arxiv_id":"2509.19729","last_updated":"2026-04-22T06:30:11Z","snapshot_observed_at":"2026-08-03T15:52:53.487402Z","submitted_at":"2025-09-24T03:15:37Z","title":"Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-18T14:59:38.194894Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2509.19729"},"observation_digest":"sha256:1b6d680a9d3404f07548086bf16040d909febc19b32c18f7b7a98e6a5c47856e","observation_id":"aa051f31-3a6f-4b06-b44a-918fcd2191a2","resolution":{"observed_at":"2026-05-18T15:01:31.471942Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":"2503.06433","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hazy Re- search Blog","venue":null,"work_id":"f1f52ea9-133b-4b6d-93e0-dc848f88a0e0","year":2025},"citing_paper":{"arxiv_id":"2511.09557","last_updated":"2026-05-20T17:50:51Z","snapshot_observed_at":"2026-08-08T17:02:49.917616Z","submitted_at":"2025-11-12T18:59:26Z","title":"Understanding and Improving Communication Performance in Multi-node LLM Inference","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T19:24:34.459446Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2511.09557"},"observation_digest":"sha256:d0d01d3167bd9931cf081be18a57c64edacce3f49225e74b643e822f093ab98a","observation_id":"3032c9cd-70e1-4ccb-ad37-ff338f069620","resolution":{"observed_at":"2026-05-21T19:25:31.178045Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":"2503.06433","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hazy Re- search Blog","venue":null,"work_id":"f1f52ea9-133b-4b6d-93e0-dc848f88a0e0","year":2025},"citing_paper":{"arxiv_id":"2605.02189","last_updated":"2026-05-04T03:37:40Z","snapshot_observed_at":"2026-08-02T13:10:09.655062Z","submitted_at":"2026-05-04T03:37:40Z","title":"PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-08T18:49:56.357400Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2605.02189"},"observation_digest":"sha256:f75785f12737ee3ba4b9b3793d5c365880078a9a0c7a113e0e536b152899b288","observation_id":"477b74ee-b1b7-40a1-81df-105cc8c4381c","resolution":{"observed_at":"2026-05-09T06:10:41.559447Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":"2503.06433","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hazy Re- search Blog","venue":null,"work_id":"f1f52ea9-133b-4b6d-93e0-dc848f88a0e0","year":2025},"citing_paper":{"arxiv_id":"2605.06046","last_updated":"2026-05-07T11:34:10Z","snapshot_observed_at":"2026-07-06T23:18:36.537416Z","submitted_at":"2026-05-07T11:34:10Z","title":"Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T14:06:01.934357Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2605.06046"},"observation_digest":"sha256:d6190268ece901c501d99cc4bc7d29a9b763613ce25dbe7395b5171ca06acda2","observation_id":"d03c1a45-79c5-4cc0-aa89-63acdc06e022","resolution":{"observed_at":"2026-05-11T18:46:09.132527Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-07-13T04:56:20.002453Z","title":"Seesaw: High-throughput llm inference via model re-sharding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09172","last_updated":"2026-07-17T15:11:48Z","snapshot_observed_at":"2026-08-06T03:29:55.314701Z","submitted_at":"2026-07-10T08:04:19Z","title":"Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T04:56:20.002453Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2607.09172"},"observation_digest":"sha256:605f3d4ded0f1c6e72e8778e066b5a6ca50a13b078424a900d1b193d362a61a2","observation_id":"d5ce4034-caa2-4db7-b85a-033e718f27c3","resolution":{"observed_at":"2026-07-13T04:56:20.002453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06433","snapshot_observed_at":"2026-08-02T07:43:18.447867Z","title":"Seesaw: High-throughput llm inference via model re-sharding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09172","last_updated":"2026-07-17T15:11:48Z","snapshot_observed_at":"2026-08-06T03:29:55.314701Z","submitted_at":"2026-07-10T08:04:19Z","title":"Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T07:43:18.447867Z"},"links":{"cited_paper":"/paper/2503.06433","citing_paper":"/paper/2607.09172"},"observation_digest":"sha256:1a7b44439ad64c6f0d98029d3ed841da3152e073118e3756b06c866f2aaf2908","observation_id":"c21354b9-9887-4bd3-b3e7-198b286803d2","resolution":{"observed_at":"2026-08-02T07:43:18.447867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.06433/citation-record","integrity":"/paper/2503.06433/integrity","json":"/paper/2503.06433/citation-record.json","paper":"/paper/2503.06433"},"outbound":[],"paper":{"arxiv_id":"2503.06433","last_updated":"2025-03-09T04:14:06Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-07T17:19:30.916136Z","submitted_at":"2025-03-09T04:14:06Z","title":"Seesaw: High-throughput LLM Inference via Model Re-sharding"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.06433."}