{"as_of":"2026-08-12T12:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9466309b588eca8b9a4954380deaf8967bfd7702840152fe0b69f666153fd3b6","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:48:21.027815Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-11T03:00:19.355357Z","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":"2026-05-11T03:00:55.550196Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"cited_work":{"arxiv_id":"2505.17548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.17548","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"H2: Towards efficient large-scale llm training on hyper-heterogeneous cluster over 1,000 chips","venue":null,"work_id":"4e4fc3e7-4db3-4019-a9ed-2a4f92b4949e","year":2025},"citing_paper":{"arxiv_id":"2605.07569","last_updated":"2026-05-08T10:41:04Z","snapshot_observed_at":"2026-07-06T23:19:56.415523Z","submitted_at":"2026-05-08T10:41:04Z","title":"HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-11T03:00:19.355357Z"},"links":{"cited_paper":"/paper/2505.17548","citing_paper":"/paper/2605.07569"},"observation_digest":"sha256:569edf6b12e4c6e73c30f4cfb548e39079cf9651ff0a5c06bd85759a822e0252","observation_id":"12ba1178-7752-4de3-981b-d5c68feb2042","resolution":{"observed_at":"2026-05-11T03:00:55.562160Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.17548/citation-record","integrity":"/paper/2505.17548/integrity","json":"/paper/2505.17548/citation-record.json","paper":"/paper/2505.17548"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:24.592517Z","title":"2016.{TensorFlow}: a system for{Large-Scale} machine learning","venue":null,"work_id":"c090e03e-2c9d-428b-9ef5-ad752a61ae50","year":2016},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.114978Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:49896ada76a75390cfeecf0948a0a311dd093875b14660347e7d8216fb8b1051","observation_id":"c44a3f27-7bc6-4b58-a3e5-9c80d87f6086","resolution":{"observed_at":"2026-08-07T14:48:24.650146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T14:48:17.193873Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.193873Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:22afb71ebfd0646f56f5166fcd2a8a8150852166bbabec2d194a11df2c124afd","observation_id":"0a801d9a-fb46-49a8-93dc-c8e30ebafc33","resolution":{"observed_at":"2026-08-07T14:48:17.193873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.2024","last_updated":"2014-06-08T20:52:15Z","snapshot_observed_at":"2026-08-10T09:51:19.534548Z","submitted_at":"2014-06-08T20:52:15Z","title":"Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.2024","snapshot_observed_at":"2026-08-07T14:48:17.311739Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.311739Z"},"links":{"cited_paper":"/paper/1406.2024","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:0741d109b328a078a4013b0763b90326858eec9a64a74ca9a90b1652bed1dee3","observation_id":"b975c21a-e04e-4b22-9dc5-7781857d4605","resolution":{"observed_at":"2026-08-07T14:48:17.311739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:17.389227Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.389227Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:04f5c4a14a981a594aaa4a0bf34ee942546d184d742d430b9ff6288a54a7c365","observation_id":"16f55966-a899-4ef8-beba-502d86872516","resolution":{"observed_at":"2026-08-07T14:48:17.389227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17297","last_updated":"2024-03-26T00:53:24Z","snapshot_observed_at":"2026-08-02T11:10:24.263044Z","submitted_at":"2024-03-26T00:53:24Z","title":"InternLM2 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17297","snapshot_observed_at":"2026-08-07T14:48:17.465664Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.465664Z"},"links":{"cited_paper":"/paper/2403.17297","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:ef9ff5888c56d242d5b49972daa6eae4d3fec220d8828c1b39216481023e5758","observation_id":"7cf3ea9a-76cf-406e-8357-04e7c25c8b48","resolution":{"observed_at":"2026-08-07T14:48:17.465664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06858","last_updated":"2024-10-23T18:45:33Z","snapshot_observed_at":"2026-08-07T05:35:13.505081Z","submitted_at":"2024-06-11T00:17:39Z","title":"FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06858","snapshot_observed_at":"2026-08-07T14:48:17.541894Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.541894Z"},"links":{"cited_paper":"/paper/2406.06858","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:419082083ee5a16643f6a0cfa7b974dcd02561ffe62466b9808757f5504a3ace","observation_id":"5a8e9774-b5e6-4664-8e58-07f53de3b53d","resolution":{"observed_at":"2026-08-07T14:48:17.541894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:24.496883Z","title":null,"venue":null,"work_id":"d76699d4-a50c-42f0-8558-ceccf671980b","year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.626169Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:a22674b4c440c911f32e806f7bfb3264f92d2500ff1b66042f8ef4b1400696d3","observation_id":"2a954a4a-1ce0-4a55-95a2-8cc630aa1679","resolution":{"observed_at":"2026-08-07T14:48:24.540060Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:24.324797Z","title":null,"venue":null,"work_id":"177c50c6-b5cf-4a73-aa48-15b7dde5cb80","year":2021},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.792015Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:11b6ab53d7db238bf00e66978bd6eba8ce59e13ec9163f3fe98bcb0adbb84826","observation_id":"8748ac2d-9227-4132-8a81-9e1b0155f1b4","resolution":{"observed_at":"2026-08-07T14:48:24.413329Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:17.907887Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.907887Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:e7886c25a7368d74f90529a71e9f07e7260f96251fb8d62df5668ce18c98c460","observation_id":"cad73c0d-0ebc-49b4-b160-af0c57f85114","resolution":{"observed_at":"2026-08-07T14:48:17.907887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:24.239078Z","title":null,"venue":null,"work_id":"35f6e49a-040a-46aa-ac66-6b1a04a3a9c3","year":2022},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.066009Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:ea06c4ca642eaf36c3d3f3271a955397abf1908ffcfff3d033bd874fa061d7ec","observation_id":"e29c968b-6dd0-4978-a67b-dea2abefc1bc","resolution":{"observed_at":"2026-08-07T14:48:24.252787Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:24.102316Z","title":null,"venue":null,"work_id":"ba6918b8-c1c6-4e18-8042-d4feae5f23a3","year":2021},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.172770Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:d3bd9acd994936c2188461bc2d9274286453b57ba9fa66d46336afd84ae85568","observation_id":"a1e1373b-aec1-440e-b7c1-1dd6d049485a","resolution":{"observed_at":"2026-08-07T14:48:24.164432Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-07T14:48:18.226164Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.226164Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:60f4e4dddf8d63c21830e806e73fff318f964780c5329e83e399e24719361ec0","observation_id":"a20cb1a8-9c2b-4eb2-9fdd-7ac288b6dc1a","resolution":{"observed_at":"2026-08-07T14:48:18.226164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T14:48:18.287460Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.287460Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:8c76bd6f1e7e9821e31930424d67f1b63208561ef2b70e67c71039b88d0d8984","observation_id":"5f69d2b3-a494-45d9-b895-b8204d498933","resolution":{"observed_at":"2026-08-07T14:48:18.287460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:18.377266Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.377266Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:79e930dac5c0a4856b19ec9629114006bec28c6d20dcb71516dd211d4881e977","observation_id":"76015afa-470e-40aa-8f4d-5c005d0674bf","resolution":{"observed_at":"2026-08-07T14:48:18.377266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:23.972694Z","title":null,"venue":null,"work_id":"ddd3b6f0-a3a4-4893-9c3e-3eac9079fd47","year":2022},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.456048Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:275a1031cff76f469b0affd31532ddd06472f2ed55a5dd5ca7e8fd7ecc84f49f","observation_id":"5926ddf8-1d57-47d6-8a88-5016c1da1bd6","resolution":{"observed_at":"2026-08-07T14:48:24.018698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:23.835742Z","title":null,"venue":null,"work_id":"fc3a61e4-a3a0-41a0-8eea-d89b1e82c176","year":2019},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.667268Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:5c3dd04a0d1011d41161078de9f681ceee9c5c796424d1f75c8994f407ffb22c","observation_id":"001614ce-4208-4746-9fb8-abdcdcd33a51","resolution":{"observed_at":"2026-08-07T14:48:23.893749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:23.703965Z","title":null,"venue":null,"work_id":"e9656bc5-3142-41a3-a836-2df7d5d37c3c","year":2022},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.735065Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:588dbedf1a3f848a0fbb89cf8807646eac161b0ef074f7be0ff855bb25ce1d26","observation_id":"ed2da6dc-48c4-4f4f-bdd0-a79c4d041236","resolution":{"observed_at":"2026-08-07T14:48:23.759023Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:18.784159Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.784159Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:1b9f06bbd34b0e9553725e9fd51867e47cfce5752bcdd959980fb6e519509f01","observation_id":"0fcdda0a-468c-430a-afc1-377b119fa41c","resolution":{"observed_at":"2026-08-07T14:48:18.784159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.15704","last_updated":"2020-06-28T20:39:45Z","snapshot_observed_at":"2026-07-06T09:33:26.082100Z","submitted_at":"2020-06-28T20:39:45Z","title":"PyTorch Distributed: Experiences on Accelerating Data Parallel Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.15704","snapshot_observed_at":"2026-08-07T14:48:18.863517Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.863517Z"},"links":{"cited_paper":"/paper/2006.15704","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:d3481809fdef1f2ed7a696d8eedbce140e9daff10b897b2ad669bb12228bdbb0","observation_id":"7289d3ec-94ab-424f-8176-d9ccec855ae8","resolution":{"observed_at":"2026-08-07T14:48:18.863517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:18.951527Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:18.951527Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:7ff39e2b6fb7d2f2117b6cc607e306a7ee996b6999a100602f5d2d557e2027b4","observation_id":"562cf48a-0f7e-4875-ad31-6818f5f7b850","resolution":{"observed_at":"2026-08-07T14:48:18.951527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:23.515914Z","title":null,"venue":null,"work_id":"0b89a536-3f47-41e9-83aa-11995b2ad900","year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.119000Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:ad8bced7d938c8e03b44ca0b7a4f901dc79c0a965c059b68d720ffbb03b54245","observation_id":"18dcd68f-daa6-4241-8af1-fb478c5a1a0c","resolution":{"observed_at":"2026-08-07T14:48:23.568366Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:23.276476Z","title":null,"venue":null,"work_id":"d890da3f-509d-4643-a3c1-f54507b356d8","year":2019},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.317208Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:05f6986f86fff649d1c71ac41cbeb6f1b83cfe6b919905e961428c557f7e4d30","observation_id":"18194835-6722-4cf7-acd9-1d93b2b9ab86","resolution":{"observed_at":"2026-08-07T14:48:23.337454Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-08-11T01:48:59.557045Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T14:48:19.043514Z","title":"arXiv preprint arXiv:2412.19437 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.043514Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:b45a50a20e84061e7a30888e1bf553ffc8446f1d9556e280fafecb32d00db033","observation_id":"9a4872d6-d423-43c3-8183-c6bc2c2cfefe","resolution":{"observed_at":"2026-08-07T14:48:19.043514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:23.063072Z","title":null,"venue":null,"work_id":"857ed737-dd6e-4e51-b733-95fbe18550a3","year":2025},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.436606Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:7a60d3888ff39d577a3da853c692e65f9f7ec4496fd37b5c05272af481551c51","observation_id":"9dd4e77a-b045-45cf-8bec-b1742fa34c91","resolution":{"observed_at":"2026-08-07T14:48:23.093809Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:22.958876Z","title":null,"venue":null,"work_id":"0ba52232-dcfd-4f64-b667-a8959b51ec6a","year":2025},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.518534Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:dca97496013b1071f20aa169b0eedefe666c6dd324755d1af8db14a12ee271ed","observation_id":"f2d471aa-bc65-4e55-b059-51596c047ac2","resolution":{"observed_at":"2026-08-07T14:48:23.018174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:22.817583Z","title":null,"venue":null,"work_id":"ad3c57dd-f8e3-46aa-af12-eaf3fb7dbd8b","year":2020},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.656140Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:c689db56ab456f5be792e4134e451fcf8de15ad206a538fb126cdb8f279fea0c","observation_id":"b6d4e12e-25c8-4062-a1b8-0aff15a5833b","resolution":{"observed_at":"2026-08-07T14:48:22.878623Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:23.139948Z","title":null,"venue":null,"work_id":"ba911ddc-d502-496e-bd34-4c59776ce968","year":2021},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.400667Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:49244f12e3e486b463c0f367fafed316e5bcbafa79d4a7b7363bdc1c23111efc","observation_id":"727247f9-631e-4c65-923b-7ad6d1bc915f","resolution":{"observed_at":"2026-08-07T14:48:23.196758Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:19.771764Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.771764Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:490b869cbfce6bcd92c20f45087bbbcba332b6ac72a65c942ea68780255eaf2a","observation_id":"701179cc-eb21-4d2a-81fc-eef733eac208","resolution":{"observed_at":"2026-08-07T14:48:19.771764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:19.978908Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.978908Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:fa37d303d00affa9f244a461aee3eced2152ac20b283b5f20d7492392eb851d6","observation_id":"3c575a77-083f-4c62-a9f3-88cb51b9d14b","resolution":{"observed_at":"2026-08-07T14:48:19.978908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:20.179315Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.179315Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:4e42f62a0772c250054964d999e2c97a8d77db411f24f532e2503a1c509e3187","observation_id":"fd0870e6-ae01-404b-8974-5134450db665","resolution":{"observed_at":"2026-08-07T14:48:20.179315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:22.661177Z","title":null,"venue":null,"work_id":"660df3ac-13c0-4583-a952-d90871c03ca7","year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.719035Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:7593061bc412ab91fec72967d0e207bfb4e2cfc28f31977d5fd884ddf5aeef07","observation_id":"844bb2bd-2f7f-470c-a354-2f5189c9f248","resolution":{"observed_at":"2026-08-07T14:48:22.764762Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03791","last_updated":"2024-05-24T08:32:48Z","snapshot_observed_at":"2026-08-04T18:38:41.364557Z","submitted_at":"2024-02-06T08:14:56Z","title":"ZeroPP: Unleashing Exceptional Parallelism Efficiency through Tensor-Parallelism-Free Methodology","version":3},"cited_work":{"arxiv_id":"2402.03791","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.03791","snapshot_observed_at":"2026-08-07T14:48:21.353669Z","title":"ZeroPP: Unleashing Exceptional Parallelism Efficiency through Tensor-Parallelism-Free Methodology","venue":"cs.DC","work_id":"2a4e59d2-dec5-40bf-9e8c-c462d443e18b","year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.380408Z"},"links":{"cited_paper":"/paper/2402.03791","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:45987c66e13003761914281cc05a30126277a253147353e0eeb83c56d9749538","observation_id":"bc3aceba-10a9-45fa-8253-c3e511b61e47","resolution":{"observed_at":"2026-08-07T14:48:21.438458Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:22.477644Z","title":null,"venue":null,"work_id":"1156669f-03ee-47ce-8f35-0192745c8ed8","year":2023},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.487601Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:285f979f3c391571d5c336d17a40ed83e35cfcb85734602b136548cdf622f954","observation_id":"c4725d6a-b362-4d74-a4c5-d1c0f4fc50a1","resolution":{"observed_at":"2026-08-07T14:48:22.523254Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:22.293085Z","title":null,"venue":null,"work_id":"962b20fa-d3ce-457e-afc3-68320366b69b","year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.589626Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:1badf77d5857df8f1b2dd3a37f2f2b958978a6c9be8dc7f4c79686c9683a19fc","observation_id":"0eafde2b-ca30-4fac-90d0-6d2862c6f6c1","resolution":{"observed_at":"2026-08-07T14:48:22.387042Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-07T14:48:20.620548Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.620548Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:8b3bad37109ee010f69026974456fa64543da421c6c3dbce30883d21bd1255df","observation_id":"8453bead-47c7-4498-9cd4-6644343dac8f","resolution":{"observed_at":"2026-08-07T14:48:20.620548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:20.687491Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.687491Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:9f06273da2c2d6c4a993981f30d9a76cb2a7f3eba54a2ac2ba5189dda36f43e5","observation_id":"f4d4e74c-11e5-44a0-8311-4a48db52eedf","resolution":{"observed_at":"2026-08-07T14:48:20.687491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:20.261724Z","title":"In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.261724Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:eabd90e8396c7a5d7e866124aa56e21c67c5fe64228e9305829e5d05604dff55","observation_id":"5fe9c6f3-6614-46d2-901f-5d8576312de6","resolution":{"observed_at":"2026-08-07T14:48:20.261724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08053","last_updated":"2020-03-13T23:45:18Z","snapshot_observed_at":"2026-08-12T10:50:46.357243Z","submitted_at":"2019-09-17T19:42:54Z","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08053","snapshot_observed_at":"2026-08-07T14:48:20.330351Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.330351Z"},"links":{"cited_paper":"/paper/1909.08053","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:4eb88ed8f4fc3549d81476ad94b8c006d595d9a8c95d91a3bc8443bf28ee644a","observation_id":"da390d11-6235-4257-90ab-8104e79a3201","resolution":{"observed_at":"2026-08-07T14:48:20.330351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16256","last_updated":"2024-08-09T02:38:07Z","snapshot_observed_at":"2026-07-06T18:19:52.121906Z","submitted_at":"2024-05-25T14:36:35Z","title":"HETHUB: A Distributed Training System with Heterogeneous Cluster for Large-Scale Models","version":2},"cited_work":{"arxiv_id":"2405.16256","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.16256","snapshot_observed_at":"2026-08-07T14:48:21.163876Z","title":"HETHUB: A Distributed Training System with Heterogeneous Cluster for Large-Scale Models","venue":"cs.DC","work_id":"4e4384d4-0355-4dc0-8309-df2629aebf7a","year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.917317Z"},"links":{"cited_paper":"/paper/2405.16256","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:4de0bf00bd55ef529d338de268c7689e2c66a70b5918177a7cc41596303ecd34","observation_id":"e3d89304-6368-4233-8443-331a9b259b4d","resolution":{"observed_at":"2026-08-07T14:48:21.225566Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:21.993499Z","title":null,"venue":null,"work_id":"e5a72efb-6498-4721-8e2b-65bfb415c615","year":2022},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.966919Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:f824ccbb2d69a11e8cb90bcb8c78f9af9353a5ad0c3b97f6a81b83fddb5d8dd6","observation_id":"ba5f2b52-9b62-493b-b648-4dd871636026","resolution":{"observed_at":"2026-08-07T14:48:22.115941Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T14:48:21.848732Z","title":null,"venue":null,"work_id":"eac7cfa9-da68-4d2c-845f-f4b3414d5180","year":2023},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:21.027815Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:28078b0f5ec9d24226cebd471edf29b5a3260aa923cb53518a4d0e0019516d1b","observation_id":"548eccde-839d-432c-8ac6-06179cd89f23","resolution":{"observed_at":"2026-08-07T14:48:21.899491Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:20.768936Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.768936Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:725d83b58fc46c2fe6c4c314f0595b64dca7e4175b06787ce367f6e63c2246a9","observation_id":"a20bd5ab-e8bc-4bd2-a5c6-2f24b92a1257","resolution":{"observed_at":"2026-08-07T14:48:20.768936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:22.154634Z","title":null,"venue":null,"work_id":"8610bdf2-52c4-4b78-97a4-d589e1a7c660","year":2023},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.872496Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:f5363df95d7563066287a773978fb2ec668467afc05948440a4dceb8552bb9a9","observation_id":"7fa37aed-2ba5-49d3-8568-bd28cdc147e5","resolution":{"observed_at":"2026-08-07T14:48:22.203290Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:19.891274Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.891274Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:97dcacb64b3bcd5cfb5b06e4746c8bcc628372d145cf3bb02db19074dfacf437","observation_id":"aa8873da-55af-4d99-b488-6209342ac42a","resolution":{"observed_at":"2026-08-07T14:48:19.891274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:17.982584Z","title":"In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.982584Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:5fc175f53d339c7f46e3f248a65847dd87b9402aafe8b12e1712c0bce2c61b91","observation_id":"774d64f4-bef4-437f-a21f-1fd8fe0691c7","resolution":{"observed_at":"2026-08-07T14:48:17.982584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:20.079392Z","title":"In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:20.079392Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:d35bf8a364de5046a5aeea58c9aa6138d9748d1170ac495a6c132204d80b67e8","observation_id":"5659a03a-9e3e-4e10-915f-61311592be5c","resolution":{"observed_at":"2026-08-07T14:48:20.079392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:48:23.383036Z","title":"In 2021 Design, Au- tomation & Test in Europe Conference & Exhibition (DATE)","venue":null,"work_id":"9a33d7a9-33a8-49d9-83a3-bbc99268ee25","year":2021},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:19.208821Z"},"links":{"citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:c9285b7ce41fb3bd32d060589905ede02d4a42f60f4d0dc4fe64e7af042c4e37","observation_id":"81e23540-055a-4a2b-b590-ef2aa62ee6f5","resolution":{"observed_at":"2026-08-07T14:48:23.436773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08756","last_updated":"2025-03-28T02:43:40Z","snapshot_observed_at":"2026-08-12T07:57:14.140016Z","submitted_at":"2024-06-13T02:31:36Z","title":"Optimizing Large Model Training through Overlapped Activation Recomputation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08756","snapshot_observed_at":"2026-08-07T14:48:17.697488Z","title":"arXiv preprint arXiv:2406.08756 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T14:48:17.697488Z"},"links":{"cited_paper":"/paper/2406.08756","citing_paper":"/paper/2505.17548"},"observation_digest":"sha256:7553329296b0039f166cf81f1a8b6c662f3909b7cf9dea35b9c35c9fefb58f1e","observation_id":"311b407c-7411-4994-9ab9-0756da03477e","resolution":{"observed_at":"2026-08-07T14:48:17.697488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.17548","last_updated":"2025-05-23T06:54:29Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-10T15:44:14.316514Z","submitted_at":"2025-05-23T06:54:29Z","title":"H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":44,"verified_exact":2,"verified_fuzzy":2},"total_outbound_references":48},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2505.17548."}