{"as_of":"2026-08-09T19:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:02fa04690aeb07b83456daad2f6abe45b3cb5b7bd464b00a9130e46778d81fa9","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T22:34:40.359485Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.01707/citation-record","integrity":"/paper/2608.01707/integrity","json":"/paper/2608.01707/citation-record.json","paper":"/paper/2608.01707"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2958.14029","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.524295Z","title":null,"venue":null,"work_id":"5420c2b4-089b-43c8-8769-f4e1d7dee163","year":2008},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.950153Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:413194916eb5a455c0be875d55308e132d02a05ba2ffa5c3b646206dd005e935","observation_id":"18fc2b75-5bd8-43f4-8404-01418de1fba3","resolution":{"observed_at":"2026-08-04T22:34:41.533967Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.216110Z","title":null,"venue":null,"work_id":"8049b1b6-09fe-4d08-8465-8796c8f7c216","year":2011},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.955914Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:7bbaf9e6ca16b0a41da4267327916b46e3701028b63dc66d79ad5445e05e3ba9","observation_id":"581388e3-858a-418d-ab45-0503df9c61b1","resolution":{"observed_at":"2026-08-04T22:34:42.221675Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:39.961903Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.961903Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:8d5bcc013405dd88c15f8fa2cbf0c8c37a3df39f2505ddbe019bd4a4d6bbb569","observation_id":"59507f41-98ec-4a8b-9b75-cc41625ed9db","resolution":{"observed_at":"2026-08-04T22:34:39.961903Z","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":"5549.36586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.496744Z","title":null,"venue":null,"work_id":"79b0a7c1-5b5f-4c6d-9e8c-a15df913d12e","year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.967645Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:b3942dcae206c1b50f08632cfa2a65729edfaf4f12c60973e172f270410b632f","observation_id":"a0735e68-ebac-40b9-be30-6edf9c43b447","resolution":{"observed_at":"2026-08-04T22:34:41.505968Z","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":null,"cited_work":{"arxiv_id":"1988.44670","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.467878Z","title":"Borkar, R","venue":null,"work_id":"a1fd9e5b-5059-4e19-a1bd-2afdc1ebf611","year":1988},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.973154Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:0401001040ef3ec530ee2a3561a23eb671f57c89b331d7102e942b56756a06d9","observation_id":"8be5b352-bf8c-44d6-8398-553cff28bd3f","resolution":{"observed_at":"2026-08-04T22:34:41.478349Z","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":"2002.10048","last_updated":"2020-02-24T02:51:06Z","snapshot_observed_at":"2026-08-08T21:15:41.788504Z","submitted_at":"2020-02-24T02:51:06Z","title":"Nuclear multipole responses from chiral effective field theory interaction","version":1},"cited_work":{"arxiv_id":"2002.10048","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.10048","snapshot_observed_at":"2026-08-04T22:34:41.437518Z","title":"Nuclear multipole responses from chiral effective field theory interaction","venue":"nucl-th","work_id":"98b2be3d-1f12-4f78-ad2a-3dfb23ca4014","year":2020},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.978215Z"},"links":{"cited_paper":"/paper/2002.10048","citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:23f3dcabf70a7cbfaed34ccdf57ec262bc6ab67b5b2fb8611018f7059c3097cd","observation_id":"a4a7192c-1ac6-4450-9787-39d180ed4cbd","resolution":{"observed_at":"2026-08-04T22:34:41.444921Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.186345Z","title":null,"venue":null,"work_id":"c8c2f6b0-3223-44f2-b83d-8b7db88dda55","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.985352Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:72187465a560f19df83af9f43cdcbfc7930339cf29ed3d4cecab2ad847a4e096","observation_id":"e239d477-8367-435c-83c1-c37f992dfa92","resolution":{"observed_at":"2026-08-04T22:34:42.192797Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.169295Z","title":null,"venue":null,"work_id":"b93761b9-5c2d-43de-b85a-8d2146f96675","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.996065Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:dccdc61cb8b1f877a7b4d1b81f3371a7e380496aa597e646bba2fddc9755ffc9","observation_id":"f3529369-3f33-46a6-b938-4c7e7b949c8a","resolution":{"observed_at":"2026-08-04T22:34:42.174522Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"1014.18175","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.379746Z","title":null,"venue":null,"work_id":"0c75b420-adf1-44e7-993e-f17441160323","year":1994},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.002332Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:89f0c1c55c29395e34319826e22c40c46cff8eaba24234e6e8f9d1bdb56e189e","observation_id":"de21b511-51fb-4be4-a9bd-c9a42f1b7b32","resolution":{"observed_at":"2026-08-04T22:34:41.393328Z","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":null,"cited_work":{"arxiv_id":"7801.34416","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.341185Z","title":null,"venue":null,"work_id":"bdf6477a-8c70-48c5-954c-6b773a8c42b7","year":2021},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.007930Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:175ebe57b41a5d69ff4305f32cc01185da50a344fa45b0a3988f72a3da4a71eb","observation_id":"9e372520-9e2c-453b-918e-a0b2ae5c2030","resolution":{"observed_at":"2026-08-04T22:34:41.353953Z","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":null,"cited_work":{"arxiv_id":"8958.37504","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.301483Z","title":null,"venue":null,"work_id":"c4c33595-7e64-46fe-974d-47e3691bd10a","year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.013242Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:b5ed25dbb2b9c1da818d0fff95fac75f3e646e1065c2ed1f55fbf3f047733b9c","observation_id":"3827ade5-c099-4dcb-9a4c-9138769c1c56","resolution":{"observed_at":"2026-08-04T22:34:41.311202Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.153095Z","title":null,"venue":null,"work_id":"38d5dd36-eb3a-4ee1-b857-eb614e4d86c3","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.018363Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:a90a2fbd425f56735abbbe35cdaa8a24cd545aef72458e9255ea17d3d87ff217","observation_id":"5628b6ac-8c44-4707-89ab-adb35c09feeb","resolution":{"observed_at":"2026-08-04T22:34:42.158607Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"2510.03891","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.266628Z","title":null,"venue":null,"work_id":"8ea7d18f-8659-4129-9200-e08aa1d24333","year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.028290Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:e97f50dcfac139c86a0ad797fb4a5a4aeb386d93d780fd28b777b54649802c80","observation_id":"43a26e85-7a46-42e6-ad38-f2bf0cf12896","resolution":{"observed_at":"2026-08-04T22:34:41.277575Z","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":null,"cited_work":{"arxiv_id":"7305.1953","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.235279Z","title":null,"venue":null,"work_id":"c586370d-f0fb-4f63-934a-6a1b8cbdb569","year":1953},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.033430Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ca21d8bb7bb5b8ee8c033ea4a1e7996469535fbc0ae9545882b38ecf2dba2591","observation_id":"4487e113-6a24-4e1f-aa75-5615b8216d5b","resolution":{"observed_at":"2026-08-04T22:34:41.245261Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.038608Z","title":null,"venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.038608Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ce8ee848a4d2cbb3e49651f0ad129584d717992722c23acfb9b52f139725baad","observation_id":"04d95775-6a23-424b-886b-7edd970121c6","resolution":{"observed_at":"2026-08-04T22:34:40.038608Z","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":"2026-08-04T22:34:42.121451Z","title":"Dally and C.L","venue":null,"work_id":"9877746d-2fac-46c5-bc0a-c072086ffcbd","year":1986},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.043929Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:3730bd322abaa7b78db597a09954213466227ef8524816f72102ee8cae15a733","observation_id":"02a1e591-70b5-4c69-9b64-e15efff78627","resolution":{"observed_at":"2026-08-04T22:34:42.126282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-04T22:34:40.048591Z","title":"Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.048591Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:d2b5c7a9a82233474f5595c8e9498fcb9cafece5814832ad8ac4b723e6d32426","observation_id":"09c234ec-6368-4446-b988-e03b4f6ae825","resolution":{"observed_at":"2026-08-04T22:34:40.048591Z","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":"2026-08-04T22:34:42.104610Z","title":"Fagg and Jack J","venue":null,"work_id":"a618032a-0db0-4172-9bb0-84203a16cb63","year":2000},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.054218Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:91c5f8e4a6dc12a7be0db91eb48287db9acb82357b19829fa2fcb4974f3384f8","observation_id":"dd87335d-6bf1-4d9d-8cbf-b24e540a2420","resolution":{"observed_at":"2026-08-04T22:34:42.110368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.059118Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.059118Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:a6e435513b6a91d7535e5608128d5fa8aab0b503aa7690e09c5c5bee220bf466","observation_id":"156fecce-aced-4cb8-ac83-e8fd0f7d0cc7","resolution":{"observed_at":"2026-08-04T22:34:40.059118Z","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":"6348.36968","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.135431Z","title":"Gherghescu, Vlad-Andrei B˘adoiu, Alexandru Agache, Mihai-Valentin Dumitru, Iuliu Vasilescu, Radu Mantu, and Costin Raiciu","venue":null,"work_id":"353bf55a-d7a6-4448-9152-a9bd8e13b849","year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.069205Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ca8d72a5c42b356f311b60b166789a07fe6d589720b8f78d908780e83e189e53","observation_id":"be52fb03-2fd8-4aec-abfc-f95d51415135","resolution":{"observed_at":"2026-08-04T22:34:41.147172Z","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":null,"cited_work":{"arxiv_id":"8436.20184","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.104845Z","title":null,"venue":null,"work_id":"00d3c1b7-d29b-43ec-ba40-73d1829e706d","year":2011},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.074119Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c98c149798a7da9e3f1959a6e2e609260d69dd04cf04e40d300746a19ad64091","observation_id":"57853d43-bc63-4cba-a488-67cefe6a3c8b","resolution":{"observed_at":"2026-08-04T22:34:41.113866Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.087734Z","title":null,"venue":null,"work_id":"8d67932f-90fd-4e45-bbb7-058e845f539d","year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.079145Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:2b8e947cf950da318a363a2dd8a88f6ad402d22c51aa7d34e74ff90c2f1b8783","observation_id":"baf93b22-cc4a-406d-9fe5-25626d5e6dae","resolution":{"observed_at":"2026-08-04T22:34:42.092722Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.070973Z","title":null,"venue":null,"work_id":"db124d8a-a124-43dc-8571-0fa0eaa5c408","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.084212Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:54594ad3c787ccacc34994c217fa39e05932eb94d4b730a06b8f478b685b5f8c","observation_id":"0eb73829-c26e-4e5d-8b99-1825c5b192f8","resolution":{"observed_at":"2026-08-04T22:34:42.076778Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.053412Z","title":null,"venue":null,"work_id":"256cf032-3cd4-45d6-b80f-9f2e2c8b8a69","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.089133Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:bc997612c1b02db662c73bcf9a4033404755c46c647a95213ae06a542d43c633","observation_id":"62009b7d-a0a7-4938-9c4c-6a3a92cbbdf8","resolution":{"observed_at":"2026-08-04T22:34:42.059793Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.031722Z","title":null,"venue":null,"work_id":"e8d7d7f1-13d7-46a8-98d5-ec3945de0df4","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.094494Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:0be6e840fadd94de152e9f1674c4ff356a7728fa5f166d36dbb333168421f88f","observation_id":"0c622f42-cc0f-4f92-bd97-04d353b84684","resolution":{"observed_at":"2026-08-04T22:34:42.037576Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.011469Z","title":null,"venue":null,"work_id":"6d5a8b0c-1b48-4e0c-a722-047c0bbd1b23","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.099528Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:da33d46204cc3110ef5902badd1200ca116bda6ff71cecd607d731ffe520ae45","observation_id":"e4ea3218-e900-4379-9c56-4473f0d2e59a","resolution":{"observed_at":"2026-08-04T22:34:42.017137Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"2568.15925","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.067874Z","title":"Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A","venue":null,"work_id":"a8c9424a-6c3d-41bd-bc9e-7c7452da35f3","year":2009},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.104576Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:17b2657fbd6290845b37ca7de82ae328b062238f662be633e132c42d93a95c7e","observation_id":"62483e9f-9abf-4773-804f-c0512b2d4f9c","resolution":{"observed_at":"2026-08-04T22:34:41.082790Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.991400Z","title":"Le, Yonghui Wu, and Zhifeng Chen","venue":null,"work_id":"e5b60fe4-8a82-4348-944a-e12708455313","year":2019},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.110084Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:01c722e3d7cbc4575290f3ab5c449f031dda0c293eed8424bf90190fb39e8db2","observation_id":"874d8a0a-3069-41b1-9362-9fb4aefbcacc","resolution":{"observed_at":"2026-08-04T22:34:41.997424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.973918Z","title":null,"venue":null,"work_id":"34592e0e-46c9-4232-a93a-7069c8ad6e24","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.114724Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:22ef7e41fc58f1922f88c21b950ca1eecc6c9ab592d21c99077a066b47e722f9","observation_id":"adf44b9a-d515-480a-9e44-bbacd8e9fafd","resolution":{"observed_at":"2026-08-04T22:34:41.979088Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"0085.18100","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.027620Z","title":null,"venue":null,"work_id":"36d640e0-a112-419a-9303-c47ace1dcd39","year":2010},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.119658Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c4da9a2089f918ad3bd91652fd3d87b8ea573ee5de41a74e6a12d18518f0d426","observation_id":"c09d0bdd-814d-4019-b904-2a67e51b0e52","resolution":{"observed_at":"2026-08-04T22:34:41.043031Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.946099Z","title":null,"venue":null,"work_id":"bc854475-ca58-4c43-8504-d7d3ff43ef50","year":2023},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.124726Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:a43ec36051ad7d0088ff7b923cb1c03388d357690d478c4c2238aba9a6d5653a","observation_id":"7a656d9d-93ae-4a5a-bc70-7d71c7c666d9","resolution":{"observed_at":"2026-08-04T22:34:41.951272Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.129575Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.129575Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c09bdd555f0d23f36a0d2798a5a192b1e437e9e2b9958bb939166b83ea3553af","observation_id":"1af23aff-3647-4ec2-aad2-073374df4211","resolution":{"observed_at":"2026-08-04T22:34:40.129575Z","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":"1993.28966","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.958138Z","title":"Kessler and J.L","venue":null,"work_id":"0e8813ab-6d4c-4602-8fcc-1d1575d4ed37","year":1993},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.136051Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:6e3a4c818b5c7b2bfe7ea7119381da692bb2333a26ccf6221c5f05771f95d61c","observation_id":"786bb497-71e6-432c-9a9a-1fe8b9178734","resolution":{"observed_at":"2026-08-04T22:34:40.967585Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.141010Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.141010Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:f670a88c5dca19cd61f781a9fe812d43b366944c484a3659e68b2a9b66d19ff8","observation_id":"9b53aaf4-8967-4fd3-a305-18d1ac436cf9","resolution":{"observed_at":"2026-08-04T22:34:40.141010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21680","last_updated":"2025-02-06T22:12:21Z","snapshot_observed_at":"2026-08-09T13:55:48.879033Z","submitted_at":"2024-10-29T03:02:53Z","title":"Revisiting Reliability in Large-Scale Machine Learning Research Clusters","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21680","snapshot_observed_at":"2026-08-04T22:34:40.145931Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.145931Z"},"links":{"cited_paper":"/paper/2410.21680","citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:8169ea1fcb7955a7a69d535fd739375a02ef7afa75743ecbf9eb4dee38bb81dc","observation_id":"db99687c-c3a2-42c1-8c99-1b83900c693d","resolution":{"observed_at":"2026-08-04T22:34:40.145931Z","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":"2026-08-04T22:34:41.921053Z","title":null,"venue":null,"work_id":"fd9d6b35-3706-419e-9663-0f44d717305b","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.151191Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:b12888c1b053435c3d56186a0061bbb87a0c81329b8954ed6fb51579f7253958","observation_id":"8d4c737c-6abd-439e-9b8c-f96bb2de9d0c","resolution":{"observed_at":"2026-08-04T22:34:41.929286Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"3408.36634","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.884247Z","title":null,"venue":null,"work_id":"78158f62-8c4b-4943-bf35-d03bb9796d70","year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.156382Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:24350a3fb5aab21e7027ddccd19ea415f00f4f9a1f326242d34d8b656fed603f","observation_id":"c2959dd4-19f1-4445-aa6e-25cb70f98c3b","resolution":{"observed_at":"2026-08-04T22:34:40.892818Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.896919Z","title":null,"venue":null,"work_id":"77db9702-4413-4020-bbf2-302c94e3228e","year":2022},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.166133Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:7f206a1809d9106d490deb8493258e8c0cc58ac2ca4b49857ed2da7705909332","observation_id":"20cd9372-7d1a-4ca5-8299-eb3f344e769a","resolution":{"observed_at":"2026-08-04T22:34:41.905577Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":"5745.30057","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.852759Z","title":"Mellette, Alex C","venue":null,"work_id":"a16a1854-2e90-4cdd-980f-cb67b50d42b5","year":2016},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.170870Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:6adec7a9b88b5c76feabf3c6ba7062c0d3e8daa023157ff6aadc5771aef6b8fb","observation_id":"76e46d36-15ec-4b02-995a-c841a0f52671","resolution":{"observed_at":"2026-08-04T22:34:40.862865Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.866285Z","title":null,"venue":null,"work_id":"fc27a3c4-8c56-4826-9d87-fb6bf7cbec26","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.176316Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:115a87c1cda877898df618fdbb77d2afb18e26d5543db6bbcc3f0cf7d6df2cac","observation_id":"c1f4f498-274b-4d7f-97cf-8473fff264d9","resolution":{"observed_at":"2026-08-04T22:34:41.874257Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.186623Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.186623Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:e7bd17c09e66c6e6556e0051ed6e6742b67551943c6c567f83fd55715dc529cf","observation_id":"19bab8f7-f471-478d-9f03-5c3954e37323","resolution":{"observed_at":"2026-08-04T22:34:40.186623Z","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":"1990.63649","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.765424Z","title":"Nickolls","venue":null,"work_id":"bca3a957-decb-4f3d-9ed7-2557f9e557d2","year":1990},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.191438Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:050aa5bde09ab11e0830d130eb8172981e0f5dfdaa5b0073bdf72d40cf89fec8","observation_id":"bbaed29b-4a87-4e6c-ad59-c7dd63808d71","resolution":{"observed_at":"2026-08-04T22:34:40.775650Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.840235Z","title":null,"venue":null,"work_id":"87f484e4-5b8d-4529-8803-281175372b25","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.196420Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ecd43786ac14b84a3678b9dde179624d95203c7e4ac37a8166f6f1c2cd54ee28","observation_id":"1e965145-a26a-4526-9f97-131f3256d4f8","resolution":{"observed_at":"2026-08-04T22:34:41.850311Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.819834Z","title":null,"venue":null,"work_id":"1a2b9664-04fa-42d0-b163-edceac5a1d32","year":2022},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.201250Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:f87172c3370d9ecb8acc8df2f519281d3eb93082d465d63fe20eeb810864d232","observation_id":"3f73b3d3-1432-46fe-ae38-b57b04c96b4e","resolution":{"observed_at":"2026-08-04T22:34:41.825631Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.798153Z","title":null,"venue":null,"work_id":"f162b8a0-77ef-43a8-af16-9f0a1be0ae02","year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.206453Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:2983ae1d8bcbeb0265f7e0e06dd7e774e4cdd48273af0e5e9f7f49fae4c3210f","observation_id":"63878261-c4ff-4375-9c8d-6b1ed313e3af","resolution":{"observed_at":"2026-08-04T22:34:41.803813Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.776265Z","title":null,"venue":null,"work_id":"30bee170-abe0-471f-9e4c-9c434ada9c47","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.211188Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:9743170ad9d14f93977761e737c03fcb6d4b238d81922f236ce147e0b8601050","observation_id":"e69f0a4c-09a8-464b-970e-a30c795f5daa","resolution":{"observed_at":"2026-08-04T22:34:41.782230Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.757791Z","title":null,"venue":null,"work_id":"3619f8b9-cef5-40a2-9ec1-327a560c41e4","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.216121Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:7e9a65e12f4b0558f2be6706871d9faa218d93dc4ae57f1a48d431cac8bb6143","observation_id":"929c780a-19cf-43c4-a882-0def36303999","resolution":{"observed_at":"2026-08-04T22:34:41.763083Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.741274Z","title":null,"venue":null,"work_id":"aa3b373a-a45f-4e35-b5b4-73202003f7f3","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.221169Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:f0f18352dc14141936c51e40aac8e96c62293661e1030848e263d11c655c007c","observation_id":"5720e8ca-10e4-4dd8-8aaf-724906431ef7","resolution":{"observed_at":"2026-08-04T22:34:41.746051Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.725247Z","title":null,"venue":null,"work_id":"64c82cbb-7cdb-4d38-bde1-35bb9750d049","year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.226521Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:3fcc28c99407196b98b771d3cf9eb5504662427be88162db1bb9e1cfd2a6d366","observation_id":"ac246c67-bcac-4a42-b305-9f7f1408ac70","resolution":{"observed_at":"2026-08-04T22:34:41.730390Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.231508Z","title":"Pjesivac-Grbovic, T","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.231508Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ebb4ab584e51b7be1603512068f51753db4510035be2479c69e7a3e7f7a70f04","observation_id":"40a90488-7b56-4d86-8c56-070b4ff97225","resolution":{"observed_at":"2026-08-04T22:34:40.231508Z","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":"1168.19211","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.735687Z","title":null,"venue":null,"work_id":"eb9933fb-31bf-4bee-b8c1-30b84d74c2f2","year":2010},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.237463Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:5045484765bdb57167be208c8b0ac5264c46cc11f3723cfb39d6a526c3c0f168","observation_id":"c8bac79f-409d-47ee-97f1-c2d3816d6914","resolution":{"observed_at":"2026-08-04T22:34:40.744958Z","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":null,"cited_work":{"arxiv_id":"4216.35442","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.702432Z","title":null,"venue":null,"work_id":"57ead40f-a35d-459c-8baf-0c80ed1c6993","year":2022},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.242865Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:d0651cfe6ac2d7d4b4a63a01948b2f1a804d11cafad396dc2794c5faf8f5fc0a","observation_id":"7b7b1fb8-3971-4998-94d5-b85d44bf7be0","resolution":{"observed_at":"2026-08-04T22:34:40.713559Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.248508Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.248508Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:013677a00088f6472fb3720640202c2fe3a39c83b0a90ff2e44ba09b33b2c899","observation_id":"6ca06087-1318-43fd-a472-18b810908603","resolution":{"observed_at":"2026-08-04T22:34:40.248508Z","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":"2026-08-04T22:34:40.253187Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.253187Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:89f92f0e679cd3d801a807c53f69a4fa8078aac563506825f433010836176f27","observation_id":"3739bffd-fa94-4260-b9d0-1e177ce620c3","resolution":{"observed_at":"2026-08-04T22:34:40.253187Z","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":"6884.29669","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.639681Z","title":null,"venue":null,"work_id":"bbfe4483-77a0-4e44-9e18-49591e21618d","year":2016},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.258202Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:2c419c973990b7d8e75535d149a7f2c974b7412433752283b327ee11fb7c4630","observation_id":"9c873333-fb54-42b8-bbad-27defae74516","resolution":{"observed_at":"2026-08-04T22:34:40.652051Z","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":null,"cited_work":{"arxiv_id":"0036.21458","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.603164Z","title":null,"venue":null,"work_id":"8198c086-d8d5-4e1c-8a77-94b5dff8fd72","year":2012},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.263620Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c6bc4ed125a0be5400a73ab3c5cc5a4fe81a5189d5073a14b7da20b90f06e381","observation_id":"2220af5e-7e10-418a-890b-c2e6239eb8f1","resolution":{"observed_at":"2026-08-04T22:34:40.612194Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.707722Z","title":null,"venue":null,"work_id":"d43896b9-ef4d-4bb9-9f1b-524f0400fad8","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.269427Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:1029d88302febc29d908fcd861c820696b1ca49756392baaf363cfe7e74a92c5","observation_id":"75190304-1f1b-4456-b966-6a594d118b78","resolution":{"observed_at":"2026-08-04T22:34:41.713568Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.672061Z","title":null,"venue":null,"work_id":"4a017133-25cb-40d2-9969-db3faf23b024","year":2023},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.280225Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:e629c9b0f92638ee8d824254962faa414c41bc43fdb23facc8bff262570cd46e","observation_id":"66589569-f6ae-4c71-9e7c-b7972f937527","resolution":{"observed_at":"2026-08-04T22:34:41.677939Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.653189Z","title":"Shipman, Richard L","venue":null,"work_id":"afd15c75-3b72-4e80-8b4f-310425a2a1de","year":2007},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.285742Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:5083d03cbe69558c1432925a97807dd817ecfdffcc6c0d7f8515fdf5031b501e","observation_id":"aab4f0a4-d537-44e2-86de-10265426eeae","resolution":{"observed_at":"2026-08-04T22:34:41.658798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1909.08053","last_updated":"2020-03-13T23:45:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-04T22:34:40.291136Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.291136Z"},"links":{"cited_paper":"/paper/1909.08053","citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c9d40bb9e4a5ef0a733b6f467cfe3888ea158107ee5b4864f8c80a1f93e5458a","observation_id":"ae1e958d-e67c-4c1b-b93d-4d487ea4443d","resolution":{"observed_at":"2026-08-04T22:34:40.291136Z","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":"2026-08-04T22:34:40.297272Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.297272Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:7207b5a12a2902624339ec2b3fa6c9e1ad4c8b02b0ab1ed45c489759ddc680b9","observation_id":"3b02c218-8f37-4e95-a4ce-d1ab954c549b","resolution":{"observed_at":"2026-08-04T22:34:40.297272Z","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":"2026-08-04T22:34:41.634346Z","title":null,"venue":null,"work_id":"2c601d57-e69a-478c-96fc-3e65e30e304d","year":null},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.304302Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:699c7084b067b4f089c49c723e5a1b6311ce6492d7e5473c3a3aa2136a5b958b","observation_id":"85ae2dbc-97cf-4b91-8294-ee84c22a3d88","resolution":{"observed_at":"2026-08-04T22:34:41.640273Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.613468Z","title":null,"venue":null,"work_id":"30ca0591-61e4-409a-a59c-a963b0b30f0f","year":1996},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.315664Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:53d1e5cdc1701f35c22e69af78d3c9c12b34d1eb8a69dfcfb7faadb4b7437356","observation_id":"a9242039-46e8-408e-b6af-7a1f8c89dade","resolution":{"observed_at":"2026-08-04T22:34:41.619624Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.595271Z","title":null,"venue":null,"work_id":"6efdfe2f-4f0a-4fa4-92e0-859b7bd510d4","year":2003},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.321048Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:fe8a1bda5d9b48b6b0cc3ec3061dec9a67788c26872f00e730f4d5d7741ae0d7","observation_id":"df265ccb-4a13-407b-be95-46154a73523f","resolution":{"observed_at":"2026-08-04T22:34:41.600433Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.327839Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.327839Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:2f7255efc4a4478b8edc3cb3df79958d572f8ad59110052a580a344e3bfc3d93","observation_id":"a8bb16eb-3889-425b-bf36-c2c5421248c2","resolution":{"observed_at":"2026-08-04T22:34:40.327839Z","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":"2026-08-04T22:34:41.572535Z","title":null,"venue":null,"work_id":"b8bffe03-3e9a-4821-b90f-b5768dc41331","year":2023},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.333082Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:19b6fc2180085cd2cb4c5f6185f916dd2f8ef45b5ce1297eff895f3bbafdd0ec","observation_id":"f392ec9b-c2ee-49eb-b194-2a5f2b988a78","resolution":{"observed_at":"2026-08-04T22:34:41.579802Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1611.06334","last_updated":"2016-11-19T10:02:40Z","snapshot_observed_at":"2026-07-06T05:19:17.255376Z","submitted_at":"2016-11-19T10:02:40Z","title":"A Survey of Methods for Collective Communication Optimization and Tuning","version":1},"cited_work":{"arxiv_id":"1611.06334","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.06334","snapshot_observed_at":"2026-08-04T22:34:40.494021Z","title":"A Survey of Methods for Collective Communication Optimization and Tuning","venue":"cs.DC","work_id":"e61dde13-b5f4-4f8c-b829-5d5cc416a9fb","year":2016},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.339363Z"},"links":{"cited_paper":"/paper/1611.06334","citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:c868fb23064fdc2e6cf3b9d2986737b253843642e2f323ed7c546340a4aa49e8","observation_id":"91774b1c-b355-4077-be98-f127c9d1c640","resolution":{"observed_at":"2026-08-04T22:34:40.501346Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/ipdps.2011.219","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"2bad86ae-5844-45bf-95a3-84ab0dd64166","year":2011},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.346233Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:d98e0093f70d35d60510d30a9f05f5c1f2c2acc59c9ba18123e0f09cf987c956","observation_id":"2a16dd41-3ad5-4b18-993b-8a136a6720b7","resolution":{"observed_at":"2026-08-04T22:34:40.415780Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.351689Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.351689Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ecccf02df1998cf4015170217d77cb361b538cac7d81793717ffd56d4e2e4ee0","observation_id":"e1a7deb1-dbbb-4792-998f-b8079b6096ea","resolution":{"observed_at":"2026-08-04T22:34:40.351689Z","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":"2026-08-04T22:34:41.547388Z","title":"schedule","venue":null,"work_id":"747ce299-0c98-4701-91aa-e615e1a4c5cd","year":2024},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.359485Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:d269716df99489b165c495a52b356df7ec1fb61974de3cd21fd4f532ca950f09","observation_id":"be8f0004-8bf7-46df-a901-070ec0982486","resolution":{"observed_at":"2026-08-04T22:34:41.555102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:41.690689Z","title":null,"venue":null,"work_id":"1e3fe8ff-ba4d-4ab6-aae6-6b90b9f93b08","year":2005},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.274846Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:d81e5aaf58fd38ba26422aec0544c8ab2eafb2992551572a0ac3de84d0f3bde4","observation_id":"d0020c25-1637-4ef1-9048-fab0c68c4cf5","resolution":{"observed_at":"2026-08-04T22:34:41.695877Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:42.137115Z","title":"Comput.: Pract","venue":null,"work_id":"bb4c38b4-fb61-4579-b412-56dd581af2c2","year":2007},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.023345Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:ed87bb15c106beb03758279f37035b149d793a7228c7dd797fa335629b68f4a6","observation_id":"775b8ae1-0cea-4b33-86c2-fec999bf5196","resolution":{"observed_at":"2026-08-04T22:34:42.142024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:39.990722Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:39.990722Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:0cbb491e8c770b0b7ea8576186593f3d85b9caef3866b20df273ea2b1bc138dd","observation_id":"c1e5b612-7b47-4ee0-9347-ee1434c41da6","resolution":{"observed_at":"2026-08-04T22:34:39.990722Z","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":"2026-08-04T22:34:40.310536Z","title":"InProceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (SIGCOMM ’15)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.310536Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:0ce6027a7927e572f9ff0641374c4dda4be78c65f6bf3b4cf8eadfbafdb74088","observation_id":"4384cce6-d820-42e2-a40f-cc0d4b8e21d3","resolution":{"observed_at":"2026-08-04T22:34:40.310536Z","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":"8532.32785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T22:34:40.817985Z","title":"In Proceedings of the Internet Measurement Conference 2018 (IMC ’18)","venue":null,"work_id":"95ce2e1a-1960-4b33-ac36-a4ca8bc6adbd","year":2018},"citing_paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-04T22:34:40.181587Z"},"links":{"citing_paper":"/paper/2608.01707"},"observation_digest":"sha256:751769f9179f3beb42ce0e9a824142b03ab435717d61eb0742dc23f42d8668ee","observation_id":"6aba45c4-6ee0-4b44-82c4-62b6770783af","resolution":{"observed_at":"2026-08-04T22:34:40.827472Z","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"}}],"paper":{"arxiv_id":"2608.01707","last_updated":"2026-08-03T05:14:08Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-08T23:37:19.588953Z","submitted_at":"2026-08-03T05:14:08Z","title":"On Topology's Role in ML Training Performance"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":17,"parse_uncertain":0,"unresolved":45,"verified_exact":7,"verified_fuzzy":6},"total_outbound_references":75},"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 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2608.01707."}