{"as_of":"2026-08-09T19:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3f5ad4fa17633c8c9fde80ba5a16a6ca93f55ae0d16c08bba3aa898bc98851bd","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:43:29.427195Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"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/2507.02173/citation-record","integrity":"/paper/2507.02173/integrity","json":"/paper/2507.02173/citation-record.json","paper":"/paper/2507.02173"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2204.05862","last_updated":"2022-04-12T15:02:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-12T15:02:38Z","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05862","snapshot_observed_at":"2026-08-06T20:43:25.297350Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.297350Z"},"links":{"cited_paper":"/paper/2204.05862","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:8f64d5a34ee5423db6d48af99159065183a0b0ca9081807a57200368773f223b","observation_id":"d92ffc23-8a64-46af-bfa4-d4db85d4754d","resolution":{"observed_at":"2026-08-06T20:43:25.297350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08073","last_updated":"2022-12-15T06:19:23Z","snapshot_observed_at":"2026-08-02T04:53:58.766070Z","submitted_at":"2022-12-15T06:19:23Z","title":"Constitutional AI: Harmlessness from AI Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08073","snapshot_observed_at":"2026-08-06T20:43:25.373440Z","title":"Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.373440Z"},"links":{"cited_paper":"/paper/2212.08073","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:fa2e9adc603ee0f8c972933e918e7f198bbccfc34434a6851660657f8e61eb4e","observation_id":"7157dea3-8c15-41b8-9bb5-25ef58a14369","resolution":{"observed_at":"2026-08-06T20:43:25.373440Z","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":"2026-08-06T20:43:25.475454Z","title":null,"venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.475454Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:7fa7077935e6e3af417f0cfff5c2f8e94b7254c376b9c286affcf6789bc2a094","observation_id":"c42a73a5-7816-488d-88f3-0f7fe511240e","resolution":{"observed_at":"2026-08-06T20:43:25.475454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14207","last_updated":"2024-04-08T05:38:50Z","snapshot_observed_at":"2026-07-06T17:33:41.634820Z","submitted_at":"2024-02-22T01:20:17Z","title":"Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14207","snapshot_observed_at":"2026-08-06T20:43:25.630857Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.630857Z"},"links":{"cited_paper":"/paper/2402.14207","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:47c4ef8ae873191cc0781925c8f089f3ac5f2e983320ec41fbcae5f81aa0ff98","observation_id":"1fca4070-72b2-4e17-945a-71322eb9b07a","resolution":{"observed_at":"2026-08-06T20:43:25.630857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08701","last_updated":"2024-02-13T18:37:25Z","snapshot_observed_at":"2026-07-06T15:54:58.589775Z","submitted_at":"2023-07-17T17:59:40Z","title":"AlpaGasus: Training A Better Alpaca with Fewer Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08701","snapshot_observed_at":"2026-08-06T20:43:25.745977Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.745977Z"},"links":{"cited_paper":"/paper/2307.08701","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:19d46755bca17c82c0f67d499c3ea6cc9c5f27909db7aacb5a32c4c8c6413564","observation_id":"bd415e0a-8f97-4623-bc54-0cedd279b351","resolution":{"observed_at":"2026-08-06T20:43:25.745977Z","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":"2026-08-06T20:43:32.597527Z","title":null,"venue":null,"work_id":"dc7506e4-6b08-447d-ab17-89bce1755e65","year":2017},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.795130Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:3078520a5437179e0a12dc7d972131cec6cddbb031d7e1c60b2a94571b7482ed","observation_id":"1f435057-e261-4bd1-b10b-2051be45f099","resolution":{"observed_at":"2026-08-06T20:43:32.694358Z","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":"2026-08-06T20:43:32.442584Z","title":null,"venue":null,"work_id":"3e8f1a67-1c1a-4c15-ab0b-80c2fb9347de","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.886740Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:edd340977b455a3e4d062133ec3ccdc01361c9e1ca3857b89591e2ca98b57fd0","observation_id":"c98a11c4-9a41-44d3-8c36-fba22a53a96e","resolution":{"observed_at":"2026-08-06T20:43:32.483099Z","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":"2026-08-06T20:43:25.973794Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:25.973794Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:2f5dd373d6ebf3233b037f53a2403f7baeec003b27c03f48423a0bd7b51b280e","observation_id":"8fd487ea-fefa-48ab-8426-a33c9be58a96","resolution":{"observed_at":"2026-08-06T20:43:25.973794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17179","last_updated":"2024-02-09T00:13:46Z","snapshot_observed_at":"2026-07-06T16:25:25.534843Z","submitted_at":"2023-09-29T12:20:19Z","title":"Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17179","snapshot_observed_at":"2026-08-06T20:43:26.040552Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.040552Z"},"links":{"cited_paper":"/paper/2309.17179","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:727c33afe0b66670348ee5a072beb21280fd7eef07f3677b18189a68d747626c","observation_id":"2a1f5f4c-bd4b-4007-af55-873c9248df71","resolution":{"observed_at":"2026-08-06T20:43:26.040552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10345","last_updated":"2024-05-16T01:30:47Z","snapshot_observed_at":"2026-08-06T06:21:50.467402Z","submitted_at":"2024-05-16T01:30:47Z","title":"Machine Learning Driven Biomarker Selection for Medical Diagnosis","version":1},"cited_work":{"arxiv_id":"2405.10345","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.10345","snapshot_observed_at":"2026-08-06T20:43:30.499702Z","title":"Machine Learning Driven Biomarker Selection for Medical Diagnosis","venue":"q-bio.QM","work_id":"55829cb2-2188-4c7c-a137-a8211a813fa4","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.106281Z"},"links":{"cited_paper":"/paper/2405.10345","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:98a05825ca18dbda8e0f97397957091a66dd7c26a4d5fbc25fdda44dc45c0c94","observation_id":"03c7ab66-53b6-4fd8-af83-8aca3e33e2d6","resolution":{"observed_at":"2026-08-06T20:43:30.543205Z","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":{"arxiv_id":"2406.00330","last_updated":"2024-12-05T05:18:53Z","snapshot_observed_at":"2026-08-07T22:26:10.084265Z","submitted_at":"2024-06-01T07:13:20Z","title":"Magnetic ground state of monolayer CeI$_{2}$: occupation matrix control and DFT+U calculations","version":2},"cited_work":{"arxiv_id":"2406.00330","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.00330","snapshot_observed_at":"2026-08-06T20:43:30.337854Z","title":"Magnetic ground state of monolayer CeI$_{2}$: occupation matrix control and DFT+U calculations","venue":"cond-mat.mtrl-sci","work_id":"c54932f1-a225-467f-a874-2e320e39e4d7","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.169275Z"},"links":{"cited_paper":"/paper/2406.00330","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:bc68cec24bd27297d5aca4e463499371bf86ae9d58186f1e80146fe1c4a00b8d","observation_id":"78ca6f3f-a653-4381-9165-dc2028650198","resolution":{"observed_at":"2026-08-06T20:43:30.416067Z","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":{"arxiv_id":"2402.04792","last_updated":"2024-02-29T20:59:17Z","snapshot_observed_at":"2026-08-09T00:20:15.609286Z","submitted_at":"2024-02-07T12:31:13Z","title":"Direct Language Model Alignment from Online AI Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04792","snapshot_observed_at":"2026-08-06T20:43:26.226540Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.226540Z"},"links":{"cited_paper":"/paper/2402.04792","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:2d9151262648b707aadeb3ef7a405e42122869b283e23c506b41e022bdc458aa","observation_id":"a140d1b5-77dc-4353-9028-07de6ffee18f","resolution":{"observed_at":"2026-08-06T20:43:26.226540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12178","last_updated":"2024-01-22T18:09:52Z","snapshot_observed_at":"2026-08-05T12:31:24.577801Z","submitted_at":"2024-01-22T18:09:52Z","title":"In-Context Learning for Extreme Multi-Label Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12178","snapshot_observed_at":"2026-08-06T20:43:26.295700Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.295700Z"},"links":{"cited_paper":"/paper/2401.12178","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:073ac262078008a4989ad8485e9649269355fe330c6bd6caa50adc27e0dbc9cf","observation_id":"b5d6526b-a6de-4671-adf1-a677ebe22893","resolution":{"observed_at":"2026-08-06T20:43:26.295700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10963","last_updated":"2024-06-25T03:14:10Z","snapshot_observed_at":"2026-08-05T17:51:38.668510Z","submitted_at":"2024-02-13T20:16:29Z","title":"GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10963","snapshot_observed_at":"2026-08-06T20:43:26.369435Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.369435Z"},"links":{"cited_paper":"/paper/2402.10963","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:de636d5a36f94df727cdcdbf12e0bcf4f1a6ca92244d8322e40e94174396e460","observation_id":"9f34cb8c-8df1-4872-9ef5-ba94cc628c77","resolution":{"observed_at":"2026-08-06T20:43:26.369435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07691","last_updated":"2024-03-14T07:47:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-12T14:34:08Z","title":"ORPO: Monolithic Preference Optimization without Reference Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07691","snapshot_observed_at":"2026-08-06T20:43:26.410004Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.410004Z"},"links":{"cited_paper":"/paper/2403.07691","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:236549b3a110f320b7836945ba9d6248f75235010d797d139fb8130922c11fea","observation_id":"64e5b573-db6f-472b-a8b3-7281bd471371","resolution":{"observed_at":"2026-08-06T20:43:26.410004Z","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":"2026-08-06T20:43:32.224395Z","title":null,"venue":null,"work_id":"7a6e147b-844e-4429-9bd0-f21a74fe5846","year":2017},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.497763Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:aa5de7d91a381fe759272bd0cd1159a01b9de77ea23d5ae65717d33d9283c499","observation_id":"d58765e0-60b8-41ca-8842-80620c7b26f3","resolution":{"observed_at":"2026-08-06T20:43:32.320909Z","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":"2010.05848","last_updated":"2020-10-12T16:53:00Z","snapshot_observed_at":"2026-08-06T05:34:10.204310Z","submitted_at":"2020-10-12T16:53:00Z","title":"Human-centric Dialog Training via Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.05848","snapshot_observed_at":"2026-08-06T20:43:26.565724Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.565724Z"},"links":{"cited_paper":"/paper/2010.05848","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:666bdefdd2783d64177b21ec3641c0ca2e4c773c16a7c786841e01faa9ee6ec3","observation_id":"c13c74ba-0510-45eb-b4da-2f6fa9eccded","resolution":{"observed_at":"2026-08-06T20:43:26.565724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00183","last_updated":"2024-07-02T16:21:05Z","snapshot_observed_at":"2026-08-05T00:14:15.034077Z","submitted_at":"2024-06-28T18:38:30Z","title":"Top-philic Machine Learning","version":2},"cited_work":{"arxiv_id":"2407.00183","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.00183","snapshot_observed_at":"2026-08-06T20:43:30.106830Z","title":"Top-philic Machine Learning","venue":"hep-ph","work_id":"b2a34fc3-bd12-4564-9d96-58b9c9783f33","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.639651Z"},"links":{"cited_paper":"/paper/2407.00183","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:15d1acc35cfdf29987e2755833f8df3ae2a27a204c689800836549d67652c80c","observation_id":"41b2e59f-0d9b-41ee-b8b3-496fed02fd9b","resolution":{"observed_at":"2026-08-06T20:43:30.166636Z","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":{"arxiv_id":"2312.13382","last_updated":"2024-02-02T18:20:03Z","snapshot_observed_at":"2026-08-09T17:40:03.947335Z","submitted_at":"2023-12-20T19:13:26Z","title":"DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13382","snapshot_observed_at":"2026-08-06T20:43:26.719336Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.719336Z"},"links":{"cited_paper":"/paper/2312.13382","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:a56d3a565205605fc323890679ef183f31b13f0cb7e6a0912c0e77c8526249bb","observation_id":"b9ae8b89-1084-4bca-951c-0fd403558898","resolution":{"observed_at":"2026-08-06T20:43:26.719336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15298","last_updated":"2024-12-19T10:38:46Z","snapshot_observed_at":"2026-07-06T20:10:29.099784Z","submitted_at":"2024-12-19T10:38:46Z","title":"A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15298","snapshot_observed_at":"2026-08-06T20:43:26.773582Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.773582Z"},"links":{"cited_paper":"/paper/2412.15298","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:c16cef161ff66cc6664a20cd06f10d6b215672e6623a65979762eaa3e82551d0","observation_id":"9fda36b6-16e5-4f65-a47e-93d2b28a15bd","resolution":{"observed_at":"2026-08-06T20:43:26.773582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14024","last_updated":"2023-01-23T17:00:01Z","snapshot_observed_at":"2026-08-06T22:47:15.172350Z","submitted_at":"2022-12-28T18:52:44Z","title":"Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.14024","snapshot_observed_at":"2026-08-06T20:43:26.891310Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.891310Z"},"links":{"cited_paper":"/paper/2212.14024","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:360093ab1aa8a232283b263c8fb32367dddaeaa624009f133bb4fb878eb1c9a4","observation_id":"98e6a46a-df0f-406b-9770-48f65a56ac2e","resolution":{"observed_at":"2026-08-06T20:43:26.891310Z","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":"2026-08-06T20:43:32.067988Z","title":null,"venue":null,"work_id":"dc6b0b34-051f-435a-a391-54d653fb28e9","year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:26.951884Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:72d223a4e29279e8fe91893805fa91dba9b585807c92dd528e137ec32f0cdc1d","observation_id":"74a78612-4639-4436-9307-225e8bbe64e3","resolution":{"observed_at":"2026-08-06T20:43:32.132088Z","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":"2403.13787","last_updated":"2024-06-08T16:40:12Z","snapshot_observed_at":"2026-08-02T18:11:57.036767Z","submitted_at":"2024-03-20T17:49:54Z","title":"RewardBench: Evaluating Reward Models for Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13787","snapshot_observed_at":"2026-08-06T20:43:27.030856Z","title":"Smith, and Hanna Hajishirzi","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.030856Z"},"links":{"cited_paper":"/paper/2403.13787","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:500e1285b27a233bbc33aef27981ccafc1cfada31b4b635544178617af4a72c5","observation_id":"d3766c4b-fd3b-449d-82f3-0af9f8b682a6","resolution":{"observed_at":"2026-08-06T20:43:27.030856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07871","last_updated":"2018-11-19T18:48:04Z","snapshot_observed_at":"2026-07-06T07:15:51.575438Z","submitted_at":"2018-11-19T18:48:04Z","title":"Scalable agent alignment via reward modeling: a research direction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07871","snapshot_observed_at":"2026-08-06T20:43:27.141756Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.141756Z"},"links":{"cited_paper":"/paper/1811.07871","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:e8f2374c6d1b2b042e38c736c45b07b0d95c4a62a0e995054cee020ce8327fb0","observation_id":"ca30404d-f858-4923-bbef-4c3b8c65c32b","resolution":{"observed_at":"2026-08-06T20:43:27.141756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-05T13:11:04.104454Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-06T20:43:27.242716Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.242716Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:44ffdc545e404fca961442b05f3e89cd1c46f44ad8b2d4585da16bb5182345b0","observation_id":"4ce9cb4d-caeb-4049-8170-27981c03d074","resolution":{"observed_at":"2026-08-06T20:43:27.242716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00676","last_updated":"2024-05-01T17:59:45Z","snapshot_observed_at":"2026-07-06T18:08:17.557905Z","submitted_at":"2024-05-01T17:59:45Z","title":"Spectrally Pruned Gaussian Fields with Neural Compensation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00676","snapshot_observed_at":"2026-08-06T20:43:27.365315Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.365315Z"},"links":{"cited_paper":"/paper/2405.00676","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:fba41713f455853117b3f7c1c35b9044e8f18e660f838ad6dc0ef66fc1d93f01","observation_id":"3acae069-a9e5-4ad9-8660-402d0202892d","resolution":{"observed_at":"2026-08-06T20:43:27.365315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15685","last_updated":"2024-04-16T02:46:58Z","snapshot_observed_at":"2026-07-06T17:07:52.197869Z","submitted_at":"2023-12-25T10:29:28Z","title":"What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15685","snapshot_observed_at":"2026-08-06T20:43:27.467789Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.467789Z"},"links":{"cited_paper":"/paper/2312.15685","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:b9ef8fd4c030b0dda0fbadc994308e49c6d59a5c6d5608310c359441cddfc408","observation_id":"e02f8e1a-08f2-4517-b09a-dca55508671e","resolution":{"observed_at":"2026-08-06T20:43:27.467789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09583","last_updated":"2025-06-04T08:58:56Z","snapshot_observed_at":"2026-08-02T06:48:43.121988Z","submitted_at":"2023-08-18T14:23:21Z","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09583","snapshot_observed_at":"2026-08-06T20:43:27.578947Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.578947Z"},"links":{"cited_paper":"/paper/2308.09583","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:e8a969666ac18088a7fa82a549746ab6f55c64edcb422291125ab2eb8f4d2f11","observation_id":"c82d6083-6cac-458f-819f-695fa29c96b4","resolution":{"observed_at":"2026-08-06T20:43:27.578947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14734","last_updated":"2024-11-01T20:05:19Z","snapshot_observed_at":"2026-08-03T02:02:15.325394Z","submitted_at":"2024-05-23T16:01:46Z","title":"SimPO: Simple Preference Optimization with a Reference-Free Reward","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14734","snapshot_observed_at":"2026-08-06T20:43:27.665291Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.665291Z"},"links":{"cited_paper":"/paper/2405.14734","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:75137337c35381e1d969396ce84b9b4e6e8144c307f316f767a0d1402aa7923a","observation_id":"a5119672-470d-427a-b83d-82a4addb89ff","resolution":{"observed_at":"2026-08-06T20:43:27.665291Z","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":"2026-08-06T20:43:31.908352Z","title":null,"venue":null,"work_id":"53ea2d49-0626-4a87-a971-6ca60ba1d1f1","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.750492Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:8d96707c3317137f395ddb2b8a2650b1875357c7c666bfa22fa34f02c1661fe5","observation_id":"d09995e2-1c1d-4ead-961f-e0b58834e731","resolution":{"observed_at":"2026-08-06T20:43:31.954153Z","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":"2026-08-06T20:43:31.765060Z","title":null,"venue":null,"work_id":"0b7539e4-241f-4e6e-9c37-d7eff9f7a50e","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.816531Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:882e701eeb00873b0420d0a4d316cd5f7ba2bc631b7e76befb3b0ef069fa551a","observation_id":"9839e3ad-6891-4d57-b5ee-0c4de2a60fa7","resolution":{"observed_at":"2026-08-06T20:43:31.821340Z","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":"2026-08-06T20:43:31.639578Z","title":"Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E","venue":null,"work_id":"86915746-6db2-43f9-9709-a67fd49c32bb","year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.870481Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:9663d23d35fdec06f6075c61e7c6953582ea773299cd26901f0fe1d9642d4014","observation_id":"11f834ca-61ef-4c14-aff7-3377728ac151","resolution":{"observed_at":"2026-08-06T20:43:31.704902Z","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":"2026-08-06T20:43:27.937209Z","title":"Plackett","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:27.937209Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:829700ef174bb6917c065b233ce0784b845d934c3bbf36036e5b994d40210704","observation_id":"d864c01a-0f98-44b6-99a3-28f70c0cf0be","resolution":{"observed_at":"2026-08-06T20:43:27.937209Z","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":"2026-08-06T20:43:28.017727Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.017727Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:230bef5dbdd043fcb52006ddc56b971364e39936def0efdf2c62098683d20036","observation_id":"67846956-50c1-45ea-862d-80a9695cf0ef","resolution":{"observed_at":"2026-08-06T20:43:28.017727Z","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":"2026-08-06T20:43:31.443968Z","title":null,"venue":null,"work_id":"85e20704-f4ba-4421-b2bd-aa4099376503","year":2020},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.088213Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:fd3a68f6d5e23c922fe96bd17da570fcd45e762f1d7439d4e55a92ee6547ea27","observation_id":"02c90f97-bf16-44f3-b895-e3ded481a023","resolution":{"observed_at":"2026-08-06T20:43:31.522941Z","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":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T20:43:28.206026Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.206026Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:362299ba4f8357a23157db09a41480c620e71a5013d09bbae69e1d185103ffe4","observation_id":"e264d90a-da9f-4b84-8963-2048a6a9c5df","resolution":{"observed_at":"2026-08-06T20:43:28.206026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09778","last_updated":"2025-04-23T11:03:29Z","snapshot_observed_at":"2026-08-09T18:01:41.262986Z","submitted_at":"2024-06-14T07:27:19Z","title":"Small Solutions of generic ternary quadratic congruences","version":5},"cited_work":{"arxiv_id":"2406.09778","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.09778","snapshot_observed_at":"2026-08-06T20:43:29.795010Z","title":"Small Solutions of generic ternary quadratic congruences","venue":"math.NT","work_id":"d980808e-7983-4a55-9bf4-103bed4faac6","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.334842Z"},"links":{"cited_paper":"/paper/2406.09778","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:f28b1aa6072916133c93e4637fe222c0e0e990ea4fb2de56d7e303171a95640a","observation_id":"2876bc76-70a6-4968-821d-33b64aea6876","resolution":{"observed_at":"2026-08-06T20:43:29.866506Z","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":"2026-08-06T20:43:31.291183Z","title":null,"venue":null,"work_id":"90769bb9-5097-4fcc-9721-8d953c1394fb","year":2016},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.413295Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:e12745e7362ab621e93fb6ee2cef52afe5e45ef908d2bcc79549a92cccb5749d","observation_id":"44fbde2e-45aa-4645-8be3-64f31345cb7f","resolution":{"observed_at":"2026-08-06T20:43:31.346846Z","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":"2026-08-06T20:43:28.475185Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.475185Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:3c859a1cee79727fd23500de776d88d44396ee49981b117a902752a85184af80","observation_id":"268ee180-92b5-45fb-93da-c996e3e89ae8","resolution":{"observed_at":"2026-08-06T20:43:28.475185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11191","last_updated":"2024-06-18T08:18:33Z","snapshot_observed_at":"2026-08-03T11:31:07.805515Z","submitted_at":"2024-06-17T03:52:51Z","title":"A Survey on Human Preference Learning for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11191","snapshot_observed_at":"2026-08-06T20:43:28.561126Z","title":"Yao, Shi-Xiong Zhang, and Sambit Sahu","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.561126Z"},"links":{"cited_paper":"/paper/2406.11191","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:8a395bc82a23fb6196ba0c84edd2a44e24d857819b03f18c3e35922dac96cbe5","observation_id":"145d718e-7b02-4278-98be-c7ed15b2070a","resolution":{"observed_at":"2026-08-06T20:43:28.561126Z","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":"2026-08-06T20:43:31.163335Z","title":null,"venue":null,"work_id":"4dfd85de-3320-47ac-86f3-15fa69d2351b","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.639849Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:f19fa2353a2d8454013de9677485a7c2d992188d52094af9ab66cf66696fbbe4","observation_id":"d16e4121-2391-4386-8d22-9d14fe6d3555","resolution":{"observed_at":"2026-08-06T20:43:31.218132Z","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":"2026-08-06T20:43:31.013292Z","title":"Rush, and Thomas Wolf","venue":null,"work_id":"29d42215-c2fa-4475-980d-cc942effd3a1","year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.688639Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:82e3af1208a90da1a22cdc92e0be474683d96a069a8a5a576736593fa64bfcdc","observation_id":"505e5c89-3fd9-492e-bbe6-ef669eb24bae","resolution":{"observed_at":"2026-08-06T20:43:31.075825Z","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":"2026-08-06T20:43:30.833983Z","title":null,"venue":null,"work_id":"02888733-a0f8-491f-9f73-68d062d4da36","year":2022},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.756142Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:70ef223f3ca2b9bcb493aeded6dd9d9089729ba7e4d5a6b1bfbcbf6d569c0160","observation_id":"c61b6799-576f-4998-82e0-c0fa5ae3da00","resolution":{"observed_at":"2026-08-06T20:43:30.918375Z","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":"2402.04333","last_updated":"2024-06-13T03:42:02Z","snapshot_observed_at":"2026-08-07T17:47:02.067367Z","submitted_at":"2024-02-06T19:18:04Z","title":"LESS: Selecting Influential Data for Targeted Instruction Tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04333","snapshot_observed_at":"2026-08-06T20:43:28.792871Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.792871Z"},"links":{"cited_paper":"/paper/2402.04333","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:c4b27671e73b641a7f61ed2aeeef68eebe69312dce7fb583b2894fcba21fb203","observation_id":"fd07e8cb-16c9-47e2-aeb9-a5b319f6dc64","resolution":{"observed_at":"2026-08-06T20:43:28.792871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10719","last_updated":"2024-10-10T08:30:17Z","snapshot_observed_at":"2026-07-06T18:01:05.698046Z","submitted_at":"2024-04-16T16:51:53Z","title":"Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10719","snapshot_observed_at":"2026-08-06T20:43:28.888089Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.888089Z"},"links":{"cited_paper":"/paper/2404.10719","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:8b04e0d38c43253f9cbab630c61b93b550920d5eb3a9706c139c74584cfbf6fe","observation_id":"e4a752a6-7d73-4165-b915-105a3afcc6c9","resolution":{"observed_at":"2026-08-06T20:43:28.888089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10601","last_updated":"2023-12-03T22:50:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T23:16:17Z","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10601","snapshot_observed_at":"2026-08-06T20:43:28.969383Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:28.969383Z"},"links":{"cited_paper":"/paper/2305.10601","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:f8d010a5a8ead04d9da34fb2f7867cd04629cea0718254d31b37efded7f10d72","observation_id":"881ee8d8-e4dc-400c-b1cd-98c7b7f2914f","resolution":{"observed_at":"2026-08-06T20:43:28.969383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-02T15:00:50.388422Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12284","snapshot_observed_at":"2026-08-06T20:43:29.058781Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.058781Z"},"links":{"cited_paper":"/paper/2309.12284","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:3203eac81ecfb25cdd1dc51d4410336abbc995b84032036e43672ad8b9dff010","observation_id":"7455639d-3a6d-4ee7-8b6f-1bbbd092c814","resolution":{"observed_at":"2026-08-06T20:43:29.058781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05685","last_updated":"2023-12-24T02:01:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-09T05:55:52Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05685","snapshot_observed_at":"2026-08-06T20:43:29.154180Z","title":"Xing, Hao Zhang, Joseph E","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.154180Z"},"links":{"cited_paper":"/paper/2306.05685","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:0b2f1b93ccd89234c12f932728a95adc19059a414b682ba9976317f17693a401","observation_id":"5f87e13e-c8d8-4dfd-a5da-8bf382cdd869","resolution":{"observed_at":"2026-08-06T20:43:29.154180Z","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":"2026-08-06T20:43:30.713988Z","title":null,"venue":null,"work_id":"f0d34050-0237-45e2-9cf5-cfc5f1a37b0c","year":2023},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.241836Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:c1bc5dcc46ef44dadc39663d871c81503b42915be58cf1e7db255543782f9bc5","observation_id":"1acab3fa-9085-402c-b881-dd1951581835","resolution":{"observed_at":"2026-08-06T20:43:30.757053Z","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":"2403.11315","last_updated":"2024-06-26T13:20:40Z","snapshot_observed_at":"2026-07-06T17:45:59.984749Z","submitted_at":"2024-03-17T19:35:21Z","title":"On widely degenerate \\textit{p}-Laplace equations with symmetric data","version":2},"cited_work":{"arxiv_id":"2403.11315","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.11315","snapshot_observed_at":"2026-08-06T20:43:29.572928Z","title":"On widely degenerate \\textit{p}-Laplace equations with symmetric data","venue":"math.AP","work_id":"6bcb50de-4230-4d05-b208-950f388a59de","year":2024},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.303604Z"},"links":{"cited_paper":"/paper/2403.11315","citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:318d2555c989a4e2ea7c88cdb81a751572f7f450988331ff12e856922c5cc2bd","observation_id":"22e057b2-25da-4681-a6f7-b422f0d6d6c3","resolution":{"observed_at":"2026-08-06T20:43:29.615209Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:43:29.350602Z","title":"URL: \" 'urlintro :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.350602Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:8b291c05805def0f54f40fa831888447ff8ae63fc574a1a0e277cd4e89133e7b","observation_id":"15160f95-381a-409c-b1ac-e71fd46fb900","resolution":{"observed_at":"2026-08-06T20:43:29.350602Z","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":"2026-08-06T20:43:29.427195Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T20:43:29.427195Z"},"links":{"citing_paper":"/paper/2507.02173"},"observation_digest":"sha256:ba8547a438ba25dc3712b1a96aceb1789e0f75fdd8e32d2c329b1210fcccc41b","observation_id":"50750e9d-5cb9-4e02-a835-690459f00bae","resolution":{"observed_at":"2026-08-06T20:43:29.427195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.02173","last_updated":"2025-07-02T22:12:03Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T13:56:29.658636Z","submitted_at":"2025-07-02T22:12:03Z","title":"Data Diversification Methods In Alignment Enhance Math Performance In LLMs"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":45,"verified_exact":5,"verified_fuzzy":2},"total_outbound_references":52},"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 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.02173."}