{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QTSJT6B423CDVCZ67Y3GDKUUDP","short_pith_number":"pith:QTSJT6B4","schema_version":"1.0","canonical_sha256":"84e499f83cd6c43a8b3efe3661aa941bf581365de619a0367b2e532800d678c6","source":{"kind":"arxiv","id":"2507.14683","version":1},"attestation_state":"computed","paper":{"title":"MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Wang, Chong Zhang, Dianwen Ng, Feng Ji, Hai Ye, Han Zhao, Lei Lei, Lidong Bing, Ruilin Li, Shihao Xu, Weiling Chen, Xiang Lin, Xingxuan Li, Yao Xiao, Yue Deng, Yueyi Zhang, Zhanfeng Mo, Zonglin Yang","submitted_at":"2025-07-19T16:21:23Z","abstract_excerpt":"Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by om"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.14683","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-19T16:21:23Z","cross_cats_sorted":[],"title_canon_sha256":"c6c0211e23da4f462f1816762be7c9f2a8b4b5d1498436c25a874521d1daf356","abstract_canon_sha256":"e1305f45aae8a9393d656e2f588135909cfd87ad3ec5ba236d47e01ca0aeda7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:11.763800Z","signature_b64":"547/i2/CAlT9kmUMDqAK9y27Eq8PvWP2Me67UpM1jMH0EUs5Twh9Mz+xHAaVdGs9w8F5lryD/B3be/j7ElBgDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84e499f83cd6c43a8b3efe3661aa941bf581365de619a0367b2e532800d678c6","last_reissued_at":"2026-07-05T11:40:11.763227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:11.763227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Wang, Chong Zhang, Dianwen Ng, Feng Ji, Hai Ye, Han Zhao, Lei Lei, Lidong Bing, Ruilin Li, Shihao Xu, Weiling Chen, Xiang Lin, Xingxuan Li, Yao Xiao, Yue Deng, Yueyi Zhang, Zhanfeng Mo, Zonglin Yang","submitted_at":"2025-07-19T16:21:23Z","abstract_excerpt":"Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by om"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14683","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.14683/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.14683","created_at":"2026-07-05T11:40:11.763293+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.14683v1","created_at":"2026-07-05T11:40:11.763293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14683","created_at":"2026-07-05T11:40:11.763293+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTSJT6B423CD","created_at":"2026-07-05T11:40:11.763293+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTSJT6B423CDVCZ6","created_at":"2026-07-05T11:40:11.763293+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTSJT6B4","created_at":"2026-07-05T11:40:11.763293+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01249","citing_title":"Trust Region On-Policy Distillation","ref_index":288,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25571","citing_title":"AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2509.08827","citing_title":"A Survey of Reinforcement Learning for Large Reasoning Models","ref_index":287,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10480","citing_title":"Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP","json":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP.json","graph_json":"https://pith.science/api/pith-number/QTSJT6B423CDVCZ67Y3GDKUUDP/graph.json","events_json":"https://pith.science/api/pith-number/QTSJT6B423CDVCZ67Y3GDKUUDP/events.json","paper":"https://pith.science/paper/QTSJT6B4"},"agent_actions":{"view_html":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP","download_json":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP.json","view_paper":"https://pith.science/paper/QTSJT6B4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.14683&json=true","fetch_graph":"https://pith.science/api/pith-number/QTSJT6B423CDVCZ67Y3GDKUUDP/graph.json","fetch_events":"https://pith.science/api/pith-number/QTSJT6B423CDVCZ67Y3GDKUUDP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP/action/storage_attestation","attest_author":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP/action/author_attestation","sign_citation":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP/action/citation_signature","submit_replication":"https://pith.science/pith/QTSJT6B423CDVCZ67Y3GDKUUDP/action/replication_record"}},"created_at":"2026-07-05T11:40:11.763293+00:00","updated_at":"2026-07-05T11:40:11.763293+00:00"}