{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PFHYNEIXE4TKDFFGM3YS77MPDW","short_pith_number":"pith:PFHYNEIX","schema_version":"1.0","canonical_sha256":"794f8691172726a194a666f12ffd8f1d87a095b430f1d82907aa84aa472b91c2","source":{"kind":"arxiv","id":"2512.19347","version":3},"attestation_state":"computed","paper":{"title":"OMP: One-step Meanflow Policy with Directional Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Han Fang, Paul Weng, Xiao Li, Yize Huang, Yuheng Zhao, Yutong Ban","submitted_at":"2025-12-22T12:45:35Z","abstract_excerpt":"Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models suffer from high inference latency, while flow-based methods often require complex architectural constraints. Although in image generation domain, the MeanFlow paradigm offers a path to single-step inference, its direct application to robotics is impeded by critical theoretical pathologies, specifically spectral bias and gradient starvation in low-velocity regimes. To overcome these limitations, we propose the One-step MeanFlow Policy (OMP), a novel"},"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":"2512.19347","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-12-22T12:45:35Z","cross_cats_sorted":[],"title_canon_sha256":"67ebd1fe03d5ef6e3dd111b5e518923c8dea3e2774b61b4dbb80c4d0c1bd9960","abstract_canon_sha256":"a7bd16867038c5b41f6d2a64d679d26e4825dfbd0c22f27860045d8285970bfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:47.049572Z","signature_b64":"IaQqCIIOrm9zlJNmAreGDDKkaVDySqy8pRh/+mo/Mt4L8hFCrpNiE2Eai02ci3QEkwNGhIK0tRkuCTecLPY2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"794f8691172726a194a666f12ffd8f1d87a095b430f1d82907aa84aa472b91c2","last_reissued_at":"2026-06-03T01:05:47.049206Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:47.049206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OMP: One-step Meanflow Policy with Directional Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Han Fang, Paul Weng, Xiao Li, Yize Huang, Yuheng Zhao, Yutong Ban","submitted_at":"2025-12-22T12:45:35Z","abstract_excerpt":"Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models suffer from high inference latency, while flow-based methods often require complex architectural constraints. Although in image generation domain, the MeanFlow paradigm offers a path to single-step inference, its direct application to robotics is impeded by critical theoretical pathologies, specifically spectral bias and gradient starvation in low-velocity regimes. To overcome these limitations, we propose the One-step MeanFlow Policy (OMP), a novel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.19347","kind":"arxiv","version":3},"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/2512.19347/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":"2512.19347","created_at":"2026-06-03T01:05:47.049263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.19347v3","created_at":"2026-06-03T01:05:47.049263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.19347","created_at":"2026-06-03T01:05:47.049263+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFHYNEIXE4TK","created_at":"2026-06-03T01:05:47.049263+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFHYNEIXE4TKDFFG","created_at":"2026-06-03T01:05:47.049263+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFHYNEIX","created_at":"2026-06-03T01:05:47.049263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW","json":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW.json","graph_json":"https://pith.science/api/pith-number/PFHYNEIXE4TKDFFGM3YS77MPDW/graph.json","events_json":"https://pith.science/api/pith-number/PFHYNEIXE4TKDFFGM3YS77MPDW/events.json","paper":"https://pith.science/paper/PFHYNEIX"},"agent_actions":{"view_html":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW","download_json":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW.json","view_paper":"https://pith.science/paper/PFHYNEIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.19347&json=true","fetch_graph":"https://pith.science/api/pith-number/PFHYNEIXE4TKDFFGM3YS77MPDW/graph.json","fetch_events":"https://pith.science/api/pith-number/PFHYNEIXE4TKDFFGM3YS77MPDW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW/action/storage_attestation","attest_author":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW/action/author_attestation","sign_citation":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW/action/citation_signature","submit_replication":"https://pith.science/pith/PFHYNEIXE4TKDFFGM3YS77MPDW/action/replication_record"}},"created_at":"2026-06-03T01:05:47.049263+00:00","updated_at":"2026-06-03T01:05:47.049263+00:00"}