{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2XQSFE6LRUZDMES5UQEZ57V7RV","short_pith_number":"pith:2XQSFE6L","schema_version":"1.0","canonical_sha256":"d5e12293cb8d3236125da4099efebf8d42f3aaa0c689cca7e0e44d754092df00","source":{"kind":"arxiv","id":"2606.03834","version":1},"attestation_state":"computed","paper":{"title":"Let the Dynamics Flow: Stable Flow Matching Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrea Testa, Francisco Leiva, Javier Ruiz del Solar, Leonel Rozo, No\\'emie Jaquier, Rodrigo P\\'erez-Dattari","submitted_at":"2026-06-02T16:17:26Z","abstract_excerpt":"Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, incorporating formal stability guarantees into these generative models, a prerequisite to ensure safe and generalizable robot behaviors, remains a significant challenge. While modeling robot motions as dynamical systems allows for such stability-based inductive biases, existing frameworks struggle to capture the rich action distributions inherent in complex robotic tasks. This paper introduces Stable Flow Matching Dynamical Systems (SFMDS), a"},"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":"2606.03834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-02T16:17:26Z","cross_cats_sorted":[],"title_canon_sha256":"f9e7e257f259af3062b1fb29220eee110678be2bec82c47d054df2e376324b48","abstract_canon_sha256":"7dd1ce24e7a6c40fa6f4a2de33881925b5b0adffe51043a20f1cd82377b65220"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T02:06:03.686756Z","signature_b64":"SaLYader4ASQbhab/2UNED9N21Fwlmdv8xvXc0XyUPwo6MSpyaqvzwXc5G14QwIFjHVndDPPuHTU+xz0mgQfDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5e12293cb8d3236125da4099efebf8d42f3aaa0c689cca7e0e44d754092df00","last_reissued_at":"2026-06-03T02:06:03.686423Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T02:06:03.686423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Let the Dynamics Flow: Stable Flow Matching Dynamical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Andrea Testa, Francisco Leiva, Javier Ruiz del Solar, Leonel Rozo, No\\'emie Jaquier, Rodrigo P\\'erez-Dattari","submitted_at":"2026-06-02T16:17:26Z","abstract_excerpt":"Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, incorporating formal stability guarantees into these generative models, a prerequisite to ensure safe and generalizable robot behaviors, remains a significant challenge. While modeling robot motions as dynamical systems allows for such stability-based inductive biases, existing frameworks struggle to capture the rich action distributions inherent in complex robotic tasks. This paper introduces Stable Flow Matching Dynamical Systems (SFMDS), a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03834","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/2606.03834/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":"2606.03834","created_at":"2026-06-03T02:06:03.686478+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03834v1","created_at":"2026-06-03T02:06:03.686478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03834","created_at":"2026-06-03T02:06:03.686478+00:00"},{"alias_kind":"pith_short_12","alias_value":"2XQSFE6LRUZD","created_at":"2026-06-03T02:06:03.686478+00:00"},{"alias_kind":"pith_short_16","alias_value":"2XQSFE6LRUZDMES5","created_at":"2026-06-03T02:06:03.686478+00:00"},{"alias_kind":"pith_short_8","alias_value":"2XQSFE6L","created_at":"2026-06-03T02:06:03.686478+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/2XQSFE6LRUZDMES5UQEZ57V7RV","json":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV.json","graph_json":"https://pith.science/api/pith-number/2XQSFE6LRUZDMES5UQEZ57V7RV/graph.json","events_json":"https://pith.science/api/pith-number/2XQSFE6LRUZDMES5UQEZ57V7RV/events.json","paper":"https://pith.science/paper/2XQSFE6L"},"agent_actions":{"view_html":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV","download_json":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV.json","view_paper":"https://pith.science/paper/2XQSFE6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03834&json=true","fetch_graph":"https://pith.science/api/pith-number/2XQSFE6LRUZDMES5UQEZ57V7RV/graph.json","fetch_events":"https://pith.science/api/pith-number/2XQSFE6LRUZDMES5UQEZ57V7RV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV/action/storage_attestation","attest_author":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV/action/author_attestation","sign_citation":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV/action/citation_signature","submit_replication":"https://pith.science/pith/2XQSFE6LRUZDMES5UQEZ57V7RV/action/replication_record"}},"created_at":"2026-06-03T02:06:03.686478+00:00","updated_at":"2026-06-03T02:06:03.686478+00:00"}