{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VIABQEZZWCKYFXKM77ABTYMZT2","short_pith_number":"pith:VIABQEZZ","schema_version":"1.0","canonical_sha256":"aa00181339b09582dd4cffc019e1999e8ca8c146d25317ea9dc970d98663ee3b","source":{"kind":"arxiv","id":"2608.05970","version":1},"attestation_state":"computed","paper":{"title":"SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Angyuan Ma, Changyuan Wang, Chubin Zhang, Jiwen Lu, Ke Chao, Runhao Li, Xiuwei Xu, Yansong Tang, Yinan Liang, Zhenyu Wu, Ziwei Wang","submitted_at":"2026-08-06T12:46:13Z","abstract_excerpt":"Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. However, their potential remains fundamentally constrained by the scarcity of large-scale embodied trajectory datasets, leading to insufficient compositional generalization in out-of-distribution (OOD) scenarios with limited capability to capture reusable skill structures. To address this limitation, we propose Skill-Based Memory (SkillMemo) framework that implicitly decomposes long-horizon demonstrations into latent at"},"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":"2608.05970","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-08-06T12:46:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8675ea8bf12e74aee29073d604ffee53c852cf4127a0c0fbb6e872f516d09716","abstract_canon_sha256":"694f703b97534c388d680aa10522c28200ba2d3ab642f52d8924ebdcd3db2219"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:54:49.833409Z","signature_b64":"yaqbd1ZBcvrhCAHQfDllGfYPaSLeKh1/lboqx3IJd3wWq+zBRwOhgX147bwhGtUzwZ0KU4SmgJmENHLOL6aqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa00181339b09582dd4cffc019e1999e8ca8c146d25317ea9dc970d98663ee3b","last_reissued_at":"2026-08-07T00:54:49.831959Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:54:49.831959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Angyuan Ma, Changyuan Wang, Chubin Zhang, Jiwen Lu, Ke Chao, Runhao Li, Xiuwei Xu, Yansong Tang, Yinan Liang, Zhenyu Wu, Ziwei Wang","submitted_at":"2026-08-06T12:46:13Z","abstract_excerpt":"Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. However, their potential remains fundamentally constrained by the scarcity of large-scale embodied trajectory datasets, leading to insufficient compositional generalization in out-of-distribution (OOD) scenarios with limited capability to capture reusable skill structures. To address this limitation, we propose Skill-Based Memory (SkillMemo) framework that implicitly decomposes long-horizon demonstrations into latent at"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05970","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/2608.05970/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":"2608.05970","created_at":"2026-08-07T00:54:49.833507+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.05970v1","created_at":"2026-08-07T00:54:49.833507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05970","created_at":"2026-08-07T00:54:49.833507+00:00"},{"alias_kind":"pith_short_12","alias_value":"VIABQEZZWCKY","created_at":"2026-08-07T00:54:49.833507+00:00"},{"alias_kind":"pith_short_16","alias_value":"VIABQEZZWCKYFXKM","created_at":"2026-08-07T00:54:49.833507+00:00"},{"alias_kind":"pith_short_8","alias_value":"VIABQEZZ","created_at":"2026-08-07T00:54:49.833507+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/VIABQEZZWCKYFXKM77ABTYMZT2","json":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2.json","graph_json":"https://pith.science/api/pith-number/VIABQEZZWCKYFXKM77ABTYMZT2/graph.json","events_json":"https://pith.science/api/pith-number/VIABQEZZWCKYFXKM77ABTYMZT2/events.json","paper":"https://pith.science/paper/VIABQEZZ"},"agent_actions":{"view_html":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2","download_json":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2.json","view_paper":"https://pith.science/paper/VIABQEZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.05970&json=true","fetch_graph":"https://pith.science/api/pith-number/VIABQEZZWCKYFXKM77ABTYMZT2/graph.json","fetch_events":"https://pith.science/api/pith-number/VIABQEZZWCKYFXKM77ABTYMZT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2/action/storage_attestation","attest_author":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2/action/author_attestation","sign_citation":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2/action/citation_signature","submit_replication":"https://pith.science/pith/VIABQEZZWCKYFXKM77ABTYMZT2/action/replication_record"}},"created_at":"2026-08-07T00:54:49.833507+00:00","updated_at":"2026-08-07T00:54:49.833507+00:00"}