{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RTJMVY4SJFWG7RIBSWXN2YAMXJ","short_pith_number":"pith:RTJMVY4S","schema_version":"1.0","canonical_sha256":"8cd2cae392496c6fc50195aedd600cba4e99a8756654268b521f83560d190116","source":{"kind":"arxiv","id":"2606.07217","version":1},"attestation_state":"computed","paper":{"title":"Robotic Policy Adaptation via Weight-Space Meta-Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Alessio Sampieri, Andrea Roberti, Christian Bianchi, Fabio Galasso, Luca Franco, Luca Rigazio, Siamak Yousefi","submitted_at":"2026-06-05T12:29:28Z","abstract_excerpt":"Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations and action labels. However, adapting these models to new tasks still typically requires task-specific demonstrations, action annotations, and additional fine-tuning, making deployment costly and difficult to scale.\n  We propose WIZARD, a weight-space meta-learning framework that sidesteps task-specific fine-tuning by generating task-specific LoRA parameters for a frozen VLA policy. Given only a language instruction and"},"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":"2606.07217","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-05T12:29:28Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"bbc5068f52b8beb7e4292954ebc06128da63d9c205d16835e2acbffcc8898cb9","abstract_canon_sha256":"dd8861c7aed6937af0fde438fe970d008e2bb11360e1f186180ad10aab0a9244"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:53.856694Z","signature_b64":"uiKh6y3ynD/w79bETpkaRyPjvQLCXw1U36TR7/BE5z0FQ71BKFx8CbwnJsK56qrv0TYe+UQI/ZdMuiKIHitCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cd2cae392496c6fc50195aedd600cba4e99a8756654268b521f83560d190116","last_reissued_at":"2026-06-08T01:04:53.855961Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:53.855961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robotic Policy Adaptation via Weight-Space Meta-Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Alessio Sampieri, Andrea Roberti, Christian Bianchi, Fabio Galasso, Luca Franco, Luca Rigazio, Siamak Yousefi","submitted_at":"2026-06-05T12:29:28Z","abstract_excerpt":"Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations and action labels. However, adapting these models to new tasks still typically requires task-specific demonstrations, action annotations, and additional fine-tuning, making deployment costly and difficult to scale.\n  We propose WIZARD, a weight-space meta-learning framework that sidesteps task-specific fine-tuning by generating task-specific LoRA parameters for a frozen VLA policy. Given only a language instruction and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07217","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/2606.07217/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":"2606.07217","created_at":"2026-06-08T01:04:53.856081+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07217v1","created_at":"2026-06-08T01:04:53.856081+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07217","created_at":"2026-06-08T01:04:53.856081+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTJMVY4SJFWG","created_at":"2026-06-08T01:04:53.856081+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTJMVY4SJFWG7RIB","created_at":"2026-06-08T01:04:53.856081+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTJMVY4S","created_at":"2026-06-08T01:04:53.856081+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/RTJMVY4SJFWG7RIBSWXN2YAMXJ","json":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ.json","graph_json":"https://pith.science/api/pith-number/RTJMVY4SJFWG7RIBSWXN2YAMXJ/graph.json","events_json":"https://pith.science/api/pith-number/RTJMVY4SJFWG7RIBSWXN2YAMXJ/events.json","paper":"https://pith.science/paper/RTJMVY4S"},"agent_actions":{"view_html":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ","download_json":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ.json","view_paper":"https://pith.science/paper/RTJMVY4S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07217&json=true","fetch_graph":"https://pith.science/api/pith-number/RTJMVY4SJFWG7RIBSWXN2YAMXJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RTJMVY4SJFWG7RIBSWXN2YAMXJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ/action/storage_attestation","attest_author":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ/action/author_attestation","sign_citation":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ/action/citation_signature","submit_replication":"https://pith.science/pith/RTJMVY4SJFWG7RIBSWXN2YAMXJ/action/replication_record"}},"created_at":"2026-06-08T01:04:53.856081+00:00","updated_at":"2026-06-08T01:04:53.856081+00:00"}