{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:W5O7CXLVGSWKSI6D3FPIA5ITO2","short_pith_number":"pith:W5O7CXLV","schema_version":"1.0","canonical_sha256":"b75df15d7534aca923c3d95e80751376ab41180abfd7bd4397d3972c60c0c9ca","source":{"kind":"arxiv","id":"2607.27690","version":1},"attestation_state":"computed","paper":{"title":"LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Jingya Wang, Liuzhenghao Lv, Yonghong Tian, Yuyang Gao, Yuyang Liu","submitted_at":"2026-07-30T05:19:33Z","abstract_excerpt":"We introduce LabEvolver, a training-free framework that equips safe and grounded wet-lab agents with episodic memory from execution experience. LabEvolver couples a state-grounded inner trial loop for adaptive perception, online planning, and safety validation with an outer evolution loop that distills completed trajectories into reusable skill, strategy, and safety experience. On robotic solution-preparation tasks, LabEvolver demonstrates real-world feasibility, reducing pH-regulation completion time and safety-gate intercepts by 48.2% and 60.0%, respectively. On ALFWorld, it further improves"},"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":"2607.27690","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-07-30T05:19:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"feb6bf4a0bdc9b09fcfc9cbe84197a93f5fb845fd72a47c9b6c476b668af17b9","abstract_canon_sha256":"04886754edd0338d77bddcc378a828a845d8d423327dc1673a5ddeec6d093f8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b75df15d7534aca923c3d95e80751376ab41180abfd7bd4397d3972c60c0c9ca","last_reissued_at":"2026-07-31T01:29:44.536453Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:29:44.536453Z"},"graph_snapshot":{"paper":{"title":"LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Jingya Wang, Liuzhenghao Lv, Yonghong Tian, Yuyang Gao, Yuyang Liu","submitted_at":"2026-07-30T05:19:33Z","abstract_excerpt":"We introduce LabEvolver, a training-free framework that equips safe and grounded wet-lab agents with episodic memory from execution experience. LabEvolver couples a state-grounded inner trial loop for adaptive perception, online planning, and safety validation with an outer evolution loop that distills completed trajectories into reusable skill, strategy, and safety experience. On robotic solution-preparation tasks, LabEvolver demonstrates real-world feasibility, reducing pH-regulation completion time and safety-gate intercepts by 48.2% and 60.0%, respectively. On ALFWorld, it further improves"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27690","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/2607.27690/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":"2607.27690","created_at":"2026-07-31T01:29:44.539691+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27690v1","created_at":"2026-07-31T01:29:44.539691+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27690","created_at":"2026-07-31T01:29:44.539691+00:00"},{"alias_kind":"pith_short_12","alias_value":"W5O7CXLVGSWK","created_at":"2026-07-31T01:29:44.539691+00:00"},{"alias_kind":"pith_short_16","alias_value":"W5O7CXLVGSWKSI6D","created_at":"2026-07-31T01:29:44.539691+00:00"},{"alias_kind":"pith_short_8","alias_value":"W5O7CXLV","created_at":"2026-07-31T01:29:44.539691+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/W5O7CXLVGSWKSI6D3FPIA5ITO2","json":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2.json","graph_json":"https://pith.science/api/pith-number/W5O7CXLVGSWKSI6D3FPIA5ITO2/graph.json","events_json":"https://pith.science/api/pith-number/W5O7CXLVGSWKSI6D3FPIA5ITO2/events.json","paper":"https://pith.science/paper/W5O7CXLV"},"agent_actions":{"view_html":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2","download_json":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2.json","view_paper":"https://pith.science/paper/W5O7CXLV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27690&json=true","fetch_graph":"https://pith.science/api/pith-number/W5O7CXLVGSWKSI6D3FPIA5ITO2/graph.json","fetch_events":"https://pith.science/api/pith-number/W5O7CXLVGSWKSI6D3FPIA5ITO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2/action/storage_attestation","attest_author":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2/action/author_attestation","sign_citation":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2/action/citation_signature","submit_replication":"https://pith.science/pith/W5O7CXLVGSWKSI6D3FPIA5ITO2/action/replication_record"}},"created_at":"2026-07-31T01:29:44.539691+00:00","updated_at":"2026-07-31T01:29:44.539691+00:00"}