{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HTTIY7EBBJGXR7VDYWTXMDJWAM","short_pith_number":"pith:HTTIY7EB","schema_version":"1.0","canonical_sha256":"3ce68c7c810a4d78fea3c5a7760d36031a83d4ecfaf2f43a68b86f88bebcbdde","source":{"kind":"arxiv","id":"2506.02539","version":3},"attestation_state":"computed","paper":{"title":"VerificAgent: Domain-Specific Memory Verification for Scalable Oversight of Aligned Computer-Use Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Firoz Shaik, Raja Hasnain Anwar, Shubhang Desai, Thong Q. Nguyen, Vishal Chowdhary, Vishwas Suryanarayanan","submitted_at":"2025-06-03T07:25:49Z","abstract_excerpt":"Continual memory augmentation lets computer-using agents (CUAs) learn from prior interactions, but unvetted memories can encode domain-inappropriate or unsafe heuristics--spurious rules that drift from user intent and safety constraints. We introduce VerificAgent, a scalable oversight framework that treats persistent memory as an explicit alignment surface. VerificAgent combines (1) an expert-curated seed of domain knowledge, (2) iterative, trajectory-based memory growth during training, and (3) a post-hoc human fact-checking pass to sanitize accumulated memories before deployment. Evaluated o"},"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":"2506.02539","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-03T07:25:49Z","cross_cats_sorted":[],"title_canon_sha256":"b60ce4a014ebd0139bf892c0b5b14dddd2b70e3b024241465215186ab6dfcd1d","abstract_canon_sha256":"1d8526f818ac2790914e3096027b82f35a2b53275cd8b7b40ba663c9fc68b90b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:37.426898Z","signature_b64":"1dCljfiIbL2iArtxDXhiXC+ZsdcJ5382j/KL87yPvZXMxvxHTQHYb476sARa0Ahb+BMvG/ZFHs4Gc/hRtP4wAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ce68c7c810a4d78fea3c5a7760d36031a83d4ecfaf2f43a68b86f88bebcbdde","last_reissued_at":"2026-07-05T11:50:37.426410Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:37.426410Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VerificAgent: Domain-Specific Memory Verification for Scalable Oversight of Aligned Computer-Use Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Firoz Shaik, Raja Hasnain Anwar, Shubhang Desai, Thong Q. Nguyen, Vishal Chowdhary, Vishwas Suryanarayanan","submitted_at":"2025-06-03T07:25:49Z","abstract_excerpt":"Continual memory augmentation lets computer-using agents (CUAs) learn from prior interactions, but unvetted memories can encode domain-inappropriate or unsafe heuristics--spurious rules that drift from user intent and safety constraints. We introduce VerificAgent, a scalable oversight framework that treats persistent memory as an explicit alignment surface. VerificAgent combines (1) an expert-curated seed of domain knowledge, (2) iterative, trajectory-based memory growth during training, and (3) a post-hoc human fact-checking pass to sanitize accumulated memories before deployment. Evaluated o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02539","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/2506.02539/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":"2506.02539","created_at":"2026-07-05T11:50:37.426471+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02539v3","created_at":"2026-07-05T11:50:37.426471+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02539","created_at":"2026-07-05T11:50:37.426471+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTTIY7EBBJGX","created_at":"2026-07-05T11:50:37.426471+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTTIY7EBBJGXR7VD","created_at":"2026-07-05T11:50:37.426471+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTTIY7EB","created_at":"2026-07-05T11:50:37.426471+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/HTTIY7EBBJGXR7VDYWTXMDJWAM","json":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM.json","graph_json":"https://pith.science/api/pith-number/HTTIY7EBBJGXR7VDYWTXMDJWAM/graph.json","events_json":"https://pith.science/api/pith-number/HTTIY7EBBJGXR7VDYWTXMDJWAM/events.json","paper":"https://pith.science/paper/HTTIY7EB"},"agent_actions":{"view_html":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM","download_json":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM.json","view_paper":"https://pith.science/paper/HTTIY7EB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02539&json=true","fetch_graph":"https://pith.science/api/pith-number/HTTIY7EBBJGXR7VDYWTXMDJWAM/graph.json","fetch_events":"https://pith.science/api/pith-number/HTTIY7EBBJGXR7VDYWTXMDJWAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM/action/storage_attestation","attest_author":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM/action/author_attestation","sign_citation":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM/action/citation_signature","submit_replication":"https://pith.science/pith/HTTIY7EBBJGXR7VDYWTXMDJWAM/action/replication_record"}},"created_at":"2026-07-05T11:50:37.426471+00:00","updated_at":"2026-07-05T11:50:37.426471+00:00"}