{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5THGNJRHTR7QJ3KQS2LVFEZCWO","short_pith_number":"pith:5THGNJRH","schema_version":"1.0","canonical_sha256":"ecce66a6279c7f04ed509697529322b38ca5d0f8ee83593fce119cb9eed02ace","source":{"kind":"arxiv","id":"2607.12823","version":1},"attestation_state":"computed","paper":{"title":"Human-AI Agent Interaction as a Neuroplastic Training Environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Amin Hass, Anita H. Clayton, Asanga Gunaratna, Atmaram Yarlagadd, Chalani Rajapakse, Christopher K. Rhea, Eranga Bandara, Gihan Siriwardanagea, Isurunima Kularathna, Kasun De Zoysa, Ng Wee Keong, Nihal Siriwardanagea, Pramoda Karunarathna, Preston Samuel, Ravi Mukkamala, Ross Gore, Sachini Rajapakse, Sachin Shetty, Shaifali Kaushik, Wathsala Herath","submitted_at":"2026-07-14T14:36:04Z","abstract_excerpt":"Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a coding copilot, or generating images, the interaction follows a common iterative loop: a request is issued, a result returned, appraised, and the request revised. We observe that this loop is a high-frequency stream of contact events -- moments at which a result meets a person and a conditioned response may fire before deliberate appraisal -- making everyday agent interaction an unrecognised neuroplastic training environment. When a result dis"},"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":"2607.12823","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-14T14:36:04Z","cross_cats_sorted":[],"title_canon_sha256":"45b739272213fdb66847650f85ad26fa6d4ebd553849164c37d8ab8f36b7d85e","abstract_canon_sha256":"0cbc2c720b0723857aec2a8fe6fe5cdda1dbc40541e6527710533d429f9cd2ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T01:22:20.057862Z","signature_b64":"Bfb2BvcXuuVh68CaI+BP+wCqXIYPKZ1dO9TAls+oqjZcos1C2RHJJWYSkUELqwnxl4TNHGVYXjgmfG53c113Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecce66a6279c7f04ed509697529322b38ca5d0f8ee83593fce119cb9eed02ace","last_reissued_at":"2026-07-15T01:22:20.056737Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T01:22:20.056737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Human-AI Agent Interaction as a Neuroplastic Training Environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Amin Hass, Anita H. Clayton, Asanga Gunaratna, Atmaram Yarlagadd, Chalani Rajapakse, Christopher K. Rhea, Eranga Bandara, Gihan Siriwardanagea, Isurunima Kularathna, Kasun De Zoysa, Ng Wee Keong, Nihal Siriwardanagea, Pramoda Karunarathna, Preston Samuel, Ravi Mukkamala, Ross Gore, Sachini Rajapakse, Sachin Shetty, Shaifali Kaushik, Wathsala Herath","submitted_at":"2026-07-14T14:36:04Z","abstract_excerpt":"Interaction with AI agents has become one of the most frequent activities of everyday digital life. Whether conversing with an assistant, working with a coding copilot, or generating images, the interaction follows a common iterative loop: a request is issued, a result returned, appraised, and the request revised. We observe that this loop is a high-frequency stream of contact events -- moments at which a result meets a person and a conditioned response may fire before deliberate appraisal -- making everyday agent interaction an unrecognised neuroplastic training environment. When a result dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.12823","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/2607.12823/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":"2607.12823","created_at":"2026-07-15T01:22:20.057290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.12823v1","created_at":"2026-07-15T01:22:20.057290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.12823","created_at":"2026-07-15T01:22:20.057290+00:00"},{"alias_kind":"pith_short_12","alias_value":"5THGNJRHTR7Q","created_at":"2026-07-15T01:22:20.057290+00:00"},{"alias_kind":"pith_short_16","alias_value":"5THGNJRHTR7QJ3KQ","created_at":"2026-07-15T01:22:20.057290+00:00"},{"alias_kind":"pith_short_8","alias_value":"5THGNJRH","created_at":"2026-07-15T01:22:20.057290+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/5THGNJRHTR7QJ3KQS2LVFEZCWO","json":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO.json","graph_json":"https://pith.science/api/pith-number/5THGNJRHTR7QJ3KQS2LVFEZCWO/graph.json","events_json":"https://pith.science/api/pith-number/5THGNJRHTR7QJ3KQS2LVFEZCWO/events.json","paper":"https://pith.science/paper/5THGNJRH"},"agent_actions":{"view_html":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO","download_json":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO.json","view_paper":"https://pith.science/paper/5THGNJRH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.12823&json=true","fetch_graph":"https://pith.science/api/pith-number/5THGNJRHTR7QJ3KQS2LVFEZCWO/graph.json","fetch_events":"https://pith.science/api/pith-number/5THGNJRHTR7QJ3KQS2LVFEZCWO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO/action/storage_attestation","attest_author":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO/action/author_attestation","sign_citation":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO/action/citation_signature","submit_replication":"https://pith.science/pith/5THGNJRHTR7QJ3KQS2LVFEZCWO/action/replication_record"}},"created_at":"2026-07-15T01:22:20.057290+00:00","updated_at":"2026-07-15T01:22:20.057290+00:00"}