{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LEAGIAIRYPCW7PQ7TX4FDB3OXR","short_pith_number":"pith:LEAGIAIR","schema_version":"1.0","canonical_sha256":"5900640111c3c56fbe1f9df851876ebc4c264ccedc4e60b8546ae66fb73261be","source":{"kind":"arxiv","id":"2405.12842","version":1},"attestation_state":"computed","paper":{"title":"SmartFlow: Robotic Process Automation using LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Arushi Jain, Gautam Shroff, Lovekesh Vig, Monika Sharma, Shubham Paliwal","submitted_at":"2024-05-21T14:49:12Z","abstract_excerpt":"Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. In this context, we present SmartFlow, an AI-based RPA system that uses pre-trained large language models (LLMs) coupled with deep-learning based image understanding. Our system can adapt to new scenarios, including chang"},"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":"2405.12842","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-21T14:49:12Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"4909d9658dd1ef4cfb7016ae573eb291872d986c8540ac1b0aa5458cba47bbd8","abstract_canon_sha256":"1db8cd3e90b2c2cb3e2a441e8df24e87a2df028c141ae5040fc7261d4ba12672"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:26.956595Z","signature_b64":"aLtcsWVmZ0zMWkDURR9oRyo+QI5sM+0rdy9jbCzucV62t7lCPgXpO5CK/2lSv/aTNzZ1cQm0Axpb/BvaGFeMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5900640111c3c56fbe1f9df851876ebc4c264ccedc4e60b8546ae66fb73261be","last_reissued_at":"2026-07-05T08:21:26.956145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:26.956145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SmartFlow: Robotic Process Automation using LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Arushi Jain, Gautam Shroff, Lovekesh Vig, Monika Sharma, Shubham Paliwal","submitted_at":"2024-05-21T14:49:12Z","abstract_excerpt":"Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. In this context, we present SmartFlow, an AI-based RPA system that uses pre-trained large language models (LLMs) coupled with deep-learning based image understanding. Our system can adapt to new scenarios, including chang"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.12842","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/2405.12842/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":"2405.12842","created_at":"2026-07-05T08:21:26.956205+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.12842v1","created_at":"2026-07-05T08:21:26.956205+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.12842","created_at":"2026-07-05T08:21:26.956205+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEAGIAIRYPCW","created_at":"2026-07-05T08:21:26.956205+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEAGIAIRYPCW7PQ7","created_at":"2026-07-05T08:21:26.956205+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEAGIAIR","created_at":"2026-07-05T08:21:26.956205+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/LEAGIAIRYPCW7PQ7TX4FDB3OXR","json":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR.json","graph_json":"https://pith.science/api/pith-number/LEAGIAIRYPCW7PQ7TX4FDB3OXR/graph.json","events_json":"https://pith.science/api/pith-number/LEAGIAIRYPCW7PQ7TX4FDB3OXR/events.json","paper":"https://pith.science/paper/LEAGIAIR"},"agent_actions":{"view_html":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR","download_json":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR.json","view_paper":"https://pith.science/paper/LEAGIAIR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.12842&json=true","fetch_graph":"https://pith.science/api/pith-number/LEAGIAIRYPCW7PQ7TX4FDB3OXR/graph.json","fetch_events":"https://pith.science/api/pith-number/LEAGIAIRYPCW7PQ7TX4FDB3OXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR/action/storage_attestation","attest_author":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR/action/author_attestation","sign_citation":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR/action/citation_signature","submit_replication":"https://pith.science/pith/LEAGIAIRYPCW7PQ7TX4FDB3OXR/action/replication_record"}},"created_at":"2026-07-05T08:21:26.956205+00:00","updated_at":"2026-07-05T08:21:26.956205+00:00"}