{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:METG6ECDW6AQRPOYR7H54GB2M2","short_pith_number":"pith:METG6ECD","schema_version":"1.0","canonical_sha256":"61266f1043b78108bdd88fcfde183a66975b3b4fbcfae10e2da0db7799c7ad45","source":{"kind":"arxiv","id":"2503.01861","version":3},"attestation_state":"computed","paper":{"title":"Towards Enterprise-Ready Computer Using Generalist Agent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.DC","authors_text":"Alon Oved, Asaf Adi, Aviad Sela, Avi Yaeli, Ido Levy, Nir Mashkif, Offer Akrabi, Sami Marreed, Segev Shlomov","submitted_at":"2025-02-24T09:31:56Z","abstract_excerpt":"This paper presents our ongoing work toward developing an enterprise-ready Computer Using Generalist Agent (CUGA) system. Our research highlights the evolutionary nature of building agentic systems suitable for enterprise environments. By integrating state-of-the-art agentic AI techniques with a systematic approach to iterative evaluation, analysis, and refinement, we have achieved rapid and cost-effective performance gains, notably reaching a new state-of-the-art performance on the WebArena and AppWorld benchmarks. We detail our development roadmap, the methodology and tools that facilitated "},"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":"2503.01861","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-02-24T09:31:56Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"73e29620723018e70d8bf59dd7139ad979bf97304040f5152ddbbf9fc98ef860","abstract_canon_sha256":"fa3904361871f3ca5a0d0ee6392a71564ecd290a28c61fcd508225f3e961a45a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:05.086387Z","signature_b64":"hDHlfke203pt+m1Ve97gDZCWI8RPvQgXUYNtlA7NFb7wwOkE9S5/HkkYWvi/dvjedRjtxcDOHRDXvMEiPyKnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61266f1043b78108bdd88fcfde183a66975b3b4fbcfae10e2da0db7799c7ad45","last_reissued_at":"2026-07-05T11:34:05.085943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:05.085943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Enterprise-Ready Computer Using Generalist Agent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.DC","authors_text":"Alon Oved, Asaf Adi, Aviad Sela, Avi Yaeli, Ido Levy, Nir Mashkif, Offer Akrabi, Sami Marreed, Segev Shlomov","submitted_at":"2025-02-24T09:31:56Z","abstract_excerpt":"This paper presents our ongoing work toward developing an enterprise-ready Computer Using Generalist Agent (CUGA) system. Our research highlights the evolutionary nature of building agentic systems suitable for enterprise environments. By integrating state-of-the-art agentic AI techniques with a systematic approach to iterative evaluation, analysis, and refinement, we have achieved rapid and cost-effective performance gains, notably reaching a new state-of-the-art performance on the WebArena and AppWorld benchmarks. We detail our development roadmap, the methodology and tools that facilitated "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.01861","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/2503.01861/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":"2503.01861","created_at":"2026-07-05T11:34:05.085999+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.01861v3","created_at":"2026-07-05T11:34:05.085999+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.01861","created_at":"2026-07-05T11:34:05.085999+00:00"},{"alias_kind":"pith_short_12","alias_value":"METG6ECDW6AQ","created_at":"2026-07-05T11:34:05.085999+00:00"},{"alias_kind":"pith_short_16","alias_value":"METG6ECDW6AQRPOY","created_at":"2026-07-05T11:34:05.085999+00:00"},{"alias_kind":"pith_short_8","alias_value":"METG6ECD","created_at":"2026-07-05T11:34:05.085999+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.08147","citing_title":"Prismata: Confining Cross-Site Prompt Injection in Web Agents","ref_index":54,"is_internal_anchor":true},{"citing_arxiv_id":"2607.05775","citing_title":"Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.29927","citing_title":"Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15505","citing_title":"X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20874","citing_title":"Governance by Construction for Generalist Agents","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15505","citing_title":"X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10513","citing_title":"Agent Mentor: Framing Agent Knowledge through Semantic Trajectory Analysis","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06957","citing_title":"Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2","json":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2.json","graph_json":"https://pith.science/api/pith-number/METG6ECDW6AQRPOYR7H54GB2M2/graph.json","events_json":"https://pith.science/api/pith-number/METG6ECDW6AQRPOYR7H54GB2M2/events.json","paper":"https://pith.science/paper/METG6ECD"},"agent_actions":{"view_html":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2","download_json":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2.json","view_paper":"https://pith.science/paper/METG6ECD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.01861&json=true","fetch_graph":"https://pith.science/api/pith-number/METG6ECDW6AQRPOYR7H54GB2M2/graph.json","fetch_events":"https://pith.science/api/pith-number/METG6ECDW6AQRPOYR7H54GB2M2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2/action/storage_attestation","attest_author":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2/action/author_attestation","sign_citation":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2/action/citation_signature","submit_replication":"https://pith.science/pith/METG6ECDW6AQRPOYR7H54GB2M2/action/replication_record"}},"created_at":"2026-07-05T11:34:05.085999+00:00","updated_at":"2026-07-05T11:34:05.085999+00:00"}