{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XQQOTETCIIRWUINASIZJIZACCX","short_pith_number":"pith:XQQOTETC","schema_version":"1.0","canonical_sha256":"bc20e9926242236a21a0923294640215ea521a1499ab51e1786d09c824c9af72","source":{"kind":"arxiv","id":"2502.03511","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Exploration of ChatGPT's Ability to Support Problem Formulation Tasks for Mission Engineering and a Documentation of its Performance Variability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Max Ofsa, Taylan G. Topcu","submitted_at":"2025-02-05T17:58:23Z","abstract_excerpt":"Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-pur"},"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":"2502.03511","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-02-05T17:58:23Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"50365d571ec312a97b03f88a328a53d804d576614f285ea2fb3b91de41e8c634","abstract_canon_sha256":"0d341701050832e1d933b6b888d0c3467e153536547f062bb810433c1d3fb401"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:11.375970Z","signature_b64":"sHSTakRb1kM9vysPbAwCm0+R4SLgTW9hgbEt2KmMzpuT7F9yXruCWxqy/Mt2uN911nk6qKTHyO2NOCA38R1gCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc20e9926242236a21a0923294640215ea521a1499ab51e1786d09c824c9af72","last_reissued_at":"2026-07-05T10:10:11.375636Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:11.375636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Exploration of ChatGPT's Ability to Support Problem Formulation Tasks for Mission Engineering and a Documentation of its Performance Variability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Max Ofsa, Taylan G. Topcu","submitted_at":"2025-02-05T17:58:23Z","abstract_excerpt":"Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-pur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03511","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/2502.03511/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":"2502.03511","created_at":"2026-07-05T10:10:11.375690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.03511v1","created_at":"2026-07-05T10:10:11.375690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03511","created_at":"2026-07-05T10:10:11.375690+00:00"},{"alias_kind":"pith_short_12","alias_value":"XQQOTETCIIRW","created_at":"2026-07-05T10:10:11.375690+00:00"},{"alias_kind":"pith_short_16","alias_value":"XQQOTETCIIRWUINA","created_at":"2026-07-05T10:10:11.375690+00:00"},{"alias_kind":"pith_short_8","alias_value":"XQQOTETC","created_at":"2026-07-05T10:10:11.375690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09690","citing_title":"Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes","ref_index":123,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX","json":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX.json","graph_json":"https://pith.science/api/pith-number/XQQOTETCIIRWUINASIZJIZACCX/graph.json","events_json":"https://pith.science/api/pith-number/XQQOTETCIIRWUINASIZJIZACCX/events.json","paper":"https://pith.science/paper/XQQOTETC"},"agent_actions":{"view_html":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX","download_json":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX.json","view_paper":"https://pith.science/paper/XQQOTETC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.03511&json=true","fetch_graph":"https://pith.science/api/pith-number/XQQOTETCIIRWUINASIZJIZACCX/graph.json","fetch_events":"https://pith.science/api/pith-number/XQQOTETCIIRWUINASIZJIZACCX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX/action/storage_attestation","attest_author":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX/action/author_attestation","sign_citation":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX/action/citation_signature","submit_replication":"https://pith.science/pith/XQQOTETCIIRWUINASIZJIZACCX/action/replication_record"}},"created_at":"2026-07-05T10:10:11.375690+00:00","updated_at":"2026-07-05T10:10:11.375690+00:00"}