{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NCVG6YMN464JJTCQFYA77NHJX3","short_pith_number":"pith:NCVG6YMN","schema_version":"1.0","canonical_sha256":"68aa6f618de7b894cc502e01ffb4e9bede8714481829b64f9c1ebe728ff96646","source":{"kind":"arxiv","id":"2410.09854","version":1},"attestation_state":"computed","paper":{"title":"A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Jingwei Shen, Ru Chen, Xiao He","submitted_at":"2024-10-13T14:28:04Z","abstract_excerpt":"Domain modeling, a crucial part of model-driven engineering, demands extensive domain knowledge and experience from engineers. When the system description is highly complicated, the modeling task can become particularly challenging and time-consuming. Large language Models(LLMs) can assist by automatically generating an initial object model from the system description. Although LLMs have demonstrated remarkable code-generation ability, they still struggle with model-generation using a single prompt. In real-world domain modeling, engineers usually decompose complex tasks into easily solvable s"},"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":"2410.09854","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-10-13T14:28:04Z","cross_cats_sorted":[],"title_canon_sha256":"0ce700456c19f2ceb53d8e809d2c0356a11042edcba7c5102789eb06ca6b3f54","abstract_canon_sha256":"aba77bc2fcc91c2cee1989b91be250ee12dd7b1bfe9652fe141bf99b919753ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:07.106967Z","signature_b64":"w7U1+1TtfzfyBis/oXf1vqxlUb9dhYgDmHHGRfI6J0gfKZly2w/w0UBJsZSVPnChssKq8W1H+anwWhMhK+sXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68aa6f618de7b894cc502e01ffb4e9bede8714481829b64f9c1ebe728ff96646","last_reissued_at":"2026-07-05T09:20:07.106481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:07.106481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Jingwei Shen, Ru Chen, Xiao He","submitted_at":"2024-10-13T14:28:04Z","abstract_excerpt":"Domain modeling, a crucial part of model-driven engineering, demands extensive domain knowledge and experience from engineers. When the system description is highly complicated, the modeling task can become particularly challenging and time-consuming. Large language Models(LLMs) can assist by automatically generating an initial object model from the system description. Although LLMs have demonstrated remarkable code-generation ability, they still struggle with model-generation using a single prompt. In real-world domain modeling, engineers usually decompose complex tasks into easily solvable s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09854","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/2410.09854/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":"2410.09854","created_at":"2026-07-05T09:20:07.106544+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09854v1","created_at":"2026-07-05T09:20:07.106544+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09854","created_at":"2026-07-05T09:20:07.106544+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCVG6YMN464J","created_at":"2026-07-05T09:20:07.106544+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCVG6YMN464JJTCQ","created_at":"2026-07-05T09:20:07.106544+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCVG6YMN","created_at":"2026-07-05T09:20:07.106544+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.02399","citing_title":"Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.00851","citing_title":"Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3","json":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3.json","graph_json":"https://pith.science/api/pith-number/NCVG6YMN464JJTCQFYA77NHJX3/graph.json","events_json":"https://pith.science/api/pith-number/NCVG6YMN464JJTCQFYA77NHJX3/events.json","paper":"https://pith.science/paper/NCVG6YMN"},"agent_actions":{"view_html":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3","download_json":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3.json","view_paper":"https://pith.science/paper/NCVG6YMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09854&json=true","fetch_graph":"https://pith.science/api/pith-number/NCVG6YMN464JJTCQFYA77NHJX3/graph.json","fetch_events":"https://pith.science/api/pith-number/NCVG6YMN464JJTCQFYA77NHJX3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3/action/storage_attestation","attest_author":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3/action/author_attestation","sign_citation":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3/action/citation_signature","submit_replication":"https://pith.science/pith/NCVG6YMN464JJTCQFYA77NHJX3/action/replication_record"}},"created_at":"2026-07-05T09:20:07.106544+00:00","updated_at":"2026-07-05T09:20:07.106544+00:00"}