{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:REDN7WY4HPKYQ24H7AGE64JFXT","short_pith_number":"pith:REDN7WY4","schema_version":"1.0","canonical_sha256":"8906dfdb1c3bd5886b87f80c4f7125bcda0ef54fc72121192891d4af67332813","source":{"kind":"arxiv","id":"2504.19394","version":2},"attestation_state":"computed","paper":{"title":"LLMs for Engineering: Teaching Models to Design High Powered Rockets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Toby Simonds","submitted_at":"2025-04-27T23:59:39Z","abstract_excerpt":"Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results a"},"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":"2504.19394","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-04-27T23:59:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cccd6c1fd4c6471a8d285dbca2770ca4c40a99e1d76f66a943bf449c2525b4df","abstract_canon_sha256":"0a09732d01967d787d9950778194aab1b3dc8abb64596f6bf38504e45c63b4e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:55.063240Z","signature_b64":"u/UjyGCz3tUbMDRimurF0vyiClpaWp1xppceNexcOydqA3CldS+4O6bI2rtw7u4FsnyFxz4wcYSWeBU5gZ5fBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8906dfdb1c3bd5886b87f80c4f7125bcda0ef54fc72121192891d4af67332813","last_reissued_at":"2026-07-05T10:55:55.062657Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:55.062657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLMs for Engineering: Teaching Models to Design High Powered Rockets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Toby Simonds","submitted_at":"2025-04-27T23:59:39Z","abstract_excerpt":"Large Language Models (LLMs) have transformed software engineering, but their application to physical engineering domains remains underexplored. This paper evaluates LLMs' capabilities in high-powered rocketry design through RocketBench, a benchmark connecting LLMs to high-fidelity rocket simulations. We test models on two increasingly complex design tasks: target altitude optimization and precision landing challenges. Our findings reveal that while state-of-the-art LLMs demonstrate strong baseline engineering knowledge, they struggle to iterate on their designs when given simulation results a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19394","kind":"arxiv","version":2},"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/2504.19394/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":"2504.19394","created_at":"2026-07-05T10:55:55.062716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.19394v2","created_at":"2026-07-05T10:55:55.062716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19394","created_at":"2026-07-05T10:55:55.062716+00:00"},{"alias_kind":"pith_short_12","alias_value":"REDN7WY4HPKY","created_at":"2026-07-05T10:55:55.062716+00:00"},{"alias_kind":"pith_short_16","alias_value":"REDN7WY4HPKYQ24H","created_at":"2026-07-05T10:55:55.062716+00:00"},{"alias_kind":"pith_short_8","alias_value":"REDN7WY4","created_at":"2026-07-05T10:55:55.062716+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00097","citing_title":"RocketSmith: Agentic Additive Manufacturing of High-Powered Rockets","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06747","citing_title":"TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT","json":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT.json","graph_json":"https://pith.science/api/pith-number/REDN7WY4HPKYQ24H7AGE64JFXT/graph.json","events_json":"https://pith.science/api/pith-number/REDN7WY4HPKYQ24H7AGE64JFXT/events.json","paper":"https://pith.science/paper/REDN7WY4"},"agent_actions":{"view_html":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT","download_json":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT.json","view_paper":"https://pith.science/paper/REDN7WY4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.19394&json=true","fetch_graph":"https://pith.science/api/pith-number/REDN7WY4HPKYQ24H7AGE64JFXT/graph.json","fetch_events":"https://pith.science/api/pith-number/REDN7WY4HPKYQ24H7AGE64JFXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT/action/storage_attestation","attest_author":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT/action/author_attestation","sign_citation":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT/action/citation_signature","submit_replication":"https://pith.science/pith/REDN7WY4HPKYQ24H7AGE64JFXT/action/replication_record"}},"created_at":"2026-07-05T10:55:55.062716+00:00","updated_at":"2026-07-05T10:55:55.062716+00:00"}