{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MR37JEZFSW3AS2GTQ6IKQV72WO","short_pith_number":"pith:MR37JEZF","schema_version":"1.0","canonical_sha256":"6477f4932595b60968d38790a857fab38d4040567a41b08789ded337fa846a61","source":{"kind":"arxiv","id":"2605.21622","version":1},"attestation_state":"computed","paper":{"title":"TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Faez Ahmed, Hongrui Chen, Isabella A. Stewart","submitted_at":"2026-05-20T18:32:56Z","abstract_excerpt":"Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those preferences. We present TO-Agents, a multi-agent AI framework that connects natural-language design intent with iterative topology optimization. The framework converts a human-provided problem description into validated solver inputs, runs a topology optimization solver, renders the resulting 3D topology, and uses multi-view vision-language reasonin"},"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":"2605.21622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-20T18:32:56Z","cross_cats_sorted":[],"title_canon_sha256":"1883e002bcb2685eb6e1a5909acbe4d4b4fd1056dfdceb8fb80988fae72c4af5","abstract_canon_sha256":"9a2e9e5caf76d6a10eae352d8d7155ef93e94737d179208ee2750473d348e787"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:25.817039Z","signature_b64":"clcYV8vrMbTd6S2zoLGWWgNZEx5rMlasiNEYSghuyIv2ByHDQ3ixovBVmSS4SvTghxi43/lOe7LMt/dsRLt8Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6477f4932595b60968d38790a857fab38d4040567a41b08789ded337fa846a61","last_reissued_at":"2026-05-22T01:03:25.816590Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:25.816590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Faez Ahmed, Hongrui Chen, Isabella A. Stewart","submitted_at":"2026-05-20T18:32:56Z","abstract_excerpt":"Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those preferences. We present TO-Agents, a multi-agent AI framework that connects natural-language design intent with iterative topology optimization. The framework converts a human-provided problem description into validated solver inputs, runs a topology optimization solver, renders the resulting 3D topology, and uses multi-view vision-language reasonin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21622","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/2605.21622/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":"2605.21622","created_at":"2026-05-22T01:03:25.816653+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21622v1","created_at":"2026-05-22T01:03:25.816653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21622","created_at":"2026-05-22T01:03:25.816653+00:00"},{"alias_kind":"pith_short_12","alias_value":"MR37JEZFSW3A","created_at":"2026-05-22T01:03:25.816653+00:00"},{"alias_kind":"pith_short_16","alias_value":"MR37JEZFSW3AS2GT","created_at":"2026-05-22T01:03:25.816653+00:00"},{"alias_kind":"pith_short_8","alias_value":"MR37JEZF","created_at":"2026-05-22T01:03:25.816653+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/MR37JEZFSW3AS2GTQ6IKQV72WO","json":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO.json","graph_json":"https://pith.science/api/pith-number/MR37JEZFSW3AS2GTQ6IKQV72WO/graph.json","events_json":"https://pith.science/api/pith-number/MR37JEZFSW3AS2GTQ6IKQV72WO/events.json","paper":"https://pith.science/paper/MR37JEZF"},"agent_actions":{"view_html":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO","download_json":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO.json","view_paper":"https://pith.science/paper/MR37JEZF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21622&json=true","fetch_graph":"https://pith.science/api/pith-number/MR37JEZFSW3AS2GTQ6IKQV72WO/graph.json","fetch_events":"https://pith.science/api/pith-number/MR37JEZFSW3AS2GTQ6IKQV72WO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO/action/storage_attestation","attest_author":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO/action/author_attestation","sign_citation":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO/action/citation_signature","submit_replication":"https://pith.science/pith/MR37JEZFSW3AS2GTQ6IKQV72WO/action/replication_record"}},"created_at":"2026-05-22T01:03:25.816653+00:00","updated_at":"2026-05-22T01:03:25.816653+00:00"}