{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V657D54KZE2N7S2UPZGGWPHCFI","short_pith_number":"pith:V657D54K","schema_version":"1.0","canonical_sha256":"afbbf1f78ac934dfcb547e4c6b3ce22a0135165625290985c4c3a9aefe5d4774","source":{"kind":"arxiv","id":"2507.12644","version":1},"attestation_state":"computed","paper":{"title":"VLMgineer: Vision Language Models as Robotic Toolsmiths","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Dinesh Jayaraman, George Jiayuan Gao, Junyao Shi, Nadia Figueroa, Tianyu Li, Yihan Li, Zizhe Zhang","submitted_at":"2025-07-16T21:30:05Z","abstract_excerpt":"Tool design and use reflect the ability to understand and manipulate the physical world through creativity, planning, and foresight. As such, these capabilities are often regarded as measurable indicators of intelligence across biological species. While much of today's research on robotic intelligence focuses on generating better controllers, inventing smarter tools offers a complementary form of physical intelligence: shifting the onus of problem-solving onto the tool's design. Given the vast and impressive common-sense, reasoning, and creative capabilities of today's foundation models, we in"},"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":"2507.12644","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-16T21:30:05Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"be5db562f5f1059f09241ef263675040de8fd77a16a31dab19f4c5044e561df9","abstract_canon_sha256":"779e1e14d9512068c2b653ccb19197f09613c6eaba690c17cf1943d8aed3134b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:36.470596Z","signature_b64":"EwZdX8EZYwaaA2cgxJSvi1iFYrxX28tm9e41snfgBUxg+8o3Ijv6tIqdzqZqSA0F0Cu/UwRSJtT+8JJ73xFlDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afbbf1f78ac934dfcb547e4c6b3ce22a0135165625290985c4c3a9aefe5d4774","last_reissued_at":"2026-07-05T11:38:36.470082Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:36.470082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VLMgineer: Vision Language Models as Robotic Toolsmiths","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Dinesh Jayaraman, George Jiayuan Gao, Junyao Shi, Nadia Figueroa, Tianyu Li, Yihan Li, Zizhe Zhang","submitted_at":"2025-07-16T21:30:05Z","abstract_excerpt":"Tool design and use reflect the ability to understand and manipulate the physical world through creativity, planning, and foresight. As such, these capabilities are often regarded as measurable indicators of intelligence across biological species. While much of today's research on robotic intelligence focuses on generating better controllers, inventing smarter tools offers a complementary form of physical intelligence: shifting the onus of problem-solving onto the tool's design. Given the vast and impressive common-sense, reasoning, and creative capabilities of today's foundation models, we in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12644","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/2507.12644/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":"2507.12644","created_at":"2026-07-05T11:38:36.470139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.12644v1","created_at":"2026-07-05T11:38:36.470139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12644","created_at":"2026-07-05T11:38:36.470139+00:00"},{"alias_kind":"pith_short_12","alias_value":"V657D54KZE2N","created_at":"2026-07-05T11:38:36.470139+00:00"},{"alias_kind":"pith_short_16","alias_value":"V657D54KZE2N7S2U","created_at":"2026-07-05T11:38:36.470139+00:00"},{"alias_kind":"pith_short_8","alias_value":"V657D54K","created_at":"2026-07-05T11:38:36.470139+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.02089","citing_title":"ESC: Emotional Self-Correction for Reliable Vision-Language Models","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI","json":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI.json","graph_json":"https://pith.science/api/pith-number/V657D54KZE2N7S2UPZGGWPHCFI/graph.json","events_json":"https://pith.science/api/pith-number/V657D54KZE2N7S2UPZGGWPHCFI/events.json","paper":"https://pith.science/paper/V657D54K"},"agent_actions":{"view_html":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI","download_json":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI.json","view_paper":"https://pith.science/paper/V657D54K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.12644&json=true","fetch_graph":"https://pith.science/api/pith-number/V657D54KZE2N7S2UPZGGWPHCFI/graph.json","fetch_events":"https://pith.science/api/pith-number/V657D54KZE2N7S2UPZGGWPHCFI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI/action/storage_attestation","attest_author":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI/action/author_attestation","sign_citation":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI/action/citation_signature","submit_replication":"https://pith.science/pith/V657D54KZE2N7S2UPZGGWPHCFI/action/replication_record"}},"created_at":"2026-07-05T11:38:36.470139+00:00","updated_at":"2026-07-05T11:38:36.470139+00:00"}