{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3LHZHALLPLNYXWIIUB2W2EUMK5","short_pith_number":"pith:3LHZHALL","schema_version":"1.0","canonical_sha256":"dacf93816b7adb8bd908a0756d128c57420c17037ce767e08e9d07c36a693f53","source":{"kind":"arxiv","id":"2404.18852","version":2},"attestation_state":"computed","paper":{"title":"VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Aidan Z.H. Yang, Brandon Paulsen, Daniel Kroening, Josiah Dodds, Yoshiki Takashima","submitted_at":"2024-04-29T16:45:03Z","abstract_excerpt":"Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and correct transpiling of existing code-bases to Rust. Existing work falls into two categories: rule-based and large language model (LLM)-based. While rule-based approaches can theoretically produce correct transpilations that maintain input-output equivalence to the original, they often yield unreadable Rust code that uses unsafe subsets of the Rust language. On "},"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":"2404.18852","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PL","submitted_at":"2024-04-29T16:45:03Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"7ffeb3c7d8dc5f0e2308cd16b9edaef28d8ad1361d3234ff28a0bd0ba3437559","abstract_canon_sha256":"440b91a86299a3ab190690de3551db9d4c831f8a34af8bd1634fed4156f794dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:21.764577Z","signature_b64":"L0UB5y5IthGO2NLbUMlpY/f1f6M1MnbGfm70b3IVs7ffsOD7I/fkDXlYQYv78mI0MMie+twhtxVBz4ipPP+ECg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dacf93816b7adb8bd908a0756d128c57420c17037ce767e08e9d07c36a693f53","last_reissued_at":"2026-07-05T08:23:21.764028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:21.764028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Aidan Z.H. Yang, Brandon Paulsen, Daniel Kroening, Josiah Dodds, Yoshiki Takashima","submitted_at":"2024-04-29T16:45:03Z","abstract_excerpt":"Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and correct transpiling of existing code-bases to Rust. Existing work falls into two categories: rule-based and large language model (LLM)-based. While rule-based approaches can theoretically produce correct transpilations that maintain input-output equivalence to the original, they often yield unreadable Rust code that uses unsafe subsets of the Rust language. On "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.18852","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/2404.18852/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":"2404.18852","created_at":"2026-07-05T08:23:21.764086+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.18852v2","created_at":"2026-07-05T08:23:21.764086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.18852","created_at":"2026-07-05T08:23:21.764086+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LHZHALLPLNY","created_at":"2026-07-05T08:23:21.764086+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LHZHALLPLNYXWII","created_at":"2026-07-05T08:23:21.764086+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LHZHALL","created_at":"2026-07-05T08:23:21.764086+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":13,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11470","citing_title":"The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes","ref_index":279,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25411","citing_title":"Heimdall: Formally Verified Automated Migration of Legacy eBPF Programs to Rust","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2505.15858","citing_title":"Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2505.10708","citing_title":"SafeTrans: LLM-assisted Transpilation from C to Rust","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2510.10956","citing_title":"Project-Level C-to-Rust Translation via Pointer Knowledge Graphs","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2511.02521","citing_title":"Large Lemma Miners: Can LLMs do Induction Proofs for Hardware?","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12731","citing_title":"Finding a Crab in the C: Assured Translation via Comparative Symbolic Execution","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02852","citing_title":"Dependency-Guided Repository-Level C-to-Rust Translation with Reinforcement Alignment","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12048","citing_title":"ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07341","citing_title":"ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories","ref_index":92,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04527","citing_title":"ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18027","citing_title":"CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15485","citing_title":"LLM4C2Rust: Large Language Models for Automated Memory-Safe Code Transpilation","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5","json":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5.json","graph_json":"https://pith.science/api/pith-number/3LHZHALLPLNYXWIIUB2W2EUMK5/graph.json","events_json":"https://pith.science/api/pith-number/3LHZHALLPLNYXWIIUB2W2EUMK5/events.json","paper":"https://pith.science/paper/3LHZHALL"},"agent_actions":{"view_html":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5","download_json":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5.json","view_paper":"https://pith.science/paper/3LHZHALL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.18852&json=true","fetch_graph":"https://pith.science/api/pith-number/3LHZHALLPLNYXWIIUB2W2EUMK5/graph.json","fetch_events":"https://pith.science/api/pith-number/3LHZHALLPLNYXWIIUB2W2EUMK5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5/action/storage_attestation","attest_author":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5/action/author_attestation","sign_citation":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5/action/citation_signature","submit_replication":"https://pith.science/pith/3LHZHALLPLNYXWIIUB2W2EUMK5/action/replication_record"}},"created_at":"2026-07-05T08:23:21.764086+00:00","updated_at":"2026-07-05T08:23:21.764086+00:00"}