{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FOJKOPJ2DIB6AAAA43BWDIF6ZK","short_pith_number":"pith:FOJKOPJ2","schema_version":"1.0","canonical_sha256":"2b92a73d3a1a03e00000e6c361a0beca9219b78165564e237afbdca2d6f1f861","source":{"kind":"arxiv","id":"2407.02524","version":1},"attestation_state":"computed","paper":{"title":"Meta Large Language Model Compiler: Foundation Models of Compiler Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Baptiste Roziere, Chris Cummins, Dejan Grubisic, Gabriel Synnaeve, Hugh Leather, Jonas Gehring, Volker Seeker","submitted_at":"2024-06-27T21:47:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models specifically designed for code optimization tasks. Built on the foundation of Code Llama, LLM Compiler en"},"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":"2407.02524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PL","submitted_at":"2024-06-27T21:47:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f6e6fdc1017d4553a4485912efbdcf6779dbb7418b7aced39e771b7836215931","abstract_canon_sha256":"f0153be463e9067870fb2ed22a55600ff8448825d2b722bc511b2e2cb6521129"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:37.914520Z","signature_b64":"MVP9VPTKINNZnSAWJTVOnFAVt50BrZEIsZuo9Ss4ulSSzK1n6zg7bWY/2VNlL0z4PwnHtIZ5+Lwb2gpYWzBkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b92a73d3a1a03e00000e6c361a0beca9219b78165564e237afbdca2d6f1f861","last_reissued_at":"2026-07-05T08:39:37.914169Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:37.914169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta Large Language Model Compiler: Foundation Models of Compiler Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Baptiste Roziere, Chris Cummins, Dejan Grubisic, Gabriel Synnaeve, Hugh Leather, Jonas Gehring, Volker Seeker","submitted_at":"2024-06-27T21:47:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models specifically designed for code optimization tasks. Built on the foundation of Code Llama, LLM Compiler en"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02524","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/2407.02524/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":"2407.02524","created_at":"2026-07-05T08:39:37.914224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.02524v1","created_at":"2026-07-05T08:39:37.914224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02524","created_at":"2026-07-05T08:39:37.914224+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOJKOPJ2DIB6","created_at":"2026-07-05T08:39:37.914224+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOJKOPJ2DIB6AAAA","created_at":"2026-07-05T08:39:37.914224+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOJKOPJ2","created_at":"2026-07-05T08:39:37.914224+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20373","citing_title":"AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04025","citing_title":"The Biomimetic Architecture of Software 4.0","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25954","citing_title":"Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2506.01249","citing_title":"SysLLMatic: Large Language Models are Software System Optimizers","ref_index":66,"is_internal_anchor":false},{"citing_arxiv_id":"2502.18449","citing_title":"SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17364","citing_title":"LLM-Guided Strategy Synthesis for Scalable Equality Saturation","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK","json":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK.json","graph_json":"https://pith.science/api/pith-number/FOJKOPJ2DIB6AAAA43BWDIF6ZK/graph.json","events_json":"https://pith.science/api/pith-number/FOJKOPJ2DIB6AAAA43BWDIF6ZK/events.json","paper":"https://pith.science/paper/FOJKOPJ2"},"agent_actions":{"view_html":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK","download_json":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK.json","view_paper":"https://pith.science/paper/FOJKOPJ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.02524&json=true","fetch_graph":"https://pith.science/api/pith-number/FOJKOPJ2DIB6AAAA43BWDIF6ZK/graph.json","fetch_events":"https://pith.science/api/pith-number/FOJKOPJ2DIB6AAAA43BWDIF6ZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK/action/storage_attestation","attest_author":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK/action/author_attestation","sign_citation":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK/action/citation_signature","submit_replication":"https://pith.science/pith/FOJKOPJ2DIB6AAAA43BWDIF6ZK/action/replication_record"}},"created_at":"2026-07-05T08:39:37.914224+00:00","updated_at":"2026-07-05T08:39:37.914224+00:00"}