{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:I2FZG7QXCQ3KIZOGFY5YPA45MD","short_pith_number":"pith:I2FZG7QX","schema_version":"1.0","canonical_sha256":"468b937e171436a465c62e3b87839d60db85f5d167b0aaf93d978151cfaa47d4","source":{"kind":"arxiv","id":"2412.06512","version":1},"attestation_state":"computed","paper":{"title":"The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.SE"],"primary_cat":"cs.AI","authors_text":"Jing Sun, Jin Song Dong, Jun Sun, Kailong Wang, Meng Sun, Xiaokun Luan, Xinyue Zuo, Yedi Zhang, Yifan Zhang, Yufan Cai, Zhe Hou, Zhiyuan Wei","submitted_at":"2024-12-09T14:14:21Z","abstract_excerpt":"Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs face a critical challenge: the propensity to produce unreliable outputs due to the inherent limitations of their learning-based nature. Formal methods (FMs), on the other hand, are a well-established computation paradigm that provides mathematically rigorous techniques for modeling, specifying, and verifying the correctness of systems. FMs have been extensi"},"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":"2412.06512","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-12-09T14:14:21Z","cross_cats_sorted":["cs.CL","cs.SE"],"title_canon_sha256":"c8b1e18cd8a7e79e5a5411b3c8602524dca2f142842e00d113020f629c993914","abstract_canon_sha256":"52554cc613ccf50dd58f2394007dd635c7924364f295431e5d41b871a3c13b6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:33.170534Z","signature_b64":"JNRI8kKFEoTvztD08NXppcqCWn5C05o3NwbZww2JHzWnzhw6NJwHC+tE6PncyaBnCuoRML9RoPs+sBLDzxBMBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"468b937e171436a465c62e3b87839d60db85f5d167b0aaf93d978151cfaa47d4","last_reissued_at":"2026-07-05T09:46:33.170025Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:33.170025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.SE"],"primary_cat":"cs.AI","authors_text":"Jing Sun, Jin Song Dong, Jun Sun, Kailong Wang, Meng Sun, Xiaokun Luan, Xinyue Zuo, Yedi Zhang, Yifan Zhang, Yufan Cai, Zhe Hou, Zhiyuan Wei","submitted_at":"2024-12-09T14:14:21Z","abstract_excerpt":"Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs face a critical challenge: the propensity to produce unreliable outputs due to the inherent limitations of their learning-based nature. Formal methods (FMs), on the other hand, are a well-established computation paradigm that provides mathematically rigorous techniques for modeling, specifying, and verifying the correctness of systems. FMs have been extensi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06512","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/2412.06512/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":"2412.06512","created_at":"2026-07-05T09:46:33.170084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.06512v1","created_at":"2026-07-05T09:46:33.170084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06512","created_at":"2026-07-05T09:46:33.170084+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2FZG7QXCQ3K","created_at":"2026-07-05T09:46:33.170084+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2FZG7QXCQ3KIZOG","created_at":"2026-07-05T09:46:33.170084+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2FZG7QX","created_at":"2026-07-05T09:46:33.170084+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24245","citing_title":"AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2506.01770","citing_title":"ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2602.02280","citing_title":"RACC: Representation-Aware Coverage Criteria for LLM Safety Testing","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06334","citing_title":"MANTRA: Synthesizing SMT-Validated Compliance Benchmarks for Tool-Using LLM Agents","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07321","citing_title":"Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation","ref_index":70,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07935","citing_title":"TraceFix: Repairing Agent Coordination Protocols with TLA+ Counterexamples","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD","json":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD.json","graph_json":"https://pith.science/api/pith-number/I2FZG7QXCQ3KIZOGFY5YPA45MD/graph.json","events_json":"https://pith.science/api/pith-number/I2FZG7QXCQ3KIZOGFY5YPA45MD/events.json","paper":"https://pith.science/paper/I2FZG7QX"},"agent_actions":{"view_html":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD","download_json":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD.json","view_paper":"https://pith.science/paper/I2FZG7QX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.06512&json=true","fetch_graph":"https://pith.science/api/pith-number/I2FZG7QXCQ3KIZOGFY5YPA45MD/graph.json","fetch_events":"https://pith.science/api/pith-number/I2FZG7QXCQ3KIZOGFY5YPA45MD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD/action/storage_attestation","attest_author":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD/action/author_attestation","sign_citation":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD/action/citation_signature","submit_replication":"https://pith.science/pith/I2FZG7QXCQ3KIZOGFY5YPA45MD/action/replication_record"}},"created_at":"2026-07-05T09:46:33.170084+00:00","updated_at":"2026-07-05T09:46:33.170084+00:00"}