{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YR3BCZNRRZDJVEJHDHTJNAZYEW","short_pith_number":"pith:YR3BCZNR","schema_version":"1.0","canonical_sha256":"c4761165b18e469a912719e696833825b422369bd2e19e124ee4e41e37be748f","source":{"kind":"arxiv","id":"2506.10021","version":1},"attestation_state":"computed","paper":{"title":"From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Jordi de la Torre","submitted_at":"2025-06-08T20:12:06Z","abstract_excerpt":"We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles to guide future implementations of interactive AI systems that integrate symbolic programming with ne"},"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":"2506.10021","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PL","submitted_at":"2025-06-08T20:12:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"22d0e9670492fdc18120a12de180f2042dfa8c9010c55de4f064cf39aa167284","abstract_canon_sha256":"27cdaa0bf86b1ea6336b5e055b6a4a64e27821994353f8a5b99328a866af4ac0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:15.298037Z","signature_b64":"lKHZEvDiFT+NVh/GWHW6wPjhqMt4I6o35B4WqXgJJ25yH2mThJeWThUynivRV5qNYQ5+bpYktobBNjXH38zoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4761165b18e469a912719e696833825b422369bd2e19e124ee4e41e37be748f","last_reissued_at":"2026-07-05T11:20:15.297478Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:15.297478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.PL","authors_text":"Jordi de la Torre","submitted_at":"2025-06-08T20:12:06Z","abstract_excerpt":"We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles to guide future implementations of interactive AI systems that integrate symbolic programming with ne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10021","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/2506.10021/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":"2506.10021","created_at":"2026-07-05T11:20:15.297546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.10021v1","created_at":"2026-07-05T11:20:15.297546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10021","created_at":"2026-07-05T11:20:15.297546+00:00"},{"alias_kind":"pith_short_12","alias_value":"YR3BCZNRRZDJ","created_at":"2026-07-05T11:20:15.297546+00:00"},{"alias_kind":"pith_short_16","alias_value":"YR3BCZNRRZDJVEJH","created_at":"2026-07-05T11:20:15.297546+00:00"},{"alias_kind":"pith_short_8","alias_value":"YR3BCZNR","created_at":"2026-07-05T11:20:15.297546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08740","citing_title":"Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2606.04025","citing_title":"The Biomimetic Architecture of Software 4.0","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW","json":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW.json","graph_json":"https://pith.science/api/pith-number/YR3BCZNRRZDJVEJHDHTJNAZYEW/graph.json","events_json":"https://pith.science/api/pith-number/YR3BCZNRRZDJVEJHDHTJNAZYEW/events.json","paper":"https://pith.science/paper/YR3BCZNR"},"agent_actions":{"view_html":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW","download_json":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW.json","view_paper":"https://pith.science/paper/YR3BCZNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.10021&json=true","fetch_graph":"https://pith.science/api/pith-number/YR3BCZNRRZDJVEJHDHTJNAZYEW/graph.json","fetch_events":"https://pith.science/api/pith-number/YR3BCZNRRZDJVEJHDHTJNAZYEW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW/action/storage_attestation","attest_author":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW/action/author_attestation","sign_citation":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW/action/citation_signature","submit_replication":"https://pith.science/pith/YR3BCZNRRZDJVEJHDHTJNAZYEW/action/replication_record"}},"created_at":"2026-07-05T11:20:15.297546+00:00","updated_at":"2026-07-05T11:20:15.297546+00:00"}