{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FYRJA6EXCAELWSKSNNUP2HCJ53","short_pith_number":"pith:FYRJA6EX","canonical_record":{"source":{"id":"2501.16247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-27T17:48:48Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"58801456e314e3572870f9bde9ff144a325cc8be4dcbb4851ebfad2a1a868ad3","abstract_canon_sha256":"0da32c801f306ea58557e7e7b806b483794e1ef870f257f99e84d436b98ae209"},"schema_version":"1.0"},"canonical_sha256":"2e229078971008bb49526b68fd1c49eece0c6b67b33d2c2ae32c48bde616185f","source":{"kind":"arxiv","id":"2501.16247","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.16247","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"arxiv_version","alias_value":"2501.16247v1","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16247","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_12","alias_value":"FYRJA6EXCAEL","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_16","alias_value":"FYRJA6EXCAELWSKS","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_8","alias_value":"FYRJA6EX","created_at":"2026-07-05T10:05:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FYRJA6EXCAELWSKSNNUP2HCJ53","target":"record","payload":{"canonical_record":{"source":{"id":"2501.16247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-27T17:48:48Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"58801456e314e3572870f9bde9ff144a325cc8be4dcbb4851ebfad2a1a868ad3","abstract_canon_sha256":"0da32c801f306ea58557e7e7b806b483794e1ef870f257f99e84d436b98ae209"},"schema_version":"1.0"},"canonical_sha256":"2e229078971008bb49526b68fd1c49eece0c6b67b33d2c2ae32c48bde616185f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:57.375222Z","signature_b64":"4P0PtV0Y7mmWWu7jrxCoKsiDBjEdADaiq+EVJ7FUOKrKjyUkdN+EXVvHaAv5EPi1oUrEu9patHkAHOtNZB0xDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e229078971008bb49526b68fd1c49eece0c6b67b33d2c2ae32c48bde616185f","last_reissued_at":"2026-07-05T10:05:57.374696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:57.374696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.16247","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E/aMzvaMDAqV8Fww1kj1jVPZXNxKerC8Uu3RP5CfSBkyxeTLXPCYvwKmat/x7WymjMwekRiZAI7empSh1/V5BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T19:06:09.747100Z"},"content_sha256":"592dac716d4019ddde654d141eacc7c0281e829dae0d2ddabc02540d3ae03367","schema_version":"1.0","event_id":"sha256:592dac716d4019ddde654d141eacc7c0281e829dae0d2ddabc02540d3ae03367"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FYRJA6EXCAELWSKSNNUP2HCJ53","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Zero-Shot Decision Tree Construction via Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Andr\\'es Abeliuk, Felipe Urrutia, Lucas Carrasco","submitted_at":"2025-01-27T17:48:48Z","abstract_excerpt":"This paper introduces a novel algorithm for constructing decision trees using large language models (LLMs) in a zero-shot manner based on Classification and Regression Trees (CART) principles. Traditional decision tree induction methods rely heavily on labeled data to recursively partition data using criteria such as information gain or the Gini index. In contrast, we propose a method that uses the pre-trained knowledge embedded in LLMs to build decision trees without requiring training data. Our approach leverages LLMs to perform operations essential for decision tree construction, including "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16247","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/2501.16247/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aHEhOEUs0xqyeHpB4c5Vn6ECzQUPLbhnMQIMedOfMkXYRpITLHoYY4TKMV2xAIBBtrbX/wS+NGKEd1wi6gxCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T19:06:09.747623Z"},"content_sha256":"5144015f1436fd8166091cc7f1fe71c8d42f5277145d616ed87c4e12067d4197","schema_version":"1.0","event_id":"sha256:5144015f1436fd8166091cc7f1fe71c8d42f5277145d616ed87c4e12067d4197"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/bundle.json","state_url":"https://pith.science/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-17T19:06:09Z","links":{"resolver":"https://pith.science/pith/FYRJA6EXCAELWSKSNNUP2HCJ53","bundle":"https://pith.science/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/bundle.json","state":"https://pith.science/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FYRJA6EXCAELWSKSNNUP2HCJ53/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FYRJA6EXCAELWSKSNNUP2HCJ53","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0da32c801f306ea58557e7e7b806b483794e1ef870f257f99e84d436b98ae209","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-27T17:48:48Z","title_canon_sha256":"58801456e314e3572870f9bde9ff144a325cc8be4dcbb4851ebfad2a1a868ad3"},"schema_version":"1.0","source":{"id":"2501.16247","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.16247","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"arxiv_version","alias_value":"2501.16247v1","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.16247","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_12","alias_value":"FYRJA6EXCAEL","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_16","alias_value":"FYRJA6EXCAELWSKS","created_at":"2026-07-05T10:05:57Z"},{"alias_kind":"pith_short_8","alias_value":"FYRJA6EX","created_at":"2026-07-05T10:05:57Z"}],"graph_snapshots":[{"event_id":"sha256:5144015f1436fd8166091cc7f1fe71c8d42f5277145d616ed87c4e12067d4197","target":"graph","created_at":"2026-07-05T10:05:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.16247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper introduces a novel algorithm for constructing decision trees using large language models (LLMs) in a zero-shot manner based on Classification and Regression Trees (CART) principles. Traditional decision tree induction methods rely heavily on labeled data to recursively partition data using criteria such as information gain or the Gini index. In contrast, we propose a method that uses the pre-trained knowledge embedded in LLMs to build decision trees without requiring training data. Our approach leverages LLMs to perform operations essential for decision tree construction, including ","authors_text":"Andr\\'es Abeliuk, Felipe Urrutia, Lucas Carrasco","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-27T17:48:48Z","title":"Zero-Shot Decision Tree Construction via Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.16247","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:592dac716d4019ddde654d141eacc7c0281e829dae0d2ddabc02540d3ae03367","target":"record","created_at":"2026-07-05T10:05:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0da32c801f306ea58557e7e7b806b483794e1ef870f257f99e84d436b98ae209","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-27T17:48:48Z","title_canon_sha256":"58801456e314e3572870f9bde9ff144a325cc8be4dcbb4851ebfad2a1a868ad3"},"schema_version":"1.0","source":{"id":"2501.16247","kind":"arxiv","version":1}},"canonical_sha256":"2e229078971008bb49526b68fd1c49eece0c6b67b33d2c2ae32c48bde616185f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e229078971008bb49526b68fd1c49eece0c6b67b33d2c2ae32c48bde616185f","first_computed_at":"2026-07-05T10:05:57.374696Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:57.374696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4P0PtV0Y7mmWWu7jrxCoKsiDBjEdADaiq+EVJ7FUOKrKjyUkdN+EXVvHaAv5EPi1oUrEu9patHkAHOtNZB0xDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:57.375222Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.16247","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:592dac716d4019ddde654d141eacc7c0281e829dae0d2ddabc02540d3ae03367","sha256:5144015f1436fd8166091cc7f1fe71c8d42f5277145d616ed87c4e12067d4197"],"state_sha256":"b86d72ce78528283fc333b4ac8f24616c9dd2a723ed2626bdcd8de9761c0dd30"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jg2/57ogGwHIoIX2kmKk170bR94Pbh0DMs2A1OQlwp6XlHbGePgP/76vQq1spupn0JSEsJn++BmWi7m05eswBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T19:06:09.751582Z","bundle_sha256":"687a4451507c638fd2520099f20e6a5755cdc207f3c01d8ca38521ef351650a4"}}