{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VX4YFMXBCLXCKOKQIUXPHZ25IL","short_pith_number":"pith:VX4YFMXB","canonical_record":{"source":{"id":"2410.21520","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-28T20:42:46Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"69a564882375fcc19d8508ac2106f6c3957e5687ebc3121fcf0b30bab12f665e","abstract_canon_sha256":"5ef12d7b44089b9f1264fdfb9628c1854e97f6a74e762f4cb24e70401bedd603"},"schema_version":"1.0"},"canonical_sha256":"adf982b2e112ee253950452ef3e75d42f855e592df03cfed033dce0d3bef05eb","source":{"kind":"arxiv","id":"2410.21520","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.21520","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"arxiv_version","alias_value":"2410.21520v4","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.21520","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_12","alias_value":"VX4YFMXBCLXC","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_16","alias_value":"VX4YFMXBCLXCKOKQ","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_8","alias_value":"VX4YFMXB","created_at":"2026-07-05T11:58:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VX4YFMXBCLXCKOKQIUXPHZ25IL","target":"record","payload":{"canonical_record":{"source":{"id":"2410.21520","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-28T20:42:46Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"69a564882375fcc19d8508ac2106f6c3957e5687ebc3121fcf0b30bab12f665e","abstract_canon_sha256":"5ef12d7b44089b9f1264fdfb9628c1854e97f6a74e762f4cb24e70401bedd603"},"schema_version":"1.0"},"canonical_sha256":"adf982b2e112ee253950452ef3e75d42f855e592df03cfed033dce0d3bef05eb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:21.392955Z","signature_b64":"KVZde8uMPLJheu4mBZK8palcREVAkpD+lzyfOk+rxglnmBooTZCwDlb4pLMx0LKW70BQ5CcHsw/rxg4QCRSUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adf982b2e112ee253950452ef3e75d42f855e592df03cfed033dce0d3bef05eb","last_reissued_at":"2026-07-05T11:58:21.392518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:21.392518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.21520","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:58:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9M3UYs15khzEzdPguQPy6KJqalrLKMOwzy1UYNVChFLsPoCz5Qqcn8rC12MkVyRnePDL4GQ1kAxTOOZKz/xkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T09:44:44.397137Z"},"content_sha256":"de884f06fd3d9a42f2ebab7bafe776a1619988d6efe6e37b56d94a533636eb3a","schema_version":"1.0","event_id":"sha256:de884f06fd3d9a42f2ebab7bafe776a1619988d6efe6e37b56d94a533636eb3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VX4YFMXBCLXCKOKQIUXPHZ25IL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Curtiss B. Cook, Jiaru Zou, Jingrui He, Tianxin Wei, Xinrui He, Yikun Ban","submitted_at":"2024-10-28T20:42:46Z","abstract_excerpt":"Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation, making them a promising tool for data imputation. However, challenges persist in designing effective prompts for a finetuning-free process and in mitigating biases and uncertainty in LLM outputs. To address these issues, we propose a novel framework, LLM-Forest, which introduces a \"forest\" of few-shot prompt learning LLM \"trees\" with their"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.21520","kind":"arxiv","version":4},"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/2410.21520/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":"2026-07-05T11:58:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sg1EMVLlHMrmN2hdDcn0tOnBtMfxwPuYYrwBgDTjZ2G6hwENI0aCfG5nRLuLVfd9mYdg2cQ1z1PDzVhJSy1TCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T09:44:44.397645Z"},"content_sha256":"54ce9620b9159c0a9d0d7628bb6a2d884a6eac5cba4284105eeb15964605aaab","schema_version":"1.0","event_id":"sha256:54ce9620b9159c0a9d0d7628bb6a2d884a6eac5cba4284105eeb15964605aaab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/bundle.json","state_url":"https://pith.science/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/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":"2026-08-12T09:44:44Z","links":{"resolver":"https://pith.science/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL","bundle":"https://pith.science/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/bundle.json","state":"https://pith.science/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VX4YFMXBCLXCKOKQIUXPHZ25IL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VX4YFMXBCLXCKOKQIUXPHZ25IL","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":"5ef12d7b44089b9f1264fdfb9628c1854e97f6a74e762f4cb24e70401bedd603","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-28T20:42:46Z","title_canon_sha256":"69a564882375fcc19d8508ac2106f6c3957e5687ebc3121fcf0b30bab12f665e"},"schema_version":"1.0","source":{"id":"2410.21520","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.21520","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"arxiv_version","alias_value":"2410.21520v4","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.21520","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_12","alias_value":"VX4YFMXBCLXC","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_16","alias_value":"VX4YFMXBCLXCKOKQ","created_at":"2026-07-05T11:58:21Z"},{"alias_kind":"pith_short_8","alias_value":"VX4YFMXB","created_at":"2026-07-05T11:58:21Z"}],"graph_snapshots":[{"event_id":"sha256:54ce9620b9159c0a9d0d7628bb6a2d884a6eac5cba4284105eeb15964605aaab","target":"graph","created_at":"2026-07-05T11:58:21Z","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/2410.21520/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation, making them a promising tool for data imputation. However, challenges persist in designing effective prompts for a finetuning-free process and in mitigating biases and uncertainty in LLM outputs. To address these issues, we propose a novel framework, LLM-Forest, which introduces a \"forest\" of few-shot prompt learning LLM \"trees\" with their","authors_text":"Curtiss B. Cook, Jiaru Zou, Jingrui He, Tianxin Wei, Xinrui He, Yikun Ban","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-28T20:42:46Z","title":"LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.21520","kind":"arxiv","version":4},"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:de884f06fd3d9a42f2ebab7bafe776a1619988d6efe6e37b56d94a533636eb3a","target":"record","created_at":"2026-07-05T11:58:21Z","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":"5ef12d7b44089b9f1264fdfb9628c1854e97f6a74e762f4cb24e70401bedd603","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-28T20:42:46Z","title_canon_sha256":"69a564882375fcc19d8508ac2106f6c3957e5687ebc3121fcf0b30bab12f665e"},"schema_version":"1.0","source":{"id":"2410.21520","kind":"arxiv","version":4}},"canonical_sha256":"adf982b2e112ee253950452ef3e75d42f855e592df03cfed033dce0d3bef05eb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adf982b2e112ee253950452ef3e75d42f855e592df03cfed033dce0d3bef05eb","first_computed_at":"2026-07-05T11:58:21.392518Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:21.392518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KVZde8uMPLJheu4mBZK8palcREVAkpD+lzyfOk+rxglnmBooTZCwDlb4pLMx0LKW70BQ5CcHsw/rxg4QCRSUAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:21.392955Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.21520","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de884f06fd3d9a42f2ebab7bafe776a1619988d6efe6e37b56d94a533636eb3a","sha256:54ce9620b9159c0a9d0d7628bb6a2d884a6eac5cba4284105eeb15964605aaab"],"state_sha256":"b0a2e72825fe05ba8197b9c9367fc7073f31d044306c7dbd6f0aa9679ff5e23d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dq5R+YIS9w32VRak+Jl4p2pIks67w+8MpdT84z1yJRkPgc1Ocq+p5nQdVeICDWewARapLPJ8edorluYnuL8gAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T09:44:44.401334Z","bundle_sha256":"6a2bf315493e697bf3f23b72fcc0e51447abd1b356b310e67911f1952b8f6f28"}}