{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EJ3R6BNKYFFIP5FNTWQ355CX65","short_pith_number":"pith:EJ3R6BNK","schema_version":"1.0","canonical_sha256":"22771f05aac14a87f4ad9da1bef457f7637729993d3654f9789d401f41649b24","source":{"kind":"arxiv","id":"2310.08863","version":1},"attestation_state":"computed","paper":{"title":"In-Context Learning for Few-Shot Molecular Property Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christopher Fifty, Jure Leskovec, Sebastian Thrun","submitted_at":"2023-10-13T05:12:48Z","abstract_excerpt":"In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On t"},"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":"2310.08863","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T05:12:48Z","cross_cats_sorted":[],"title_canon_sha256":"a8d2828fc7c52c54a149d57ad0a2904a847f462e8db6964f76f28c07a4ee5091","abstract_canon_sha256":"0cbdc85552965406fbbf5944dabe8de825757a93086593bc582ae135cbdc5c0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:36.056048Z","signature_b64":"qnNPXI7lgSfByKAFdF02RtGno0vG5BwSEc8wj6pCM3qb0ifM9rKa8Th3l47R4Px+OXD4xV4diZ3Ubcrwz6XMCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22771f05aac14a87f4ad9da1bef457f7637729993d3654f9789d401f41649b24","last_reissued_at":"2026-07-05T07:00:36.055593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:36.055593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In-Context Learning for Few-Shot Molecular Property Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christopher Fifty, Jure Leskovec, Sebastian Thrun","submitted_at":"2023-10-13T05:12:48Z","abstract_excerpt":"In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08863","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/2310.08863/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":"2310.08863","created_at":"2026-07-05T07:00:36.055648+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08863v1","created_at":"2026-07-05T07:00:36.055648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08863","created_at":"2026-07-05T07:00:36.055648+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJ3R6BNKYFFI","created_at":"2026-07-05T07:00:36.055648+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJ3R6BNKYFFIP5FN","created_at":"2026-07-05T07:00:36.055648+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJ3R6BNK","created_at":"2026-07-05T07:00:36.055648+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05846","citing_title":"AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking","ref_index":35,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13024","citing_title":"ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65","json":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65.json","graph_json":"https://pith.science/api/pith-number/EJ3R6BNKYFFIP5FNTWQ355CX65/graph.json","events_json":"https://pith.science/api/pith-number/EJ3R6BNKYFFIP5FNTWQ355CX65/events.json","paper":"https://pith.science/paper/EJ3R6BNK"},"agent_actions":{"view_html":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65","download_json":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65.json","view_paper":"https://pith.science/paper/EJ3R6BNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08863&json=true","fetch_graph":"https://pith.science/api/pith-number/EJ3R6BNKYFFIP5FNTWQ355CX65/graph.json","fetch_events":"https://pith.science/api/pith-number/EJ3R6BNKYFFIP5FNTWQ355CX65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65/action/storage_attestation","attest_author":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65/action/author_attestation","sign_citation":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65/action/citation_signature","submit_replication":"https://pith.science/pith/EJ3R6BNKYFFIP5FNTWQ355CX65/action/replication_record"}},"created_at":"2026-07-05T07:00:36.055648+00:00","updated_at":"2026-07-05T07:00:36.055648+00:00"}