{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LLASL6GNYEVSGGKD63HCCDD43F","short_pith_number":"pith:LLASL6GN","schema_version":"1.0","canonical_sha256":"5ac125f8cdc12b231943f6ce210c7cd9640a8cb971bc2703b46d73c797ef6705","source":{"kind":"arxiv","id":"2608.09880","version":1},"attestation_state":"computed","paper":{"title":"Financial Numerical Prediction and Allocation as Token Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Moontae Lee, Xu Ouyang","submitted_at":"2026-08-10T17:33:10Z","abstract_excerpt":"Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ultimately evaluated. We investigate whether a causal language model can instead represent forecasts and decisions directly through constrained token generation. FinATOM introduces a unified, head-free interface for three-step stock-return forecasting and dynamic five-ETF allocation. The forecasting model autoregressively emits volatility-standardized return tokens and is trained with ordinal and ranking supervision followed by a one-epoch token-l"},"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":"2608.09880","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-10T17:33:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"262f34421419a590140826ac2162c86596ccd4e15f06003c2212fb3dea6dc41f","abstract_canon_sha256":"67c75814cf5fbb11764b4db30f8f1fa6d606babd293d178dd9bbafecda2ec836"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T02:25:19.513117Z","signature_b64":"fTkUH66HpRMopq97pvey/0BBs4W2x6qMiH1f7CWcIKVVj2+m7zy9eNRG5HdLq11bfsbR53sikVPS1+4m4+oYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ac125f8cdc12b231943f6ce210c7cd9640a8cb971bc2703b46d73c797ef6705","last_reissued_at":"2026-08-11T02:25:19.511605Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T02:25:19.511605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Financial Numerical Prediction and Allocation as Token Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Moontae Lee, Xu Ouyang","submitted_at":"2026-08-10T17:33:10Z","abstract_excerpt":"Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ultimately evaluated. We investigate whether a causal language model can instead represent forecasts and decisions directly through constrained token generation. FinATOM introduces a unified, head-free interface for three-step stock-return forecasting and dynamic five-ETF allocation. The forecasting model autoregressively emits volatility-standardized return tokens and is trained with ordinal and ranking supervision followed by a one-epoch token-l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09880","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/2608.09880/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":"2608.09880","created_at":"2026-08-11T02:25:19.512210+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.09880v1","created_at":"2026-08-11T02:25:19.512210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09880","created_at":"2026-08-11T02:25:19.512210+00:00"},{"alias_kind":"pith_short_12","alias_value":"LLASL6GNYEVS","created_at":"2026-08-11T02:25:19.512210+00:00"},{"alias_kind":"pith_short_16","alias_value":"LLASL6GNYEVSGGKD","created_at":"2026-08-11T02:25:19.512210+00:00"},{"alias_kind":"pith_short_8","alias_value":"LLASL6GN","created_at":"2026-08-11T02:25:19.512210+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F","json":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F.json","graph_json":"https://pith.science/api/pith-number/LLASL6GNYEVSGGKD63HCCDD43F/graph.json","events_json":"https://pith.science/api/pith-number/LLASL6GNYEVSGGKD63HCCDD43F/events.json","paper":"https://pith.science/paper/LLASL6GN"},"agent_actions":{"view_html":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F","download_json":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F.json","view_paper":"https://pith.science/paper/LLASL6GN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.09880&json=true","fetch_graph":"https://pith.science/api/pith-number/LLASL6GNYEVSGGKD63HCCDD43F/graph.json","fetch_events":"https://pith.science/api/pith-number/LLASL6GNYEVSGGKD63HCCDD43F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F/action/storage_attestation","attest_author":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F/action/author_attestation","sign_citation":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F/action/citation_signature","submit_replication":"https://pith.science/pith/LLASL6GNYEVSGGKD63HCCDD43F/action/replication_record"}},"created_at":"2026-08-11T02:25:19.512210+00:00","updated_at":"2026-08-11T02:25:19.512210+00:00"}