{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:E3ONJNHAHF36DXKBC5JG47QHNQ","short_pith_number":"pith:E3ONJNHA","canonical_record":{"source":{"id":"2607.18467","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T19:37:35Z","cross_cats_sorted":[],"title_canon_sha256":"dc2b15a2d097aaa01355545cc18c78ec6aab9924c64c03d3c00b76c1e8417256","abstract_canon_sha256":"7f57a92b44dd6e9e3473215738a9acb93953ba20953023ddb2902c33f470fdee"},"schema_version":"1.0"},"canonical_sha256":"26dcd4b4e03977e1dd4117526e7e076c1e14e034eba1011af6c5764c8b38c51a","source":{"kind":"arxiv","id":"2607.18467","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.18467","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.18467v1","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18467","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_12","alias_value":"E3ONJNHAHF36","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_16","alias_value":"E3ONJNHAHF36DXKB","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_8","alias_value":"E3ONJNHA","created_at":"2026-07-22T00:22:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:E3ONJNHAHF36DXKBC5JG47QHNQ","target":"record","payload":{"canonical_record":{"source":{"id":"2607.18467","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T19:37:35Z","cross_cats_sorted":[],"title_canon_sha256":"dc2b15a2d097aaa01355545cc18c78ec6aab9924c64c03d3c00b76c1e8417256","abstract_canon_sha256":"7f57a92b44dd6e9e3473215738a9acb93953ba20953023ddb2902c33f470fdee"},"schema_version":"1.0"},"canonical_sha256":"26dcd4b4e03977e1dd4117526e7e076c1e14e034eba1011af6c5764c8b38c51a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:50.057898Z","signature_b64":"O7T/lxCQHIbHAKETLd9JD9oTlE+TS9IhyvLsIcqZ2oe8QD3535okZLOPPKdnRUO0dijEYDowD3g0DAUl24aWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26dcd4b4e03977e1dd4117526e7e076c1e14e034eba1011af6c5764c8b38c51a","last_reissued_at":"2026-07-22T00:22:50.057053Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:50.057053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.18467","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":"2026-07-22T00:22:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R8mE/zU82QDEZOwFGvc+VVAE6U3IV910Syi7trXby3CxmgjPEQKIzFPfNuiPe2A/uM93W1J4diOeFbdWg1asDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:13:25.628001Z"},"content_sha256":"ab65c2f82ec63c6e519a123aa25a07fd58bf47cc04a1fd0a2bdb6c93b050663f","schema_version":"1.0","event_id":"sha256:ab65c2f82ec63c6e519a123aa25a07fd58bf47cc04a1fd0a2bdb6c93b050663f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:E3ONJNHAHF36DXKBC5JG47QHNQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weak-to-Strong Learning in Decision Making","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jingwei Ji, Renyuan Xu","submitted_at":"2026-07-20T19:37:35Z","abstract_excerpt":"Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distribution"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18467","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/2607.18467/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":"2026-07-22T00:22:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iS8k8G+3oyAhZhMaqZaRLn8+UJ5OitKKYpp0dYcSE8JQ5Dr25p8z2ikTYyXbcMIC6odxMtL9K7FJ9R34EDF8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:13:25.628382Z"},"content_sha256":"8c478de841970e68cd511490109cae5cc11819e465931a7ac652238c4773c689","schema_version":"1.0","event_id":"sha256:8c478de841970e68cd511490109cae5cc11819e465931a7ac652238c4773c689"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/bundle.json","state_url":"https://pith.science/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/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":"2026-08-03T17:13:25Z","links":{"resolver":"https://pith.science/pith/E3ONJNHAHF36DXKBC5JG47QHNQ","bundle":"https://pith.science/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/bundle.json","state":"https://pith.science/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E3ONJNHAHF36DXKBC5JG47QHNQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:E3ONJNHAHF36DXKBC5JG47QHNQ","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":"7f57a92b44dd6e9e3473215738a9acb93953ba20953023ddb2902c33f470fdee","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T19:37:35Z","title_canon_sha256":"dc2b15a2d097aaa01355545cc18c78ec6aab9924c64c03d3c00b76c1e8417256"},"schema_version":"1.0","source":{"id":"2607.18467","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.18467","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"arxiv_version","alias_value":"2607.18467v1","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18467","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_12","alias_value":"E3ONJNHAHF36","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_16","alias_value":"E3ONJNHAHF36DXKB","created_at":"2026-07-22T00:22:50Z"},{"alias_kind":"pith_short_8","alias_value":"E3ONJNHA","created_at":"2026-07-22T00:22:50Z"}],"graph_snapshots":[{"event_id":"sha256:8c478de841970e68cd511490109cae5cc11819e465931a7ac652238c4773c689","target":"graph","created_at":"2026-07-22T00:22:50Z","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/2607.18467/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distribution","authors_text":"Jingwei Ji, Renyuan Xu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T19:37:35Z","title":"Weak-to-Strong Learning in Decision Making"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18467","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:ab65c2f82ec63c6e519a123aa25a07fd58bf47cc04a1fd0a2bdb6c93b050663f","target":"record","created_at":"2026-07-22T00:22:50Z","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":"7f57a92b44dd6e9e3473215738a9acb93953ba20953023ddb2902c33f470fdee","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T19:37:35Z","title_canon_sha256":"dc2b15a2d097aaa01355545cc18c78ec6aab9924c64c03d3c00b76c1e8417256"},"schema_version":"1.0","source":{"id":"2607.18467","kind":"arxiv","version":1}},"canonical_sha256":"26dcd4b4e03977e1dd4117526e7e076c1e14e034eba1011af6c5764c8b38c51a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26dcd4b4e03977e1dd4117526e7e076c1e14e034eba1011af6c5764c8b38c51a","first_computed_at":"2026-07-22T00:22:50.057053Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-22T00:22:50.057053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O7T/lxCQHIbHAKETLd9JD9oTlE+TS9IhyvLsIcqZ2oe8QD3535okZLOPPKdnRUO0dijEYDowD3g0DAUl24aWBA==","signature_status":"signed_v1","signed_at":"2026-07-22T00:22:50.057898Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.18467","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab65c2f82ec63c6e519a123aa25a07fd58bf47cc04a1fd0a2bdb6c93b050663f","sha256:8c478de841970e68cd511490109cae5cc11819e465931a7ac652238c4773c689"],"state_sha256":"0d5e585469f33ae3d57c3f002461369bae5a289a6030e2d0d6a0e8a3a148ab7a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"My+tCCyLeKrtrrSEQ3uIJ1IvYWsZI/5MH5qXEfDGnCfxMZMSx4trHLFO4zgI/PFIqJow3+q7bH4aV3JTII1RCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:13:25.630959Z","bundle_sha256":"8388dcf03c4d581592a2668e7b8c36c99e13b79957ad018d3b85c62bfd3d30a7"}}