{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RKOHABA4YMV3743VAUQEAO372A","short_pith_number":"pith:RKOHABA4","canonical_record":{"source":{"id":"2605.24564","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T12:57:18Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"dd6c8a97c01450bd1fccd3ee24e87210ef3244c66b8da5ba3bc101d9665c1051","abstract_canon_sha256":"8bc3b09872b54171750eb8ed46d5f52570442dd36fea99fe2948a15531fe4e83"},"schema_version":"1.0"},"canonical_sha256":"8a9c70041cc32bbff3750520403b7fd02aada97c17509802a90b57dc0f32a55e","source":{"kind":"arxiv","id":"2605.24564","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24564","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24564v1","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24564","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"RKOHABA4YMV3","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"RKOHABA4YMV3743V","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"RKOHABA4","created_at":"2026-05-26T01:03:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RKOHABA4YMV3743VAUQEAO372A","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24564","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T12:57:18Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"dd6c8a97c01450bd1fccd3ee24e87210ef3244c66b8da5ba3bc101d9665c1051","abstract_canon_sha256":"8bc3b09872b54171750eb8ed46d5f52570442dd36fea99fe2948a15531fe4e83"},"schema_version":"1.0"},"canonical_sha256":"8a9c70041cc32bbff3750520403b7fd02aada97c17509802a90b57dc0f32a55e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:46.535007Z","signature_b64":"1ddlr05P2eSE7DH0Tt9cZiLg7q6b+du0HlmNCujHBgAxYXuKJALDWHYeq2ztfufCBJsMAVdN3eQ2b8YQPfjWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a9c70041cc32bbff3750520403b7fd02aada97c17509802a90b57dc0f32a55e","last_reissued_at":"2026-05-26T01:03:46.534153Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:46.534153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24564","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":"2026-05-26T01:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8z15gjAo2BZMsWt9W+fL2MRE9N/qmQy1w5uYW5wQOAKraivK1ruCdjJ1Iy6vHA8SpHQXb4OqdlwbfxGyYHYVBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T06:39:21.058869Z"},"content_sha256":"262ee6f11299a8e9140c15bb2cc6af0896225344d19470ca20c08449a3744e5a","schema_version":"1.0","event_id":"sha256:262ee6f11299a8e9140c15bb2cc6af0896225344d19470ca20c08449a3744e5a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RKOHABA4YMV3743VAUQEAO372A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"cs.AI","authors_text":"Mengyu Wang, Tiejun Ma, Weixian Waylon Li","submitted_at":"2026-05-23T12:57:18Z","abstract_excerpt":"Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trained in 2024 already \"knows\" which way 2018-2020 stocks moved. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that suppresses an LLM's memory of historical outcomes without retraining. FinCAD pairs an adversarial bias-discovery pipeline that learns a model-specific memory-activating prior prompt with an entity- and date-adaptive rule that scales the CAD strength to per-("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24564","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/2605.24564/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":"2026-05-26T01:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JtCNsIqHy6Yg48q6ri23Xb0V6osgL0vJ8s2ZA1UNvOwAmpNPQ7y4WDF6LgZZlbSC8z8Oe7Cpg7FTtXrhRZPQBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T06:39:21.059245Z"},"content_sha256":"4447b47c07dbf687d230acd5a0fa41037e6b923a94e3db9fbdbc00332026f8f9","schema_version":"1.0","event_id":"sha256:4447b47c07dbf687d230acd5a0fa41037e6b923a94e3db9fbdbc00332026f8f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RKOHABA4YMV3743VAUQEAO372A/bundle.json","state_url":"https://pith.science/pith/RKOHABA4YMV3743VAUQEAO372A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RKOHABA4YMV3743VAUQEAO372A/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":"2026-06-03T06:39:21Z","links":{"resolver":"https://pith.science/pith/RKOHABA4YMV3743VAUQEAO372A","bundle":"https://pith.science/pith/RKOHABA4YMV3743VAUQEAO372A/bundle.json","state":"https://pith.science/pith/RKOHABA4YMV3743VAUQEAO372A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RKOHABA4YMV3743VAUQEAO372A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RKOHABA4YMV3743VAUQEAO372A","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":"8bc3b09872b54171750eb8ed46d5f52570442dd36fea99fe2948a15531fe4e83","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T12:57:18Z","title_canon_sha256":"dd6c8a97c01450bd1fccd3ee24e87210ef3244c66b8da5ba3bc101d9665c1051"},"schema_version":"1.0","source":{"id":"2605.24564","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24564","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24564v1","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24564","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"RKOHABA4YMV3","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"RKOHABA4YMV3743V","created_at":"2026-05-26T01:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"RKOHABA4","created_at":"2026-05-26T01:03:46Z"}],"graph_snapshots":[{"event_id":"sha256:4447b47c07dbf687d230acd5a0fa41037e6b923a94e3db9fbdbc00332026f8f9","target":"graph","created_at":"2026-05-26T01:03:46Z","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/2605.24564/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trained in 2024 already \"knows\" which way 2018-2020 stocks moved. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that suppresses an LLM's memory of historical outcomes without retraining. FinCAD pairs an adversarial bias-discovery pipeline that learns a model-specific memory-activating prior prompt with an entity- and date-adaptive rule that scales the CAD strength to per-(","authors_text":"Mengyu Wang, Tiejun Ma, Weixian Waylon Li","cross_cats":["cs.CE","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T12:57:18Z","title":"Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24564","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":"sha256:262ee6f11299a8e9140c15bb2cc6af0896225344d19470ca20c08449a3744e5a","target":"record","created_at":"2026-05-26T01:03:46Z","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":"8bc3b09872b54171750eb8ed46d5f52570442dd36fea99fe2948a15531fe4e83","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T12:57:18Z","title_canon_sha256":"dd6c8a97c01450bd1fccd3ee24e87210ef3244c66b8da5ba3bc101d9665c1051"},"schema_version":"1.0","source":{"id":"2605.24564","kind":"arxiv","version":1}},"canonical_sha256":"8a9c70041cc32bbff3750520403b7fd02aada97c17509802a90b57dc0f32a55e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a9c70041cc32bbff3750520403b7fd02aada97c17509802a90b57dc0f32a55e","first_computed_at":"2026-05-26T01:03:46.534153Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:03:46.534153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1ddlr05P2eSE7DH0Tt9cZiLg7q6b+du0HlmNCujHBgAxYXuKJALDWHYeq2ztfufCBJsMAVdN3eQ2b8YQPfjWBg==","signature_status":"signed_v1","signed_at":"2026-05-26T01:03:46.535007Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24564","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:262ee6f11299a8e9140c15bb2cc6af0896225344d19470ca20c08449a3744e5a","sha256:4447b47c07dbf687d230acd5a0fa41037e6b923a94e3db9fbdbc00332026f8f9"],"state_sha256":"ec834889bdeafc08cf1225ac592607546d468918c29476f70a0041f08589d5d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EeBEY+wvf5kO8P9rAyBEliv38FhHRzSaeqyvjmjL/VqCCM0Ci1L1P2JQWVJcWOyLogQt4aGRkpRpgw2igpO/Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T06:39:21.061348Z","bundle_sha256":"1d6cb18f2e18d650735cf92db86117a7f07923493f9d7e59325db4ba486fe92a"}}