{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4XN2YQWHY5TTO5MGZ4ISSNUWQR","short_pith_number":"pith:4XN2YQWH","canonical_record":{"source":{"id":"2607.14709","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-16T08:18:55Z","cross_cats_sorted":[],"title_canon_sha256":"fc1bf7bef694906175fef56d8f43f6fec52741d63e39e90d23f90639cccff967","abstract_canon_sha256":"6bb5edbe3a32d74582c7587c42952abc06137e534b2408dce6af0b41949eb81f"},"schema_version":"1.0"},"canonical_sha256":"e5dbac42c7c767377586cf112936968447181a7842239eea9ef658b36fe60400","source":{"kind":"arxiv","id":"2607.14709","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14709","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14709v1","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14709","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_12","alias_value":"4XN2YQWHY5TT","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_16","alias_value":"4XN2YQWHY5TTO5MG","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_8","alias_value":"4XN2YQWH","created_at":"2026-07-17T01:21:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4XN2YQWHY5TTO5MGZ4ISSNUWQR","target":"record","payload":{"canonical_record":{"source":{"id":"2607.14709","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-16T08:18:55Z","cross_cats_sorted":[],"title_canon_sha256":"fc1bf7bef694906175fef56d8f43f6fec52741d63e39e90d23f90639cccff967","abstract_canon_sha256":"6bb5edbe3a32d74582c7587c42952abc06137e534b2408dce6af0b41949eb81f"},"schema_version":"1.0"},"canonical_sha256":"e5dbac42c7c767377586cf112936968447181a7842239eea9ef658b36fe60400","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:21:26.852648Z","signature_b64":"Rn+EXg1s+44+8ZslRXyyJUYSpAubKxbCASVfiLsZffGvlCfKcRmQfyFP2qK3XQo6UAZOgTRq+NKyghpwmCFXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5dbac42c7c767377586cf112936968447181a7842239eea9ef658b36fe60400","last_reissued_at":"2026-07-17T01:21:26.851797Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:21:26.851797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.14709","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":"2026-07-17T01:21:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sT5n2dbU3Ph17/3D0JRQgDk46rXDKRAClRRQxO3arEDN728ViaNnHD3KOs5dU6AhGpsaIjKMyjd0ahQ1LzFuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:34:23.905936Z"},"content_sha256":"5529749ea6d91b6d674161426613ae6e99911baf0d72dae6afea6b97d656fe7c","schema_version":"1.0","event_id":"sha256:5529749ea6d91b6d674161426613ae6e99911baf0d72dae6afea6b97d656fe7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4XN2YQWHY5TTO5MGZ4ISSNUWQR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gold-Guided Programmatic Distillation for Financial Reasoning over Hybrid Tables and Text","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Elana Chen, Erica Zhao, Yun Dong","submitted_at":"2026-07-16T08:18:55Z","abstract_excerpt":"Financial question answering over hybrid tabular and textual data may require multi-source reasoning and precise numerical computation. While large language models (LLMs) can generate intermediate reasoning steps, natural-language rationales remain prone to arithmetic errors, making them an unreliable supervision source for distillation. Building on programmatic distillation, we develop an approach that transfers reliable numerical reasoning from a large teacher model to a compact student using execution-verified Python programs instead of free-form textual rationales. It leverages gold deriva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14709","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/2607.14709/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":"2026-07-17T01:21:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1+SSkulnX7vlEBtG8obXERos5SiDESRTx1l75NEkNOi9dCrE/v2ya2CMnYWsBw71vP0nN+NKv5R/k4u9VGROAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:34:23.906880Z"},"content_sha256":"afcec20df58681baef1c204be2959308e383743cb78f5a97ca715e29459a64a7","schema_version":"1.0","event_id":"sha256:afcec20df58681baef1c204be2959308e383743cb78f5a97ca715e29459a64a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/bundle.json","state_url":"https://pith.science/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/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":"2026-08-10T13:34:23Z","links":{"resolver":"https://pith.science/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR","bundle":"https://pith.science/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/bundle.json","state":"https://pith.science/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4XN2YQWHY5TTO5MGZ4ISSNUWQR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4XN2YQWHY5TTO5MGZ4ISSNUWQR","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":"6bb5edbe3a32d74582c7587c42952abc06137e534b2408dce6af0b41949eb81f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-16T08:18:55Z","title_canon_sha256":"fc1bf7bef694906175fef56d8f43f6fec52741d63e39e90d23f90639cccff967"},"schema_version":"1.0","source":{"id":"2607.14709","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14709","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14709v1","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14709","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_12","alias_value":"4XN2YQWHY5TT","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_16","alias_value":"4XN2YQWHY5TTO5MG","created_at":"2026-07-17T01:21:26Z"},{"alias_kind":"pith_short_8","alias_value":"4XN2YQWH","created_at":"2026-07-17T01:21:26Z"}],"graph_snapshots":[{"event_id":"sha256:afcec20df58681baef1c204be2959308e383743cb78f5a97ca715e29459a64a7","target":"graph","created_at":"2026-07-17T01:21:26Z","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/2607.14709/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Financial question answering over hybrid tabular and textual data may require multi-source reasoning and precise numerical computation. While large language models (LLMs) can generate intermediate reasoning steps, natural-language rationales remain prone to arithmetic errors, making them an unreliable supervision source for distillation. Building on programmatic distillation, we develop an approach that transfers reliable numerical reasoning from a large teacher model to a compact student using execution-verified Python programs instead of free-form textual rationales. It leverages gold deriva","authors_text":"Elana Chen, Erica Zhao, Yun Dong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-16T08:18:55Z","title":"Gold-Guided Programmatic Distillation for Financial Reasoning over Hybrid Tables and Text"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14709","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":"sha256:5529749ea6d91b6d674161426613ae6e99911baf0d72dae6afea6b97d656fe7c","target":"record","created_at":"2026-07-17T01:21:26Z","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":"6bb5edbe3a32d74582c7587c42952abc06137e534b2408dce6af0b41949eb81f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-16T08:18:55Z","title_canon_sha256":"fc1bf7bef694906175fef56d8f43f6fec52741d63e39e90d23f90639cccff967"},"schema_version":"1.0","source":{"id":"2607.14709","kind":"arxiv","version":1}},"canonical_sha256":"e5dbac42c7c767377586cf112936968447181a7842239eea9ef658b36fe60400","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e5dbac42c7c767377586cf112936968447181a7842239eea9ef658b36fe60400","first_computed_at":"2026-07-17T01:21:26.851797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-17T01:21:26.851797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rn+EXg1s+44+8ZslRXyyJUYSpAubKxbCASVfiLsZffGvlCfKcRmQfyFP2qK3XQo6UAZOgTRq+NKyghpwmCFXBw==","signature_status":"signed_v1","signed_at":"2026-07-17T01:21:26.852648Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.14709","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5529749ea6d91b6d674161426613ae6e99911baf0d72dae6afea6b97d656fe7c","sha256:afcec20df58681baef1c204be2959308e383743cb78f5a97ca715e29459a64a7"],"state_sha256":"f123fad963d3271c987f132b82ce4733c3ed95980e817fa29266e77d11fab81e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D1r5nHrUTYsW9EGD03w9X/rhsRSpZQI0e4eS+4yIn9XwYADtYBDYTetVtmwG4omBtTLZUEDVuNLOKWMfZ55FCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T13:34:23.912904Z","bundle_sha256":"43f1da0aaa7ee1c73257008271d769cdfad492673e353e24fc6245fda70444fc"}}