{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HMWRQUXVGV7F5CA6B3YTG2HEKR","short_pith_number":"pith:HMWRQUXV","canonical_record":{"source":{"id":"2606.18875","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-17T09:52:39Z","cross_cats_sorted":[],"title_canon_sha256":"94dfab9a7604e446fa0e1a2d502b5c33612ba6a36333202868591b814b76f185","abstract_canon_sha256":"ba95671c48776dcfb2b27559f93769a2daae9e829facb018457ad707953180c6"},"schema_version":"1.0"},"canonical_sha256":"3b2d1852f5357e5e881e0ef13368e45469b0a2b5323779d212f311ab38858231","source":{"kind":"arxiv","id":"2606.18875","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18875","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18875v1","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18875","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_12","alias_value":"HMWRQUXVGV7F","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_16","alias_value":"HMWRQUXVGV7F5CA6","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_8","alias_value":"HMWRQUXV","created_at":"2026-06-19T16:11:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HMWRQUXVGV7F5CA6B3YTG2HEKR","target":"record","payload":{"canonical_record":{"source":{"id":"2606.18875","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-17T09:52:39Z","cross_cats_sorted":[],"title_canon_sha256":"94dfab9a7604e446fa0e1a2d502b5c33612ba6a36333202868591b814b76f185","abstract_canon_sha256":"ba95671c48776dcfb2b27559f93769a2daae9e829facb018457ad707953180c6"},"schema_version":"1.0"},"canonical_sha256":"3b2d1852f5357e5e881e0ef13368e45469b0a2b5323779d212f311ab38858231","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:50.639205Z","signature_b64":"x6GekznonbBQaIymFofb/qHZtAgKed3eE4nKdBJf8ecPhksWZrjZSfahXbjvw5jF1tspJsOgsC23bHbGEjmvCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b2d1852f5357e5e881e0ef13368e45469b0a2b5323779d212f311ab38858231","last_reissued_at":"2026-06-19T16:11:50.638826Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:50.638826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.18875","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":"2026-06-19T16:11:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aARad31NeYKJOfQazyN6gFRNGRb24z7bwikOEllxvXWIHInndybGiTOG84bPAyz7pcuishxV4/SId2N2YpSTCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T05:27:36.887849Z"},"content_sha256":"4377d968c5e692c898bd05a52cfeee5e04851df5e360a5931dd85562d9578474","schema_version":"1.0","event_id":"sha256:4377d968c5e692c898bd05a52cfeee5e04851df5e360a5931dd85562d9578474"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HMWRQUXVGV7F5CA6B3YTG2HEKR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Financial Language Understanding via Distillation with Synthetic Data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Edwin Simpson, Wen-Fong (Xavier) Huang","submitted_at":"2026-06-17T09:52:39Z","abstract_excerpt":"Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis through distillation with synthetic data, transferring knowledge from a large instruction-tuned teacher to compact student models. The framework is designed for low-resource conditions, where a small set of real examples are collected and labelled by hand. The framework then clusters the examples and uses the clusters to select seeds for generating syntheti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18875","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/2606.18875/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":"2026-06-19T16:11:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LAjr4NUhXrVDBMeRf5zyiRc4tBi/4oBwGvdO6k/D2ZXXhbCx8QfWjAoF3sMiKftYm1wDsaWaPUlEei4Mlx7xDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T05:27:36.888209Z"},"content_sha256":"be63a4a540c967ddabbdff6a2c87f9161ed49849d3cbd770031ebca711162b17","schema_version":"1.0","event_id":"sha256:be63a4a540c967ddabbdff6a2c87f9161ed49849d3cbd770031ebca711162b17"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/bundle.json","state_url":"https://pith.science/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/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":"2026-07-04T05:27:36Z","links":{"resolver":"https://pith.science/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR","bundle":"https://pith.science/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/bundle.json","state":"https://pith.science/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HMWRQUXVGV7F5CA6B3YTG2HEKR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HMWRQUXVGV7F5CA6B3YTG2HEKR","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":"ba95671c48776dcfb2b27559f93769a2daae9e829facb018457ad707953180c6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-17T09:52:39Z","title_canon_sha256":"94dfab9a7604e446fa0e1a2d502b5c33612ba6a36333202868591b814b76f185"},"schema_version":"1.0","source":{"id":"2606.18875","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18875","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18875v1","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18875","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_12","alias_value":"HMWRQUXVGV7F","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_16","alias_value":"HMWRQUXVGV7F5CA6","created_at":"2026-06-19T16:11:50Z"},{"alias_kind":"pith_short_8","alias_value":"HMWRQUXV","created_at":"2026-06-19T16:11:50Z"}],"graph_snapshots":[{"event_id":"sha256:be63a4a540c967ddabbdff6a2c87f9161ed49849d3cbd770031ebca711162b17","target":"graph","created_at":"2026-06-19T16:11:50Z","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/2606.18875/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis through distillation with synthetic data, transferring knowledge from a large instruction-tuned teacher to compact student models. The framework is designed for low-resource conditions, where a small set of real examples are collected and labelled by hand. The framework then clusters the examples and uses the clusters to select seeds for generating syntheti","authors_text":"Edwin Simpson, Wen-Fong (Xavier) Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-17T09:52:39Z","title":"Efficient Financial Language Understanding via Distillation with Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18875","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":"sha256:4377d968c5e692c898bd05a52cfeee5e04851df5e360a5931dd85562d9578474","target":"record","created_at":"2026-06-19T16:11:50Z","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":"ba95671c48776dcfb2b27559f93769a2daae9e829facb018457ad707953180c6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-17T09:52:39Z","title_canon_sha256":"94dfab9a7604e446fa0e1a2d502b5c33612ba6a36333202868591b814b76f185"},"schema_version":"1.0","source":{"id":"2606.18875","kind":"arxiv","version":1}},"canonical_sha256":"3b2d1852f5357e5e881e0ef13368e45469b0a2b5323779d212f311ab38858231","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3b2d1852f5357e5e881e0ef13368e45469b0a2b5323779d212f311ab38858231","first_computed_at":"2026-06-19T16:11:50.638826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:11:50.638826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x6GekznonbBQaIymFofb/qHZtAgKed3eE4nKdBJf8ecPhksWZrjZSfahXbjvw5jF1tspJsOgsC23bHbGEjmvCQ==","signature_status":"signed_v1","signed_at":"2026-06-19T16:11:50.639205Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.18875","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4377d968c5e692c898bd05a52cfeee5e04851df5e360a5931dd85562d9578474","sha256:be63a4a540c967ddabbdff6a2c87f9161ed49849d3cbd770031ebca711162b17"],"state_sha256":"fd2e3202d77e250db9ced05be2ba09571f1891c81bd91826c11bcd72f992cdee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EtFsrVXy4Ds5/7cnqoKVive9TAPK8kkcVsutnzCYqanytfbYn4SzvWqRiAIlisyXBoTFrolufNuagMEAlDd3CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T05:27:36.890134Z","bundle_sha256":"f792301a2f13ef08ce3d658710b8a388e4ff4c997b311ffc7abfbcbe47e2fc19"}}