{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IQUNGTFPHATHJT5IYZBAORFCDV","short_pith_number":"pith:IQUNGTFP","canonical_record":{"source":{"id":"2509.09229","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T08:06:02Z","cross_cats_sorted":[],"title_canon_sha256":"557dd270f02073baa8ff7244973f0fd2ed0350abda85f0d959d1e035ed0f1897","abstract_canon_sha256":"791e8270e4fc697ad155a38290b89c5c4e301cd1256c7d4ae4997cc99caf88b9"},"schema_version":"1.0"},"canonical_sha256":"4428d34caf382674cfa8c6420744a21d66712a04547bebfd3c7a5c6a1a5c64b5","source":{"kind":"arxiv","id":"2509.09229","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09229","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09229v1","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09229","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_12","alias_value":"IQUNGTFPHATH","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_16","alias_value":"IQUNGTFPHATHJT5I","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_8","alias_value":"IQUNGTFP","created_at":"2026-07-05T12:09:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IQUNGTFPHATHJT5IYZBAORFCDV","target":"record","payload":{"canonical_record":{"source":{"id":"2509.09229","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T08:06:02Z","cross_cats_sorted":[],"title_canon_sha256":"557dd270f02073baa8ff7244973f0fd2ed0350abda85f0d959d1e035ed0f1897","abstract_canon_sha256":"791e8270e4fc697ad155a38290b89c5c4e301cd1256c7d4ae4997cc99caf88b9"},"schema_version":"1.0"},"canonical_sha256":"4428d34caf382674cfa8c6420744a21d66712a04547bebfd3c7a5c6a1a5c64b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:31.887968Z","signature_b64":"cSxFXwZThMsZn1FxAL7lkHbbscjacuCRoTxjeepQcukMldWuwr6z92MUso/2e9RkLa6g6ZWghen1tZ2Z2zvECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4428d34caf382674cfa8c6420744a21d66712a04547bebfd3c7a5c6a1a5c64b5","last_reissued_at":"2026-07-05T12:09:31.887451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:31.887451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.09229","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":"2026-07-05T12:09:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QO/KSE6Qz2kNM71n3S86Y69Sf4nBUlwvBpBksKLyeaI3yhRKzx4dlAr4S3uH2XOSVLthLFSXmkpqU7+6lU4VCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:41:46.832125Z"},"content_sha256":"d9682201ed3274f03b9db222766a911ad042f1bc3a1c2b6394d04edf0341c278","schema_version":"1.0","event_id":"sha256:d9682201ed3274f03b9db222766a911ad042f1bc3a1c2b6394d04edf0341c278"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IQUNGTFPHATHJT5IYZBAORFCDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reading Between the Lines: Classifying Resume Seniority with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Apartsin, Eden Menahem, Matan Cohen, Shira Shani, Yehudit Aperstein","submitted_at":"2025-09-11T08:06:02Z","abstract_excerpt":"Accurately assessing candidate seniority from resumes is a critical yet challenging task, complicated by the prevalence of overstated experience and ambiguous self-presentation. In this study, we investigate the effectiveness of large language models (LLMs), including fine-tuned BERT architectures, for automating seniority classification in resumes. To rigorously evaluate model performance, we introduce a hybrid dataset comprising both real-world resumes and synthetically generated hard examples designed to simulate exaggerated qualifications and understated seniority. Using the dataset, we ev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09229","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/2509.09229/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":"2026-07-05T12:09:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qx5l6foIajELISZHMVEu40AT813Ymohbni0bZ5JsQ/XD/s4AQiFshni+yhqKIUigPq7Y9t7b7wFgBTNvhI+lAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:41:46.832634Z"},"content_sha256":"9c560711aedcd67d76f9c586c6bb79234f129e2d66f9c0680a9a41eeef470e0c","schema_version":"1.0","event_id":"sha256:9c560711aedcd67d76f9c586c6bb79234f129e2d66f9c0680a9a41eeef470e0c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IQUNGTFPHATHJT5IYZBAORFCDV/bundle.json","state_url":"https://pith.science/pith/IQUNGTFPHATHJT5IYZBAORFCDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IQUNGTFPHATHJT5IYZBAORFCDV/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":"2026-08-05T21:41:46Z","links":{"resolver":"https://pith.science/pith/IQUNGTFPHATHJT5IYZBAORFCDV","bundle":"https://pith.science/pith/IQUNGTFPHATHJT5IYZBAORFCDV/bundle.json","state":"https://pith.science/pith/IQUNGTFPHATHJT5IYZBAORFCDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IQUNGTFPHATHJT5IYZBAORFCDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IQUNGTFPHATHJT5IYZBAORFCDV","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":"791e8270e4fc697ad155a38290b89c5c4e301cd1256c7d4ae4997cc99caf88b9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T08:06:02Z","title_canon_sha256":"557dd270f02073baa8ff7244973f0fd2ed0350abda85f0d959d1e035ed0f1897"},"schema_version":"1.0","source":{"id":"2509.09229","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.09229","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"arxiv_version","alias_value":"2509.09229v1","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09229","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_12","alias_value":"IQUNGTFPHATH","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_16","alias_value":"IQUNGTFPHATHJT5I","created_at":"2026-07-05T12:09:31Z"},{"alias_kind":"pith_short_8","alias_value":"IQUNGTFP","created_at":"2026-07-05T12:09:31Z"}],"graph_snapshots":[{"event_id":"sha256:9c560711aedcd67d76f9c586c6bb79234f129e2d66f9c0680a9a41eeef470e0c","target":"graph","created_at":"2026-07-05T12:09:31Z","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/2509.09229/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurately assessing candidate seniority from resumes is a critical yet challenging task, complicated by the prevalence of overstated experience and ambiguous self-presentation. In this study, we investigate the effectiveness of large language models (LLMs), including fine-tuned BERT architectures, for automating seniority classification in resumes. To rigorously evaluate model performance, we introduce a hybrid dataset comprising both real-world resumes and synthetically generated hard examples designed to simulate exaggerated qualifications and understated seniority. Using the dataset, we ev","authors_text":"Alexander Apartsin, Eden Menahem, Matan Cohen, Shira Shani, Yehudit Aperstein","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T08:06:02Z","title":"Reading Between the Lines: Classifying Resume Seniority with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09229","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:d9682201ed3274f03b9db222766a911ad042f1bc3a1c2b6394d04edf0341c278","target":"record","created_at":"2026-07-05T12:09:31Z","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":"791e8270e4fc697ad155a38290b89c5c4e301cd1256c7d4ae4997cc99caf88b9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-09-11T08:06:02Z","title_canon_sha256":"557dd270f02073baa8ff7244973f0fd2ed0350abda85f0d959d1e035ed0f1897"},"schema_version":"1.0","source":{"id":"2509.09229","kind":"arxiv","version":1}},"canonical_sha256":"4428d34caf382674cfa8c6420744a21d66712a04547bebfd3c7a5c6a1a5c64b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4428d34caf382674cfa8c6420744a21d66712a04547bebfd3c7a5c6a1a5c64b5","first_computed_at":"2026-07-05T12:09:31.887451Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:09:31.887451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cSxFXwZThMsZn1FxAL7lkHbbscjacuCRoTxjeepQcukMldWuwr6z92MUso/2e9RkLa6g6ZWghen1tZ2Z2zvECw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:09:31.887968Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.09229","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d9682201ed3274f03b9db222766a911ad042f1bc3a1c2b6394d04edf0341c278","sha256:9c560711aedcd67d76f9c586c6bb79234f129e2d66f9c0680a9a41eeef470e0c"],"state_sha256":"196e4adf1a915b1d9e5da7f60d5d83cb2f409758e9bd9b3e42d3d7dd2438565e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7QitaeO+hrGcCCbQ8nc3RzNzANdiysjhV8ByRU1Vl23Jhx/4qWd+yvrTVwut4AitoR0N0fCKXLVLP1PDNg8iCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T21:41:46.836023Z","bundle_sha256":"17531311de188a0cbdf42dacb7e1d3ef4dc40e703e366bce273ac826dd66045e"}}