{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","short_pith_number":"pith:IX2IM5S4","canonical_record":{"source":{"id":"2607.10245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-11T10:20:03Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"369002c303eb13a8d1ead5ebdb54c20907968c5528e2c248bf22481e27b8205f","abstract_canon_sha256":"b3f4d743bd8b8f4e9bdf633a86d4a8ea74d8e6257779468c1181a06c2f293301"},"schema_version":"1.0"},"canonical_sha256":"45f486765c5b15d89a9e123d82b6bef8487a382583db849ad19fa497dd4e12a7","source":{"kind":"arxiv","id":"2607.10245","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.10245","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"arxiv_version","alias_value":"2607.10245v1","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10245","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_12","alias_value":"IX2IM5S4LMK5","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_16","alias_value":"IX2IM5S4LMK5RGU6","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_8","alias_value":"IX2IM5S4","created_at":"2026-07-14T01:20:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"record","payload":{"canonical_record":{"source":{"id":"2607.10245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-11T10:20:03Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"369002c303eb13a8d1ead5ebdb54c20907968c5528e2c248bf22481e27b8205f","abstract_canon_sha256":"b3f4d743bd8b8f4e9bdf633a86d4a8ea74d8e6257779468c1181a06c2f293301"},"schema_version":"1.0"},"canonical_sha256":"45f486765c5b15d89a9e123d82b6bef8487a382583db849ad19fa497dd4e12a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:32.596239Z","signature_b64":"VAu/Q9yvwj+GJJWqnaIgRfTBb3n++CxlMJH7UglKY6qUcTctUc/Pv/dSDUqGiOnqvBTiO5sgASlp1sUwtj73AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45f486765c5b15d89a9e123d82b6bef8487a382583db849ad19fa497dd4e12a7","last_reissued_at":"2026-07-14T01:20:32.595326Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:32.595326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.10245","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":"2026-07-14T01:20:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tigR9jiKdP0TkfKB9KwbBE+vkrHIQiDd1LXpYxjBU07Iaao5JA19gv331zLrlbwSWoejk9RMr4XNZ6qozdoJAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.041593Z"},"content_sha256":"0506de173b2af09556029b222f60f44892f93573b04b19b843d03a26fe07e12c","schema_version":"1.0","event_id":"sha256:0506de173b2af09556029b222f60f44892f93573b04b19b843d03a26fe07e12c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Amir Reza Jafari, Noel Crespi, Praboda Rajapaksha, Reza Farahbakhsh","submitted_at":"2026-07-11T10:20:03Z","abstract_excerpt":"Despite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly from the limited incorporation of individual differences, particularly personality traits, which are fundamental to human emotional inference. To address this, we propose PTEI, a novel framework for integrating Personality Traits into Emotional Intelligence tasks using LLMs. In PTEI, MBTI and OCEAN personality traits are first extracted directly from the given emotional scenarios and then utilized as contextual knowledg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10245","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/2607.10245/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":"2026-07-14T01:20:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2yFsnxdFojjQwfQpAzYMJN1KB3utEwbn/Wk9TqmBj0rt056MFkLsxMT+GENl8tdZ8d5/npuSwy2uXahUGu4qCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.041961Z"},"content_sha256":"67cba91651e53c7e62d1180c65ff837baefa039db200cac39f1444601d7108c5","schema_version":"1.0","event_id":"sha256:67cba91651e53c7e62d1180c65ff837baefa039db200cac39f1444601d7108c5"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.2190/DUGG-P24E-52WK-6CDG resolves to 'Emotional Intelligence'. A reader following the printed text alone cannot reach it.","snippet":"Peter Salovey and John D. Mayer. 1990. Emotional Intelligence.Imagination, Cognition and Personality9, 3 (1990), 185–211. doi:10.2190/DUGG-P24E-52WK- 6CDG","arxiv_id":"2607.10245","detector":"doi_compliance","evidence":{"ref_index":25,"verdict_class":"incontrovertible","resolved_title":"Emotional Intelligence","printed_excerpt":"10.2190/dugg-p24e-52wk-","reconstructed_doi":"10.2190/DUGG-P24E-52WK-6CDG"},"severity":"advisory","ref_index":25,"audited_at":"2026-07-29T14:52:03.592110Z","event_type":"pith.integrity.v1","detected_doi":"10.2190/DUGG-P24E-52WK-6CDG","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"24271033d1af20376da64ea091d71d43e77f7618a82330c9106ed829089838b3","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"Emotional Intelligence","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":13737,"payload_sha256":"a3d2a93aa925ca3c265919462a2447dc5d2699fb965e5834fa39049ae5259bd1","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T14:56:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0D5HZ/v9RLjIshSv2TqvA/53zYchSiv7AR6uRZLflICD3RwVL4tnWCnFj8v2EJZd8giVow1sr/6n/g3s1dPhCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.044799Z"},"content_sha256":"c462a8d4d9a6ef8d9e52e39a35e0516d7ccf032771dcfee4bbb8f6828b6e111d","schema_version":"1.0","event_id":"sha256:c462a8d4d9a6ef8d9e52e39a35e0516d7ccf032771dcfee4bbb8f6828b6e111d"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.48550/ARXIV.2312.06281) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Samuel J Paech. 2023. Eq-bench: An emotional intelligence benchmark for large language models.arXiv preprint arXiv:2312.06281(2023). doi:10.48550/ARXIV.2 312.06281","arxiv_id":"2607.10245","detector":"doi_compliance","evidence":{"ref_index":20,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.48550/arxiv.2","reconstructed_doi":"10.48550/ARXIV.2312.06281"},"severity":"advisory","ref_index":20,"audited_at":"2026-07-29T14:52:03.592110Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/ARXIV.2312.06281","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"e63f3302a0aa04ab7944dcf7bf4fc6a65fd5ed2b6222693557fc6b0d586ff2de","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":13736,"payload_sha256":"84c7822b6d02af89af708a2592c34d1b124a0fad9f309c57bb6e6006076d1dc8","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T14:56:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oy+YqwKsoHWmMtSNb1BRk9WC5Cknf6+z7c9cCnn+igLmiDjY7zrklahIwESD2E1II4HLt7KGIOeZFzyHLPECCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.045027Z"},"content_sha256":"be03ccffaa3ea696fb81654513796eaccd124b6f55c5a7df00a843171e83efa4","schema_version":"1.0","event_id":"sha256:be03ccffaa3ea696fb81654513796eaccd124b6f55c5a7df00a843171e83efa4"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"integrity","payload":{"note":"Identifier '10.5555/3524938.3525087' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597–1607. doi:10","arxiv_id":"2607.10245","detector":"doi_compliance","evidence":{"doi":"10.5555/3524938.3525087","arxiv_id":null,"ref_index":3,"raw_excerpt":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597–1607. doi:10.5555/3524938.3525087","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":3,"audited_at":"2026-07-29T14:52:03.592110Z","event_type":"pith.integrity.v1","detected_doi":"10.5555/3524938.3525087","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"153f1a3ef994d92c850f5a0477a97f57b9d8669c3f26fd2a7d436d3d1ccb52e2","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":13735,"payload_sha256":"3901c0732a7f2f9090a7ca6f1bd50c4b2309a3462b3ba1e1a4fc4831d23c7ed7","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T14:56:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gbMvDYNLpUvs+U9jItN5f6lnWnfH2Aw8z2ydii8IGkg7kFCM4lUTX8IhfN0+HytgUrptnM8NY1PIlFS03HIsDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.045256Z"},"content_sha256":"9ab337fd2ac2584684b51927989740693d61d3b8e2093edfbbebc8d8efbb9e97","schema_version":"1.0","event_id":"sha256:9ab337fd2ac2584684b51927989740693d61d3b8e2093edfbbebc8d8efbb9e97"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","target":"integrity","payload":{"note":"Identifier '10.5555/3524938.3525087' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597–1607. doi:10","arxiv_id":"2607.10245","detector":"doi_compliance","evidence":{"doi":"10.5555/3524938.3525087","arxiv_id":null,"ref_index":3,"raw_excerpt":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597–1607. doi:10.5555/3524938.3525087","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":3,"audited_at":"2026-07-14T13:18:51.140560Z","event_type":"pith.integrity.v1","detected_doi":"10.5555/3524938.3525087","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"2c2a5e31a69976115fb643593943c84f9d51450a7b76af03efeffa7cef9190eb","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":13345,"payload_sha256":"1201f34f700e2ce2d047ed21d7d2ec327694ee5b30b7d7007477bcb8981f45ed","signature_b64":"nUfa+Dh9lfNAoei1Dc4aUghoaoQ7QINYPckSg1YEK3bQLY7Oaw8ObxyUhTF0MvekuEjW4jqUQHJm3dkjo0thCA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-14T13:21:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SlKgtmpPbyTHjRnjc1x+Xf6yXZoJUxSCkLHstxbQHmOGx5UGplJ2M91bPAn2QjHWY2Wy6Q5ocKwoatXFWWdQAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T02:44:03.045468Z"},"content_sha256":"bf27a7d6b7704919f16a80a68c7ece82e8cff7bae9c4294f3eb746ec19748897","schema_version":"1.0","event_id":"sha256:bf27a7d6b7704919f16a80a68c7ece82e8cff7bae9c4294f3eb746ec19748897"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/bundle.json","state_url":"https://pith.science/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/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":"2026-08-01T02:44:03Z","links":{"resolver":"https://pith.science/pith/IX2IM5S4LMK5RGU6CI6YFNV67B","bundle":"https://pith.science/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/bundle.json","state":"https://pith.science/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IX2IM5S4LMK5RGU6CI6YFNV67B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IX2IM5S4LMK5RGU6CI6YFNV67B","merge_version":"pith-open-graph-merge-v1","event_count":6,"valid_event_count":6,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b3f4d743bd8b8f4e9bdf633a86d4a8ea74d8e6257779468c1181a06c2f293301","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-11T10:20:03Z","title_canon_sha256":"369002c303eb13a8d1ead5ebdb54c20907968c5528e2c248bf22481e27b8205f"},"schema_version":"1.0","source":{"id":"2607.10245","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.10245","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"arxiv_version","alias_value":"2607.10245v1","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10245","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_12","alias_value":"IX2IM5S4LMK5","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_16","alias_value":"IX2IM5S4LMK5RGU6","created_at":"2026-07-14T01:20:32Z"},{"alias_kind":"pith_short_8","alias_value":"IX2IM5S4","created_at":"2026-07-14T01:20:32Z"}],"graph_snapshots":[{"event_id":"sha256:67cba91651e53c7e62d1180c65ff837baefa039db200cac39f1444601d7108c5","target":"graph","created_at":"2026-07-14T01:20:32Z","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/2607.10245/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly from the limited incorporation of individual differences, particularly personality traits, which are fundamental to human emotional inference. To address this, we propose PTEI, a novel framework for integrating Personality Traits into Emotional Intelligence tasks using LLMs. In PTEI, MBTI and OCEAN personality traits are first extracted directly from the given emotional scenarios and then utilized as contextual knowledg","authors_text":"Amir Reza Jafari, Noel Crespi, Praboda Rajapaksha, Reza Farahbakhsh","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-11T10:20:03Z","title":"PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10245","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":"sha256:0506de173b2af09556029b222f60f44892f93573b04b19b843d03a26fe07e12c","target":"record","created_at":"2026-07-14T01:20:32Z","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":"b3f4d743bd8b8f4e9bdf633a86d4a8ea74d8e6257779468c1181a06c2f293301","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-11T10:20:03Z","title_canon_sha256":"369002c303eb13a8d1ead5ebdb54c20907968c5528e2c248bf22481e27b8205f"},"schema_version":"1.0","source":{"id":"2607.10245","kind":"arxiv","version":1}},"canonical_sha256":"45f486765c5b15d89a9e123d82b6bef8487a382583db849ad19fa497dd4e12a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45f486765c5b15d89a9e123d82b6bef8487a382583db849ad19fa497dd4e12a7","first_computed_at":"2026-07-14T01:20:32.595326Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T01:20:32.595326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VAu/Q9yvwj+GJJWqnaIgRfTBb3n++CxlMJH7UglKY6qUcTctUc/Pv/dSDUqGiOnqvBTiO5sgASlp1sUwtj73AA==","signature_status":"signed_v1","signed_at":"2026-07-14T01:20:32.596239Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.10245","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:9ab337fd2ac2584684b51927989740693d61d3b8e2093edfbbebc8d8efbb9e97","sha256:be03ccffaa3ea696fb81654513796eaccd124b6f55c5a7df00a843171e83efa4","sha256:bf27a7d6b7704919f16a80a68c7ece82e8cff7bae9c4294f3eb746ec19748897","sha256:c462a8d4d9a6ef8d9e52e39a35e0516d7ccf032771dcfee4bbb8f6828b6e111d"]}],"invalid_events":[],"applied_event_ids":["sha256:0506de173b2af09556029b222f60f44892f93573b04b19b843d03a26fe07e12c","sha256:67cba91651e53c7e62d1180c65ff837baefa039db200cac39f1444601d7108c5"],"state_sha256":"279af4912467ab7e8ce4cd6a4a6faa17e51b7886ce39c3c5155d0c5585dbef0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8zvIdEJd1VtaQ3UFQI/O1n/EPoIdutFn7GuTIV+i1FdDCC2gp7z441/3067jRcKvJQBFsK8V2+ieeSxQPKrOCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T02:44:03.047171Z","bundle_sha256":"8e44b9036295badcb0c086ebcad56f631464c3466f03b3b95e5ad3f4be6de788"}}