{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5SSGVMSDHCY2MEWRFS3UT3GH4N","short_pith_number":"pith:5SSGVMSD","canonical_record":{"source":{"id":"2501.15427","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-26T07:07:01Z","cross_cats_sorted":[],"title_canon_sha256":"aa36ed6a2bcacf86373725f328830fc1f41375620dec6e068af5ccd4b0270a9a","abstract_canon_sha256":"b910df16221ec7feec4b103cba403ef5af94cd5fa93193da157e241bbdc6eb2e"},"schema_version":"1.0"},"canonical_sha256":"eca46ab24338b1a612d12cb749ecc7e3784a0a194e6e3d7c6225b7ade65db595","source":{"kind":"arxiv","id":"2501.15427","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15427","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15427v2","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15427","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"5SSGVMSDHCY2","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_16","alias_value":"5SSGVMSDHCY2MEWR","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_8","alias_value":"5SSGVMSD","created_at":"2026-07-05T10:15:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5SSGVMSDHCY2MEWRFS3UT3GH4N","target":"record","payload":{"canonical_record":{"source":{"id":"2501.15427","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-26T07:07:01Z","cross_cats_sorted":[],"title_canon_sha256":"aa36ed6a2bcacf86373725f328830fc1f41375620dec6e068af5ccd4b0270a9a","abstract_canon_sha256":"b910df16221ec7feec4b103cba403ef5af94cd5fa93193da157e241bbdc6eb2e"},"schema_version":"1.0"},"canonical_sha256":"eca46ab24338b1a612d12cb749ecc7e3784a0a194e6e3d7c6225b7ade65db595","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:48.597636Z","signature_b64":"j6E3WpqSJIYaQolGVVQS/TfpMaqtiI0BXYbwBFzsrZCPO3AxwYa3Pebv4coM6f16+HMRBzWQbqL2bHr9z+SSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eca46ab24338b1a612d12cb749ecc7e3784a0a194e6e3d7c6225b7ade65db595","last_reissued_at":"2026-07-05T10:15:48.596999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:48.596999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.15427","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0OV1b7c6dUxeddaDwNOgVBehFp9YeLh9Bgrl76E0SBN8uv9Qf1dSKL3JBPmpSQXm8hVnl+QhIHstolfO98dDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T04:46:51.917096Z"},"content_sha256":"2723701956b430e1d68022075c67f9f3ab0e32ae5307e33f56a4f257dd795ed6","schema_version":"1.0","event_id":"sha256:2723701956b430e1d68022075c67f9f3ab0e32ae5307e33f56a4f257dd795ed6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5SSGVMSDHCY2MEWRFS3UT3GH4N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dian Yu, Dong Yu, Hongming Zhang, Tao Ge, Wenhao Yu, Xiaoyang Wang","submitted_at":"2025-01-26T07:07:01Z","abstract_excerpt":"Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in developing and deploying role-playing dialogue agents. This study explores a large-scale data synthesis approach to equip LLMs with character generalization capabilities. We begin by synthesizing large-scale character profiles using personas from Persona Hub and then explore two strategies: response rewriting and response generation, to create character-aligned instructional responses. To validate the effectiveness of our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15427","kind":"arxiv","version":2},"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/2501.15427/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":"2026-07-05T10:15:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z4JwuJixJyzh5mAGIC2DsBvL/IkR/UIgRIFVpUpAxe45X13tWxsg3GrRz9r7uIAtuBp6Ys3Ztv2ozvQJ5tZ5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T04:46:51.917480Z"},"content_sha256":"90d5d750efd3dc94cd2b28849f24d21070328dfec6921c25feebb4fc3a0bde92","schema_version":"1.0","event_id":"sha256:90d5d750efd3dc94cd2b28849f24d21070328dfec6921c25feebb4fc3a0bde92"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/bundle.json","state_url":"https://pith.science/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/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":"2026-07-13T04:46:51Z","links":{"resolver":"https://pith.science/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N","bundle":"https://pith.science/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/bundle.json","state":"https://pith.science/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5SSGVMSDHCY2MEWRFS3UT3GH4N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5SSGVMSDHCY2MEWRFS3UT3GH4N","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":"b910df16221ec7feec4b103cba403ef5af94cd5fa93193da157e241bbdc6eb2e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-26T07:07:01Z","title_canon_sha256":"aa36ed6a2bcacf86373725f328830fc1f41375620dec6e068af5ccd4b0270a9a"},"schema_version":"1.0","source":{"id":"2501.15427","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15427","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15427v2","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15427","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_12","alias_value":"5SSGVMSDHCY2","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_16","alias_value":"5SSGVMSDHCY2MEWR","created_at":"2026-07-05T10:15:48Z"},{"alias_kind":"pith_short_8","alias_value":"5SSGVMSD","created_at":"2026-07-05T10:15:48Z"}],"graph_snapshots":[{"event_id":"sha256:90d5d750efd3dc94cd2b28849f24d21070328dfec6921c25feebb4fc3a0bde92","target":"graph","created_at":"2026-07-05T10:15:48Z","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/2501.15427/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in developing and deploying role-playing dialogue agents. This study explores a large-scale data synthesis approach to equip LLMs with character generalization capabilities. We begin by synthesizing large-scale character profiles using personas from Persona Hub and then explore two strategies: response rewriting and response generation, to create character-aligned instructional responses. To validate the effectiveness of our ","authors_text":"Dian Yu, Dong Yu, Hongming Zhang, Tao Ge, Wenhao Yu, Xiaoyang Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-26T07:07:01Z","title":"OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15427","kind":"arxiv","version":2},"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":"sha256:2723701956b430e1d68022075c67f9f3ab0e32ae5307e33f56a4f257dd795ed6","target":"record","created_at":"2026-07-05T10:15:48Z","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":"b910df16221ec7feec4b103cba403ef5af94cd5fa93193da157e241bbdc6eb2e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-26T07:07:01Z","title_canon_sha256":"aa36ed6a2bcacf86373725f328830fc1f41375620dec6e068af5ccd4b0270a9a"},"schema_version":"1.0","source":{"id":"2501.15427","kind":"arxiv","version":2}},"canonical_sha256":"eca46ab24338b1a612d12cb749ecc7e3784a0a194e6e3d7c6225b7ade65db595","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eca46ab24338b1a612d12cb749ecc7e3784a0a194e6e3d7c6225b7ade65db595","first_computed_at":"2026-07-05T10:15:48.596999Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:15:48.596999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j6E3WpqSJIYaQolGVVQS/TfpMaqtiI0BXYbwBFzsrZCPO3AxwYa3Pebv4coM6f16+HMRBzWQbqL2bHr9z+SSAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:15:48.597636Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.15427","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2723701956b430e1d68022075c67f9f3ab0e32ae5307e33f56a4f257dd795ed6","sha256:90d5d750efd3dc94cd2b28849f24d21070328dfec6921c25feebb4fc3a0bde92"],"state_sha256":"316e1631f070daf41b13197d589d79b101c6d7a3c81770221e141a317a868c6c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cZD1jdK06O0ojw5i6RPJTtu8ejioG5ymGX5u/WlKmJTxa2DlONRjmwTZeeg6OfGyQzUAHZNddEfQppAD4sPSBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T04:46:51.919521Z","bundle_sha256":"53cbf79faa1bc9fb21ae884124fea23dcb28e87ea12dbeff8307f761f055e1f6"}}