{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:U7QFZDVT7QXBA6PMFPEV2OEMGW","short_pith_number":"pith:U7QFZDVT","canonical_record":{"source":{"id":"2601.02871","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-06T10:00:15Z","cross_cats_sorted":[],"title_canon_sha256":"061f4b295b9e9430b4600441d46ef1b067cd493191ae13f0e61232632822ac61","abstract_canon_sha256":"0a36bc5a5ea8367d2e262a95f52a7fb0c732268f7d9299c75c52b537e11f292e"},"schema_version":"1.0"},"canonical_sha256":"a7e05c8eb3fc2e1079ec2bc95d388c35a19ef640538c873e6474fe04cc4aaf12","source":{"kind":"arxiv","id":"2601.02871","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02871","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02871v3","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02871","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"U7QFZDVT7QXB","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_16","alias_value":"U7QFZDVT7QXBA6PM","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_8","alias_value":"U7QFZDVT","created_at":"2026-07-10T01:19:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:U7QFZDVT7QXBA6PMFPEV2OEMGW","target":"record","payload":{"canonical_record":{"source":{"id":"2601.02871","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-06T10:00:15Z","cross_cats_sorted":[],"title_canon_sha256":"061f4b295b9e9430b4600441d46ef1b067cd493191ae13f0e61232632822ac61","abstract_canon_sha256":"0a36bc5a5ea8367d2e262a95f52a7fb0c732268f7d9299c75c52b537e11f292e"},"schema_version":"1.0"},"canonical_sha256":"a7e05c8eb3fc2e1079ec2bc95d388c35a19ef640538c873e6474fe04cc4aaf12","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T01:19:41.347283Z","signature_b64":"iJjvjSdIc5o1ZeKrap/SR/ULa2Ndd7LuA85jpKH79KdhC2rs/kGvu9X2D+xt8VXbw12yFBB+Jng/fB3/Wrq6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7e05c8eb3fc2e1079ec2bc95d388c35a19ef640538c873e6474fe04cc4aaf12","last_reissued_at":"2026-07-10T01:19:41.346791Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T01:19:41.346791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.02871","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-10T01:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Skey8hVEDho3tqBcNblrovfqM8OP2esg22ZaU9a5xjR/LZhnZNGqC1iHPBr6aQeHLLtAre3GQQzXE9rOCk/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:57:28.268889Z"},"content_sha256":"fd1626a56f45f96497d2734aca90ef03d990572f00f7cf256f1c870e6893ef63","schema_version":"1.0","event_id":"sha256:fd1626a56f45f96497d2734aca90ef03d990572f00f7cf256f1c870e6893ef63"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:U7QFZDVT7QXBA6PMFPEV2OEMGW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dunqiang Liu, Haojun Xu, Hao Wang, Huai Yuen Khor, Huan He, Ke Ma, Qi Dai, Ruqian Shi, Sicheng Zhou, Sijia Yao, Yafei Liu, Zhiyong Cao","submitted_at":"2026-01-06T10:00:15Z","abstract_excerpt":"Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02871","kind":"arxiv","version":3},"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/2601.02871/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":"2026-07-10T01:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B/WuH2F6q2FU3OTh4GC04jlyshF63A+Raisga0vW5ODts5W1T/d0ZznBHamPQQFJeEV2ZzuGuqlcAqXp4leBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:57:28.269394Z"},"content_sha256":"c39b58180b3bc78fe471fa3ca3baafd70732251c6cf842f42dbe7126c16a583d","schema_version":"1.0","event_id":"sha256:c39b58180b3bc78fe471fa3ca3baafd70732251c6cf842f42dbe7126c16a583d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/bundle.json","state_url":"https://pith.science/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/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":"2026-08-05T23:57:28Z","links":{"resolver":"https://pith.science/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW","bundle":"https://pith.science/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/bundle.json","state":"https://pith.science/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U7QFZDVT7QXBA6PMFPEV2OEMGW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:U7QFZDVT7QXBA6PMFPEV2OEMGW","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":"0a36bc5a5ea8367d2e262a95f52a7fb0c732268f7d9299c75c52b537e11f292e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-06T10:00:15Z","title_canon_sha256":"061f4b295b9e9430b4600441d46ef1b067cd493191ae13f0e61232632822ac61"},"schema_version":"1.0","source":{"id":"2601.02871","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02871","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02871v3","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02871","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"U7QFZDVT7QXB","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_16","alias_value":"U7QFZDVT7QXBA6PM","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_8","alias_value":"U7QFZDVT","created_at":"2026-07-10T01:19:41Z"}],"graph_snapshots":[{"event_id":"sha256:c39b58180b3bc78fe471fa3ca3baafd70732251c6cf842f42dbe7126c16a583d","target":"graph","created_at":"2026-07-10T01:19:41Z","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/2601.02871/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fid","authors_text":"Dunqiang Liu, Haojun Xu, Hao Wang, Huai Yuen Khor, Huan He, Ke Ma, Qi Dai, Ruqian Shi, Sicheng Zhou, Sijia Yao, Yafei Liu, Zhiyong Cao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-06T10:00:15Z","title":"SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02871","kind":"arxiv","version":3},"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:fd1626a56f45f96497d2734aca90ef03d990572f00f7cf256f1c870e6893ef63","target":"record","created_at":"2026-07-10T01:19:41Z","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":"0a36bc5a5ea8367d2e262a95f52a7fb0c732268f7d9299c75c52b537e11f292e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-06T10:00:15Z","title_canon_sha256":"061f4b295b9e9430b4600441d46ef1b067cd493191ae13f0e61232632822ac61"},"schema_version":"1.0","source":{"id":"2601.02871","kind":"arxiv","version":3}},"canonical_sha256":"a7e05c8eb3fc2e1079ec2bc95d388c35a19ef640538c873e6474fe04cc4aaf12","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7e05c8eb3fc2e1079ec2bc95d388c35a19ef640538c873e6474fe04cc4aaf12","first_computed_at":"2026-07-10T01:19:41.346791Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-10T01:19:41.346791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iJjvjSdIc5o1ZeKrap/SR/ULa2Ndd7LuA85jpKH79KdhC2rs/kGvu9X2D+xt8VXbw12yFBB+Jng/fB3/Wrq6Dw==","signature_status":"signed_v1","signed_at":"2026-07-10T01:19:41.347283Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.02871","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd1626a56f45f96497d2734aca90ef03d990572f00f7cf256f1c870e6893ef63","sha256:c39b58180b3bc78fe471fa3ca3baafd70732251c6cf842f42dbe7126c16a583d"],"state_sha256":"39de69b512d32a62de16e22c53f21523883a02d7a6cd480153a9c86e2603e7fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2CL+24mXCE+HWysETGSqFSFV4TLjdOPhn2Q8XJEMu+/rXJxLZCjhqKe0TC9vwz5xFATsw+H59GQ8BHqMhiBvDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:57:28.274396Z","bundle_sha256":"8a4cc6bc3f8909d1e93a2702a1c6a43848347638dec3ba142360e0bcd3eafc0f"}}