{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:57AG6JFZUMN264HKBO4GV2ELDC","short_pith_number":"pith:57AG6JFZ","canonical_record":{"source":{"id":"2410.14853","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T20:35:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1d6ca87f7117a9dd2d9ba15edc7319c48e7f74975238381141f5579d8b0537e1","abstract_canon_sha256":"9e6ade14125a4c5b33aa0ba640bad2d8319b58518d6ad170d90fa1e8d520392e"},"schema_version":"1.0"},"canonical_sha256":"efc06f24b9a31baf70ea0bb86ae88b18ad20248f9746b058121d4a435c0092f8","source":{"kind":"arxiv","id":"2410.14853","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.14853","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"arxiv_version","alias_value":"2410.14853v2","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14853","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_12","alias_value":"57AG6JFZUMN2","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_16","alias_value":"57AG6JFZUMN264HK","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_8","alias_value":"57AG6JFZ","created_at":"2026-07-05T10:22:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:57AG6JFZUMN264HKBO4GV2ELDC","target":"record","payload":{"canonical_record":{"source":{"id":"2410.14853","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T20:35:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1d6ca87f7117a9dd2d9ba15edc7319c48e7f74975238381141f5579d8b0537e1","abstract_canon_sha256":"9e6ade14125a4c5b33aa0ba640bad2d8319b58518d6ad170d90fa1e8d520392e"},"schema_version":"1.0"},"canonical_sha256":"efc06f24b9a31baf70ea0bb86ae88b18ad20248f9746b058121d4a435c0092f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:08.270138Z","signature_b64":"CfgmqkKNEP6QRr5ihnCZu4JYD6p8lOgjO0FRcvR5zKUdnaUlgPA0byJT1Z3HrRYrqaSpNYy9QtpCHAGcmvfmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efc06f24b9a31baf70ea0bb86ae88b18ad20248f9746b058121d4a435c0092f8","last_reissued_at":"2026-07-05T10:22:08.269511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:08.269511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.14853","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":"2026-07-05T10:22:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8NrS7pGnTsBX+UTs7QAl7Ej8D+eRMRPCSzzB732Lch7kG094C9fIdIyfC41tBsvYf0u1nkH5/Bi6DznDl7DnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:27:18.575152Z"},"content_sha256":"e37597d2e5dce550b48cc14f95c602026715d5fe32cc40ae550157ea8ddc54f0","schema_version":"1.0","event_id":"sha256:e37597d2e5dce550b48cc14f95c602026715d5fe32cc40ae550157ea8ddc54f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:57AG6JFZUMN264HKBO4GV2ELDC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DFlow: Diverse Dialogue Flow Simulation with Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"James Gung, Lijia Sun, Saab Mansour, Song Feng, Wanyu Du, Yanjun Qi, Yi Zhang","submitted_at":"2024-10-18T20:35:28Z","abstract_excerpt":"Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper proposes a novel data simulation method designed to enhance the diversity of synthetic dialogues by focusing on task execution logic. Our method uses LLMs to generate decision tree-structured task plans, which enables the derivation of diver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14853","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/2410.14853/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":"2026-07-05T10:22:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TqN6yNX3Q0dObhjtZQnDHjPJPIIK1ju3c1DVDjTvAZC/TUPQXOfvJBmyb5nfwvE8+pN+fBciIuyBXisJNM2UDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:27:18.575669Z"},"content_sha256":"5de1e930093d4ec7513f32bb31e56974edb2b46eb45a876c91e9cc2028a3531a","schema_version":"1.0","event_id":"sha256:5de1e930093d4ec7513f32bb31e56974edb2b46eb45a876c91e9cc2028a3531a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/57AG6JFZUMN264HKBO4GV2ELDC/bundle.json","state_url":"https://pith.science/pith/57AG6JFZUMN264HKBO4GV2ELDC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/57AG6JFZUMN264HKBO4GV2ELDC/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":"2026-08-18T20:27:18Z","links":{"resolver":"https://pith.science/pith/57AG6JFZUMN264HKBO4GV2ELDC","bundle":"https://pith.science/pith/57AG6JFZUMN264HKBO4GV2ELDC/bundle.json","state":"https://pith.science/pith/57AG6JFZUMN264HKBO4GV2ELDC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/57AG6JFZUMN264HKBO4GV2ELDC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:57AG6JFZUMN264HKBO4GV2ELDC","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":"9e6ade14125a4c5b33aa0ba640bad2d8319b58518d6ad170d90fa1e8d520392e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T20:35:28Z","title_canon_sha256":"1d6ca87f7117a9dd2d9ba15edc7319c48e7f74975238381141f5579d8b0537e1"},"schema_version":"1.0","source":{"id":"2410.14853","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.14853","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"arxiv_version","alias_value":"2410.14853v2","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14853","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_12","alias_value":"57AG6JFZUMN2","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_16","alias_value":"57AG6JFZUMN264HK","created_at":"2026-07-05T10:22:08Z"},{"alias_kind":"pith_short_8","alias_value":"57AG6JFZ","created_at":"2026-07-05T10:22:08Z"}],"graph_snapshots":[{"event_id":"sha256:5de1e930093d4ec7513f32bb31e56974edb2b46eb45a876c91e9cc2028a3531a","target":"graph","created_at":"2026-07-05T10:22:08Z","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/2410.14853/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper proposes a novel data simulation method designed to enhance the diversity of synthetic dialogues by focusing on task execution logic. Our method uses LLMs to generate decision tree-structured task plans, which enables the derivation of diver","authors_text":"James Gung, Lijia Sun, Saab Mansour, Song Feng, Wanyu Du, Yanjun Qi, Yi Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T20:35:28Z","title":"DFlow: Diverse Dialogue Flow Simulation with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14853","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:e37597d2e5dce550b48cc14f95c602026715d5fe32cc40ae550157ea8ddc54f0","target":"record","created_at":"2026-07-05T10:22:08Z","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":"9e6ade14125a4c5b33aa0ba640bad2d8319b58518d6ad170d90fa1e8d520392e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T20:35:28Z","title_canon_sha256":"1d6ca87f7117a9dd2d9ba15edc7319c48e7f74975238381141f5579d8b0537e1"},"schema_version":"1.0","source":{"id":"2410.14853","kind":"arxiv","version":2}},"canonical_sha256":"efc06f24b9a31baf70ea0bb86ae88b18ad20248f9746b058121d4a435c0092f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efc06f24b9a31baf70ea0bb86ae88b18ad20248f9746b058121d4a435c0092f8","first_computed_at":"2026-07-05T10:22:08.269511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:08.269511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CfgmqkKNEP6QRr5ihnCZu4JYD6p8lOgjO0FRcvR5zKUdnaUlgPA0byJT1Z3HrRYrqaSpNYy9QtpCHAGcmvfmAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:08.270138Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.14853","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e37597d2e5dce550b48cc14f95c602026715d5fe32cc40ae550157ea8ddc54f0","sha256:5de1e930093d4ec7513f32bb31e56974edb2b46eb45a876c91e9cc2028a3531a"],"state_sha256":"0d5dfeb9d16c05a81d89fad1c3f8048b5cb5a44c0d4aabfa593ec19862de8835"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GUSqoxqkm9vz9Q1K8G2AeTUqXNwdgwehRsyk/7qiXcEa/j9XB1oMb/47JsuWtmLBiMM5kkMRwRFCaAX3L95gCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T20:27:18.580546Z","bundle_sha256":"87f3281daf61e5089c100dec7c90793e025259e0093d8ca09c50be960052f809"}}