{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:NJQBT7JNIUPPMY6X6UF2KHIMAI","short_pith_number":"pith:NJQBT7JN","canonical_record":{"source":{"id":"2005.01279","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-05-04T05:45:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35fa932628aa7fd46ff17db3473e55fab8a0f43473c8082540a0a1547582ba98","abstract_canon_sha256":"6f43b5b84bdf26ef3f61b9c01daebd86513a86419438f8081b60b73e6807fada"},"schema_version":"1.0"},"canonical_sha256":"6a6019fd2d451ef663d7f50ba51d0c0222f39a6600bcb1ac915a24416633ed24","source":{"kind":"arxiv","id":"2005.01279","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.01279","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"arxiv_version","alias_value":"2005.01279v1","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.01279","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_12","alias_value":"NJQBT7JNIUPP","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_16","alias_value":"NJQBT7JNIUPPMY6X","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_8","alias_value":"NJQBT7JN","created_at":"2026-07-05T01:00:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:NJQBT7JNIUPPMY6X6UF2KHIMAI","target":"record","payload":{"canonical_record":{"source":{"id":"2005.01279","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-05-04T05:45:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"35fa932628aa7fd46ff17db3473e55fab8a0f43473c8082540a0a1547582ba98","abstract_canon_sha256":"6f43b5b84bdf26ef3f61b9c01daebd86513a86419438f8081b60b73e6807fada"},"schema_version":"1.0"},"canonical_sha256":"6a6019fd2d451ef663d7f50ba51d0c0222f39a6600bcb1ac915a24416633ed24","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:06.159461Z","signature_b64":"Fn4kqPZdL9TFYunp7tEIbJO0LG07qxTxQV04UlfG3zT+Rz9t3ijmzkGo3tnjjGKDlxpeMy1UFrt5PNHogegvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a6019fd2d451ef663d7f50ba51d0c0222f39a6600bcb1ac915a24416633ed24","last_reissued_at":"2026-07-05T01:00:06.159039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:06.159039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.01279","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":"2026-07-05T01:00:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8D1uam6Kt2RC1LlJEWqKdjBV9KxHAQht+Y9eQcp3cHd3YqClrpZ8vjQ0h4O78FB8R7CBFYrkV25ggLDzotlJDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:47:00.485518Z"},"content_sha256":"2b87483c6dd3f127ed117d7ac9011f23f542b6eff0305d76ce54b873953762d2","schema_version":"1.0","event_id":"sha256:2b87483c6dd3f127ed117d7ac9011f23f542b6eff0305d76ce54b873953762d2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:NJQBT7JNIUPPMY6X6UF2KHIMAI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Adversarial Text Generation by Modeling the Distant Future","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Changyou Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin, Ruiyi Zhang, Wenlin Wang, Zhe Gan, Zheng Wen","submitted_at":"2020-05-04T05:45:13Z","abstract_excerpt":"Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator opt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.01279","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/2005.01279/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":"2026-07-05T01:00:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mcfgl3kFRdF95+RvTOIrTGGN7Z+50GeZdS+ixTVrLdg3UgivQS4jT1q+SnBeRnnHc8sEpqRjm7SMr3+OwvfgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:47:00.486009Z"},"content_sha256":"71407a7ac794e393db1f14df4cd16b0b173f899d5c26c6c3a54e9b3b571c8c4f","schema_version":"1.0","event_id":"sha256:71407a7ac794e393db1f14df4cd16b0b173f899d5c26c6c3a54e9b3b571c8c4f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/bundle.json","state_url":"https://pith.science/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/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":"2026-07-31T13:47:00Z","links":{"resolver":"https://pith.science/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI","bundle":"https://pith.science/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/bundle.json","state":"https://pith.science/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NJQBT7JNIUPPMY6X6UF2KHIMAI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NJQBT7JNIUPPMY6X6UF2KHIMAI","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":"6f43b5b84bdf26ef3f61b9c01daebd86513a86419438f8081b60b73e6807fada","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-05-04T05:45:13Z","title_canon_sha256":"35fa932628aa7fd46ff17db3473e55fab8a0f43473c8082540a0a1547582ba98"},"schema_version":"1.0","source":{"id":"2005.01279","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.01279","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"arxiv_version","alias_value":"2005.01279v1","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.01279","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_12","alias_value":"NJQBT7JNIUPP","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_16","alias_value":"NJQBT7JNIUPPMY6X","created_at":"2026-07-05T01:00:06Z"},{"alias_kind":"pith_short_8","alias_value":"NJQBT7JN","created_at":"2026-07-05T01:00:06Z"}],"graph_snapshots":[{"event_id":"sha256:71407a7ac794e393db1f14df4cd16b0b173f899d5c26c6c3a54e9b3b571c8c4f","target":"graph","created_at":"2026-07-05T01:00:06Z","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/2005.01279/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator opt","authors_text":"Changyou Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin, Ruiyi Zhang, Wenlin Wang, Zhe Gan, Zheng Wen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-05-04T05:45:13Z","title":"Improving Adversarial Text Generation by Modeling the Distant Future"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.01279","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":"sha256:2b87483c6dd3f127ed117d7ac9011f23f542b6eff0305d76ce54b873953762d2","target":"record","created_at":"2026-07-05T01:00:06Z","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":"6f43b5b84bdf26ef3f61b9c01daebd86513a86419438f8081b60b73e6807fada","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-05-04T05:45:13Z","title_canon_sha256":"35fa932628aa7fd46ff17db3473e55fab8a0f43473c8082540a0a1547582ba98"},"schema_version":"1.0","source":{"id":"2005.01279","kind":"arxiv","version":1}},"canonical_sha256":"6a6019fd2d451ef663d7f50ba51d0c0222f39a6600bcb1ac915a24416633ed24","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a6019fd2d451ef663d7f50ba51d0c0222f39a6600bcb1ac915a24416633ed24","first_computed_at":"2026-07-05T01:00:06.159039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:00:06.159039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fn4kqPZdL9TFYunp7tEIbJO0LG07qxTxQV04UlfG3zT+Rz9t3ijmzkGo3tnjjGKDlxpeMy1UFrt5PNHogegvCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:00:06.159461Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.01279","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b87483c6dd3f127ed117d7ac9011f23f542b6eff0305d76ce54b873953762d2","sha256:71407a7ac794e393db1f14df4cd16b0b173f899d5c26c6c3a54e9b3b571c8c4f"],"state_sha256":"0de678c2a645e06aa49f8c1c25763bc0a8838a418e611ef17ee4ac6ea950f260"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NYQZjNySmHb5d9b7uxK3Tnz+uusbZxbwq0e+doMrvX7Rmjgg20R2vkw26Ausx5wXC/YiselJ2KvdotVJySVOCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T13:47:00.489001Z","bundle_sha256":"47a55abae3fa705bd49c15e9729ced0eedb56bad1b98506c40f994c83ec4e8d1"}}