{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EOZHPO57N2AHKXH43RD6WMX6LY","short_pith_number":"pith:EOZHPO57","canonical_record":{"source":{"id":"2010.08213","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T07:51:16Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4f2af292c0e6802e72e860237bc4a07f8a9cca1e86d9a9d3aa6052214fffc840","abstract_canon_sha256":"1c3bcdfa067710dabb72fd0bb87d3a74e0433478cc5e699443c130443acfdbbe"},"schema_version":"1.0"},"canonical_sha256":"23b277bbbf6e80755cfcdc47eb32fe5e110b2b6d2d70883e3314e9351c79d87d","source":{"kind":"arxiv","id":"2010.08213","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.08213","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"arxiv_version","alias_value":"2010.08213v2","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08213","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_12","alias_value":"EOZHPO57N2AH","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_16","alias_value":"EOZHPO57N2AHKXH4","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_8","alias_value":"EOZHPO57","created_at":"2026-07-05T01:49:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EOZHPO57N2AHKXH43RD6WMX6LY","target":"record","payload":{"canonical_record":{"source":{"id":"2010.08213","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T07:51:16Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4f2af292c0e6802e72e860237bc4a07f8a9cca1e86d9a9d3aa6052214fffc840","abstract_canon_sha256":"1c3bcdfa067710dabb72fd0bb87d3a74e0433478cc5e699443c130443acfdbbe"},"schema_version":"1.0"},"canonical_sha256":"23b277bbbf6e80755cfcdc47eb32fe5e110b2b6d2d70883e3314e9351c79d87d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:05.416275Z","signature_b64":"z+6Jkd05BqJMVo//GydA7cZ69ZKDowtC7RUWvJVRno39H3MGDUxODYrtezZyOfqAkekt71s7lA7R+bsC6AyrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23b277bbbf6e80755cfcdc47eb32fe5e110b2b6d2d70883e3314e9351c79d87d","last_reissued_at":"2026-07-05T01:49:05.415836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:05.415836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.08213","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":"2026-07-05T01:49:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zCpdoDyoV+hmRa3Yk32YgO2NKf1n+5PbveYXf4ZpjeebVm0LvZsPXLRJ+cgCmsfMxBrfLSvJDirWBU2K6jQaCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:48:14.961651Z"},"content_sha256":"54bd7eb9035f5cd9409e85f59e29c7d8bf4163724aaa1b097e4c011f57980423","schema_version":"1.0","event_id":"sha256:54bd7eb9035f5cd9409e85f59e29c7d8bf4163724aaa1b097e4c011f57980423"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EOZHPO57N2AHKXH43RD6WMX6LY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kyomin Jung, Seunghyun Yoon, Seungpil Won, Yanghoon Kim","submitted_at":"2020-10-16T07:51:16Z","abstract_excerpt":"Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid error accumulation due to exposure bias. Other approaches employ approximation techniques that map the text to continuous representation in order to circumvent the non-differentiable discrete process. Particularly, autoencoder-based methods eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08213","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/2010.08213/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":"2026-07-05T01:49:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ppN9eKWdj7J+yRcn8SantgIf5h6+Gr2LYb91buHfOxNkHGGkXEsaBTBdp0evoTFbSf4AHBu8C4Qt2ttg0tO7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:48:14.962728Z"},"content_sha256":"99783ddf31b6d4cae07cbbf435bea4082ba18cf87fd5d4596c09eb991cabec0f","schema_version":"1.0","event_id":"sha256:99783ddf31b6d4cae07cbbf435bea4082ba18cf87fd5d4596c09eb991cabec0f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EOZHPO57N2AHKXH43RD6WMX6LY/bundle.json","state_url":"https://pith.science/pith/EOZHPO57N2AHKXH43RD6WMX6LY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EOZHPO57N2AHKXH43RD6WMX6LY/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":"2026-08-06T17:48:14Z","links":{"resolver":"https://pith.science/pith/EOZHPO57N2AHKXH43RD6WMX6LY","bundle":"https://pith.science/pith/EOZHPO57N2AHKXH43RD6WMX6LY/bundle.json","state":"https://pith.science/pith/EOZHPO57N2AHKXH43RD6WMX6LY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EOZHPO57N2AHKXH43RD6WMX6LY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EOZHPO57N2AHKXH43RD6WMX6LY","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":"1c3bcdfa067710dabb72fd0bb87d3a74e0433478cc5e699443c130443acfdbbe","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T07:51:16Z","title_canon_sha256":"4f2af292c0e6802e72e860237bc4a07f8a9cca1e86d9a9d3aa6052214fffc840"},"schema_version":"1.0","source":{"id":"2010.08213","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.08213","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"arxiv_version","alias_value":"2010.08213v2","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.08213","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_12","alias_value":"EOZHPO57N2AH","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_16","alias_value":"EOZHPO57N2AHKXH4","created_at":"2026-07-05T01:49:05Z"},{"alias_kind":"pith_short_8","alias_value":"EOZHPO57","created_at":"2026-07-05T01:49:05Z"}],"graph_snapshots":[{"event_id":"sha256:99783ddf31b6d4cae07cbbf435bea4082ba18cf87fd5d4596c09eb991cabec0f","target":"graph","created_at":"2026-07-05T01:49:05Z","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/2010.08213/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid error accumulation due to exposure bias. Other approaches employ approximation techniques that map the text to continuous representation in order to circumvent the non-differentiable discrete process. Particularly, autoencoder-based methods eff","authors_text":"Kyomin Jung, Seunghyun Yoon, Seungpil Won, Yanghoon Kim","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T07:51:16Z","title":"Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.08213","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":"sha256:54bd7eb9035f5cd9409e85f59e29c7d8bf4163724aaa1b097e4c011f57980423","target":"record","created_at":"2026-07-05T01:49:05Z","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":"1c3bcdfa067710dabb72fd0bb87d3a74e0433478cc5e699443c130443acfdbbe","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-16T07:51:16Z","title_canon_sha256":"4f2af292c0e6802e72e860237bc4a07f8a9cca1e86d9a9d3aa6052214fffc840"},"schema_version":"1.0","source":{"id":"2010.08213","kind":"arxiv","version":2}},"canonical_sha256":"23b277bbbf6e80755cfcdc47eb32fe5e110b2b6d2d70883e3314e9351c79d87d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23b277bbbf6e80755cfcdc47eb32fe5e110b2b6d2d70883e3314e9351c79d87d","first_computed_at":"2026-07-05T01:49:05.415836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:05.415836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z+6Jkd05BqJMVo//GydA7cZ69ZKDowtC7RUWvJVRno39H3MGDUxODYrtezZyOfqAkekt71s7lA7R+bsC6AyrBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:05.416275Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.08213","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54bd7eb9035f5cd9409e85f59e29c7d8bf4163724aaa1b097e4c011f57980423","sha256:99783ddf31b6d4cae07cbbf435bea4082ba18cf87fd5d4596c09eb991cabec0f"],"state_sha256":"2a048a3c8f46ff8d7a5c4ceff54402d455480115b766a8c219aad6ff6ae79174"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VGrWKVowHOJjkTrlVYApc+3cKObsNqhgfXQjtz0BNH1iYETaJsZMOhIt5ffX3VZMGawNLDNJy5My9i4bbtGWAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T17:48:14.970535Z","bundle_sha256":"6bce0939c200f57ee2c58925ad91ca843bac3d5d89a52dd2fbae434a9c335477"}}