{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:H3F7BPVBH4SZ2S4EH4J6PTSCOF","short_pith_number":"pith:H3F7BPVB","canonical_record":{"source":{"id":"2006.15720","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-28T21:23:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9bc2d7cb44aad65207440a01c39641234fa4fc3bda67a0ccf78d24541d4cd242","abstract_canon_sha256":"99c2c9407aed1f701a76605467ae33e275e708002c62105b9416e19210584f6c"},"schema_version":"1.0"},"canonical_sha256":"3ecbf0bea13f259d4b843f13e7ce42716b2a263a992cd321a34b01a192862026","source":{"kind":"arxiv","id":"2006.15720","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.15720","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"arxiv_version","alias_value":"2006.15720v2","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.15720","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_12","alias_value":"H3F7BPVBH4SZ","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"H3F7BPVBH4SZ2S4E","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"H3F7BPVB","created_at":"2026-07-05T02:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:H3F7BPVBH4SZ2S4EH4J6PTSCOF","target":"record","payload":{"canonical_record":{"source":{"id":"2006.15720","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-28T21:23:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9bc2d7cb44aad65207440a01c39641234fa4fc3bda67a0ccf78d24541d4cd242","abstract_canon_sha256":"99c2c9407aed1f701a76605467ae33e275e708002c62105b9416e19210584f6c"},"schema_version":"1.0"},"canonical_sha256":"3ecbf0bea13f259d4b843f13e7ce42716b2a263a992cd321a34b01a192862026","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:31:59.560485Z","signature_b64":"i7KqOU7AyNiAo1FK5/hsBhSaUwT+6XbhcUNrdxhUqlQU+QTLWRZgpj0piHN93qwdesuH3XyPyvWoFKQM+HcvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ecbf0bea13f259d4b843f13e7ce42716b2a263a992cd321a34b01a192862026","last_reissued_at":"2026-07-05T02:31:59.559986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:31:59.559986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.15720","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":"2026-07-05T02:31:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GgLI0Qkm0jUbToadaRvNXtFeDarueQHaefaZ8UhrilB9C5lTOquwFULx0x/HLWi9rWS98S+DklrYVdo2UwyFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:52:26.653323Z"},"content_sha256":"281c180f6a2ecb7596192045d8c03a46a299c18259200d08e253ff02f0bd0242","schema_version":"1.0","event_id":"sha256:281c180f6a2ecb7596192045d8c03a46a299c18259200d08e253ff02f0bd0242"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:H3F7BPVBH4SZ2S4EH4J6PTSCOF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Progressive Generation of Long Text with Pretrained Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Bowen Tan, Eric P. Xing, Maruan AI-Shedivat, Zhiting Hu, Zichao Yang","submitted_at":"2020-06-28T21:23:05Z","abstract_excerpt":"Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. Previous planning-then-generation methods also fall short of producing such long text in various domains. To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.15720","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/2006.15720/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":"2026-07-05T02:31:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3GHMV1+QPulS9sSguVWYhjN91MODxxcUF0LEPgAjgX+/Qfp7OF19DmFKjvf+G+Hd555OWsVGnJJ9r5qzWS3wCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:52:26.653888Z"},"content_sha256":"a79e502bd9a5fe00a15065176cb2f9b169bfb139695a46b65499812a58991c21","schema_version":"1.0","event_id":"sha256:a79e502bd9a5fe00a15065176cb2f9b169bfb139695a46b65499812a58991c21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/bundle.json","state_url":"https://pith.science/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/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":"2026-08-07T19:52:26Z","links":{"resolver":"https://pith.science/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF","bundle":"https://pith.science/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/bundle.json","state":"https://pith.science/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H3F7BPVBH4SZ2S4EH4J6PTSCOF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:H3F7BPVBH4SZ2S4EH4J6PTSCOF","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":"99c2c9407aed1f701a76605467ae33e275e708002c62105b9416e19210584f6c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-28T21:23:05Z","title_canon_sha256":"9bc2d7cb44aad65207440a01c39641234fa4fc3bda67a0ccf78d24541d4cd242"},"schema_version":"1.0","source":{"id":"2006.15720","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.15720","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"arxiv_version","alias_value":"2006.15720v2","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.15720","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_12","alias_value":"H3F7BPVBH4SZ","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"H3F7BPVBH4SZ2S4E","created_at":"2026-07-05T02:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"H3F7BPVB","created_at":"2026-07-05T02:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:a79e502bd9a5fe00a15065176cb2f9b169bfb139695a46b65499812a58991c21","target":"graph","created_at":"2026-07-05T02:31:59Z","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/2006.15720/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text (e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. Previous planning-then-generation methods also fall short of producing such long text in various domains. To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by gene","authors_text":"Bowen Tan, Eric P. Xing, Maruan AI-Shedivat, Zhiting Hu, Zichao Yang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-28T21:23:05Z","title":"Progressive Generation of Long Text with Pretrained Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.15720","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":"sha256:281c180f6a2ecb7596192045d8c03a46a299c18259200d08e253ff02f0bd0242","target":"record","created_at":"2026-07-05T02:31:59Z","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":"99c2c9407aed1f701a76605467ae33e275e708002c62105b9416e19210584f6c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-06-28T21:23:05Z","title_canon_sha256":"9bc2d7cb44aad65207440a01c39641234fa4fc3bda67a0ccf78d24541d4cd242"},"schema_version":"1.0","source":{"id":"2006.15720","kind":"arxiv","version":2}},"canonical_sha256":"3ecbf0bea13f259d4b843f13e7ce42716b2a263a992cd321a34b01a192862026","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ecbf0bea13f259d4b843f13e7ce42716b2a263a992cd321a34b01a192862026","first_computed_at":"2026-07-05T02:31:59.559986Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:31:59.559986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i7KqOU7AyNiAo1FK5/hsBhSaUwT+6XbhcUNrdxhUqlQU+QTLWRZgpj0piHN93qwdesuH3XyPyvWoFKQM+HcvCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:31:59.560485Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.15720","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:281c180f6a2ecb7596192045d8c03a46a299c18259200d08e253ff02f0bd0242","sha256:a79e502bd9a5fe00a15065176cb2f9b169bfb139695a46b65499812a58991c21"],"state_sha256":"320c2aee855ceb44f41c788bde1c758296b3f3b66f314438e7a64f73d8725f4e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ChWyBNS6ftV0O6A0t72UkteAMxGzxnsk+McOsNOF0JHIN4SjjJoK9T9EcLtCr+/tBMMZFFHKepAehAfhcnzmBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:52:26.659099Z","bundle_sha256":"4e1999b7291324581cdf28687642ee2492876a56c595967a5c1c12088abb7fc1"}}