{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NGEOIQISEQW6TZHMDS5APPXDCI","short_pith_number":"pith:NGEOIQIS","canonical_record":{"source":{"id":"2601.02589","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-05T22:40:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"12e5611da705d7d848adc004fb13f4f564165ffceb341b965be1007bf9832781","abstract_canon_sha256":"224c98380c2ea2306a6d32474e4b34b1d2f61d6251d4c17c9912f5c0575cb6a3"},"schema_version":"1.0"},"canonical_sha256":"6988e44112242de9e4ec1cba07bee3121a65f7beecf5f1f9dd62391e6f5cde6e","source":{"kind":"arxiv","id":"2601.02589","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02589","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02589v4","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02589","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"NGEOIQISEQW6","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"NGEOIQISEQW6TZHM","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"NGEOIQIS","created_at":"2026-05-26T01:02:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NGEOIQISEQW6TZHMDS5APPXDCI","target":"record","payload":{"canonical_record":{"source":{"id":"2601.02589","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-05T22:40:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"12e5611da705d7d848adc004fb13f4f564165ffceb341b965be1007bf9832781","abstract_canon_sha256":"224c98380c2ea2306a6d32474e4b34b1d2f61d6251d4c17c9912f5c0575cb6a3"},"schema_version":"1.0"},"canonical_sha256":"6988e44112242de9e4ec1cba07bee3121a65f7beecf5f1f9dd62391e6f5cde6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:02:30.327626Z","signature_b64":"6i/HuOYL+Nl3bkIH3hpNE5GrDNnuDN6Vz5EKRQsnS2HcYB2aLlZTzZJYrmhXHxECZBi71Nup2sW71RhaSuMSAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6988e44112242de9e4ec1cba07bee3121a65f7beecf5f1f9dd62391e6f5cde6e","last_reissued_at":"2026-05-26T01:02:30.326871Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:02:30.326871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.02589","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:02:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0hYATRWqmWSZ9JDbAIB8+d8PtFMhNkSSbe0baIiPX0TZ1aFlZl35yX6+8Bs5EFHIt37QG0xiH4ugYSdcEWKlAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T15:06:36.519884Z"},"content_sha256":"4ceaae6cb9fdeb510263a06a3081dc451aa9b06d7bea24cd7fb64bca164b007d","schema_version":"1.0","event_id":"sha256:4ceaae6cb9fdeb510263a06a3081dc451aa9b06d7bea24cd7fb64bca164b007d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NGEOIQISEQW6TZHMDS5APPXDCI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Kris W Pan, Yongmin Yoo","submitted_at":"2026-01-05T22:40:15Z","abstract_excerpt":"Generating patent descriptions from scientific papers is challenging due to fundamental rhetorical and structural disparities between the two genres. Existing approaches treat this as surface-level rewriting, failing to capture the hierarchical reasoning and statutory constraints inherent in patent drafting. We propose FlowPlan-G2P, a graph-mediated generation framework that decomposes this transformation into three stages: (1) Concept Graph Induction, extracting technical entities and functional dependencies into a directed graph; (2) Section-level Planning, partitioning the graph into cohere"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"structured decomposition is a stronger determinant of quality than model scale","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the expert-validated benchmarks and domain-specific evaluation accurately measure legal compliance and that the induced concept graphs capture all statutory constraints needed for valid patent descriptions","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FlowPlan-G2P decomposes scientific paper to patent conversion into concept graph induction, section-level planning, and graph-conditioned generation, outperforming direct proprietary models under a domain-specific legal compliance evaluation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7f466ad62415d41612dbbdaabcbfe203d8b53895743523518c0465a61a87614f"},"source":{"id":"2601.02589","kind":"arxiv","version":4},"verdict":{"id":"3e75c7dd-7a45-4121-b58f-3512ff3df041","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T17:17:53.863280Z","strongest_claim":"structured decomposition is a stronger determinant of quality than model scale","one_line_summary":"FlowPlan-G2P decomposes scientific paper to patent conversion into concept graph induction, section-level planning, and graph-conditioned generation, outperforming direct proprietary models under a domain-specific legal compliance evaluation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the expert-validated benchmarks and domain-specific evaluation accurately measure legal compliance and that the induced concept graphs capture all statutory constraints needed for valid patent descriptions","pith_extraction_headline":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.02589/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":1,"snapshot_sha256":"ab1a771bd7d4c8ef841ff6222bb1c858cfbbfb04240a476945216f0c2ed7a7eb"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"3e75c7dd-7a45-4121-b58f-3512ff3df041"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:02:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9j01n+VAqMnSSlWuU0Gkcfo806GHSjRxz54ln6kYLwhzlpt+p46qwrojHlW/Q/mC/m9rFX3ZtQ767cNK2gh2DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T15:06:36.520707Z"},"content_sha256":"57cacd372d0657c3b1aacc8262d89690d041dc44e7337af0c62d93f9acd0b2d7","schema_version":"1.0","event_id":"sha256:57cacd372d0657c3b1aacc8262d89690d041dc44e7337af0c62d93f9acd0b2d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NGEOIQISEQW6TZHMDS5APPXDCI/bundle.json","state_url":"https://pith.science/pith/NGEOIQISEQW6TZHMDS5APPXDCI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NGEOIQISEQW6TZHMDS5APPXDCI/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":"2026-06-23T15:06:36Z","links":{"resolver":"https://pith.science/pith/NGEOIQISEQW6TZHMDS5APPXDCI","bundle":"https://pith.science/pith/NGEOIQISEQW6TZHMDS5APPXDCI/bundle.json","state":"https://pith.science/pith/NGEOIQISEQW6TZHMDS5APPXDCI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NGEOIQISEQW6TZHMDS5APPXDCI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NGEOIQISEQW6TZHMDS5APPXDCI","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":"224c98380c2ea2306a6d32474e4b34b1d2f61d6251d4c17c9912f5c0575cb6a3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-05T22:40:15Z","title_canon_sha256":"12e5611da705d7d848adc004fb13f4f564165ffceb341b965be1007bf9832781"},"schema_version":"1.0","source":{"id":"2601.02589","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02589","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02589v4","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02589","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"NGEOIQISEQW6","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"NGEOIQISEQW6TZHM","created_at":"2026-05-26T01:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"NGEOIQIS","created_at":"2026-05-26T01:02:30Z"}],"graph_snapshots":[{"event_id":"sha256:57cacd372d0657c3b1aacc8262d89690d041dc44e7337af0c62d93f9acd0b2d7","target":"graph","created_at":"2026-05-26T01:02:30Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"structured decomposition is a stronger determinant of quality than model scale"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the expert-validated benchmarks and domain-specific evaluation accurately measure legal compliance and that the induced concept graphs capture all statutory constraints needed for valid patent descriptions"},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"FlowPlan-G2P decomposes scientific paper to patent conversion into concept graph induction, section-level planning, and graph-conditioned generation, outperforming direct proprietary models under a domain-specific legal compliance evaluation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models."}],"snapshot_sha256":"7f466ad62415d41612dbbdaabcbfe203d8b53895743523518c0465a61a87614f"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"ab1a771bd7d4c8ef841ff6222bb1c858cfbbfb04240a476945216f0c2ed7a7eb"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2601.02589/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generating patent descriptions from scientific papers is challenging due to fundamental rhetorical and structural disparities between the two genres. Existing approaches treat this as surface-level rewriting, failing to capture the hierarchical reasoning and statutory constraints inherent in patent drafting. We propose FlowPlan-G2P, a graph-mediated generation framework that decomposes this transformation into three stages: (1) Concept Graph Induction, extracting technical entities and functional dependencies into a directed graph; (2) Section-level Planning, partitioning the graph into cohere","authors_text":"Kris W Pan, Yongmin Yoo","cross_cats":["cs.AI"],"headline":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-05T22:40:15Z","title":"FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02589","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-16T17:17:53.863280Z","id":"3e75c7dd-7a45-4121-b58f-3512ff3df041","model_set":{"reader":"grok-4.3"},"one_line_summary":"FlowPlan-G2P decomposes scientific paper to patent conversion into concept graph induction, section-level planning, and graph-conditioned generation, outperforming direct proprietary models under a domain-specific legal compliance evaluation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A structured graph framework turns scientific papers into patent descriptions more effectively than scaling up language models.","strongest_claim":"structured decomposition is a stronger determinant of quality than model scale","weakest_assumption":"That the expert-validated benchmarks and domain-specific evaluation accurately measure legal compliance and that the induced concept graphs capture all statutory constraints needed for valid patent descriptions"}},"verdict_id":"3e75c7dd-7a45-4121-b58f-3512ff3df041"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4ceaae6cb9fdeb510263a06a3081dc451aa9b06d7bea24cd7fb64bca164b007d","target":"record","created_at":"2026-05-26T01:02:30Z","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":"224c98380c2ea2306a6d32474e4b34b1d2f61d6251d4c17c9912f5c0575cb6a3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-05T22:40:15Z","title_canon_sha256":"12e5611da705d7d848adc004fb13f4f564165ffceb341b965be1007bf9832781"},"schema_version":"1.0","source":{"id":"2601.02589","kind":"arxiv","version":4}},"canonical_sha256":"6988e44112242de9e4ec1cba07bee3121a65f7beecf5f1f9dd62391e6f5cde6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6988e44112242de9e4ec1cba07bee3121a65f7beecf5f1f9dd62391e6f5cde6e","first_computed_at":"2026-05-26T01:02:30.326871Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:02:30.326871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6i/HuOYL+Nl3bkIH3hpNE5GrDNnuDN6Vz5EKRQsnS2HcYB2aLlZTzZJYrmhXHxECZBi71Nup2sW71RhaSuMSAg==","signature_status":"signed_v1","signed_at":"2026-05-26T01:02:30.327626Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.02589","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ceaae6cb9fdeb510263a06a3081dc451aa9b06d7bea24cd7fb64bca164b007d","sha256:57cacd372d0657c3b1aacc8262d89690d041dc44e7337af0c62d93f9acd0b2d7"],"state_sha256":"6f3c1f664a00620a62b8ab84ed0100024455cbb5294dcea3f1a53a3243a2c407"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4bxB26NFCmGT6ADHhFAL7PcI0VkYveFujdI1bpHK+5lCqgOqxzsjG3tS0slq+jnp1XiD82SOt3u9++ZXUu1ZBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T15:06:36.524149Z","bundle_sha256":"df58940df7d3823fbadbf0abd0f11aceb2ff03861859f0569277d027a12f6531"}}