{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EBZQBDKCKLIKJUZL73PUH3MZIX","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":"553015f46156d68a2736cac5c8425c539b0120fdf092c0aaab7d55b212f99bde","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T13:43:58Z","title_canon_sha256":"aec1c56bbe2f9fe9051362fb60dc9ad151de3599601500dfb620b3323a6f3571"},"schema_version":"1.0","source":{"id":"2208.00859","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.00859","created_at":"2026-06-09T02:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"2208.00859v2","created_at":"2026-06-09T02:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00859","created_at":"2026-06-09T02:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"EBZQBDKCKLIK","created_at":"2026-06-09T02:08:24Z"},{"alias_kind":"pith_short_16","alias_value":"EBZQBDKCKLIKJUZL","created_at":"2026-06-09T02:08:24Z"},{"alias_kind":"pith_short_8","alias_value":"EBZQBDKC","created_at":"2026-06-09T02:08:24Z"}],"graph_snapshots":[{"event_id":"sha256:f104fdb51c68f9ff7592a89c491f6826df00c141c1af6fac2dc41375b9ae75ac","target":"graph","created_at":"2026-06-09T02:08:24Z","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/2208.00859/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model. We pre-train our model on synthetically generated flowsheet topologies to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally, we use the trained model for causal language model","authors_text":"Artur M. Schweidtmann, Gabriel Vogel, Lukas Schulze Balhorn","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T13:43:58Z","title":"Learning from flowsheets: A generative transformer model for autocompletion of flowsheets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00859","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:b509e3a6bfcb75331813e8ee2f5baccfc29dc49d5de7388b25da03d104d3f835","target":"record","created_at":"2026-06-09T02:08:24Z","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":"553015f46156d68a2736cac5c8425c539b0120fdf092c0aaab7d55b212f99bde","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T13:43:58Z","title_canon_sha256":"aec1c56bbe2f9fe9051362fb60dc9ad151de3599601500dfb620b3323a6f3571"},"schema_version":"1.0","source":{"id":"2208.00859","kind":"arxiv","version":2}},"canonical_sha256":"2073008d4252d0a4d32bfedf43ed9945c5ad12b7ea5f3cbde118dbaac25c767e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2073008d4252d0a4d32bfedf43ed9945c5ad12b7ea5f3cbde118dbaac25c767e","first_computed_at":"2026-06-09T02:08:24.711028Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:08:24.711028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0hCGE8yVQvPNMfDCD3+Dp3Odg181e0Me6LXCC6fH2ZdvCo7PBzHEqc72SQmWhr/67peOVIWOHFIL3Vw4FHusBA==","signature_status":"signed_v1","signed_at":"2026-06-09T02:08:24.711946Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.00859","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b509e3a6bfcb75331813e8ee2f5baccfc29dc49d5de7388b25da03d104d3f835","sha256:f104fdb51c68f9ff7592a89c491f6826df00c141c1af6fac2dc41375b9ae75ac"],"state_sha256":"096b7438fbf5f757da8d6eed618f72e6ca63a05077b6411f38f34cdc615b5564"}