{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5QCMAQ4227W2OVHZUJ7K337LBG","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":"45059084c82a2c077d022d175d7473b951c097bf202672556ee3a4f0041185e4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-02T19:46:22Z","title_canon_sha256":"6d7e88a77dc3c9b3182a96a1ff03f48ac46e243e6401caf077fdddfb2882c073"},"schema_version":"1.0","source":{"id":"2606.04177","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04177","created_at":"2026-06-04T01:08:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04177v1","created_at":"2026-06-04T01:08:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04177","created_at":"2026-06-04T01:08:19Z"},{"alias_kind":"pith_short_12","alias_value":"5QCMAQ4227W2","created_at":"2026-06-04T01:08:19Z"},{"alias_kind":"pith_short_16","alias_value":"5QCMAQ4227W2OVHZ","created_at":"2026-06-04T01:08:19Z"},{"alias_kind":"pith_short_8","alias_value":"5QCMAQ42","created_at":"2026-06-04T01:08:19Z"}],"graph_snapshots":[{"event_id":"sha256:59fa0dee9cb0b57e6b11737b2383cf2d4cb2e54c0f90eda2a379d277ba4364e9","target":"graph","created_at":"2026-06-04T01:08:19Z","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/2606.04177/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users. However, existing findings on which features reliably indicate LLM-generated text remain fragmented across feature sets, models, and text domains. To address this gap, we conduct a large-scale empirical study assessing the robustness of linguistic signals for characterizing AI-generated text. Our analysis covers 284 interpretable linguistic features across outputs from 27 LLMs and ten text domains under cross-model and cross-domain generaliz","authors_text":"Agnieszka Falenska, Esra D\\\"onmez, Maximilian Maurer, Yassir El Attar","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-02T19:46:22Z","title":"A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04177","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":"sha256:44950275220cdea29818beba47988984cb235787e8855e3867a4d9885f66e2f3","target":"record","created_at":"2026-06-04T01:08:19Z","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":"45059084c82a2c077d022d175d7473b951c097bf202672556ee3a4f0041185e4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-02T19:46:22Z","title_canon_sha256":"6d7e88a77dc3c9b3182a96a1ff03f48ac46e243e6401caf077fdddfb2882c073"},"schema_version":"1.0","source":{"id":"2606.04177","kind":"arxiv","version":1}},"canonical_sha256":"ec04c0439ad7eda754f9a27eadefeb09a838a83549a16a4c9ce8fe5f8f4cfe2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec04c0439ad7eda754f9a27eadefeb09a838a83549a16a4c9ce8fe5f8f4cfe2b","first_computed_at":"2026-06-04T01:08:19.114888Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:19.114888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D/FtYhcpYEAfxUPLPSru6D+IBr5H67JX+BjM7WiyFW8+1PE2tKhkIzWsdAVby0FOixTRQi8zzBhu5ooFDTSHCQ==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:19.115748Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.04177","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44950275220cdea29818beba47988984cb235787e8855e3867a4d9885f66e2f3","sha256:59fa0dee9cb0b57e6b11737b2383cf2d4cb2e54c0f90eda2a379d277ba4364e9"],"state_sha256":"985ef64c6def6ad5536c1e83048e4296ca729e22e64de6b872faaea5b0052d3c"}