{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QROBQTAIBQ7PNVSI4O6HLFIWSA","short_pith_number":"pith:QROBQTAI","schema_version":"1.0","canonical_sha256":"845c184c080c3ef6d648e3bc7595169019671ac5d99d734bf533918034cda1d9","source":{"kind":"arxiv","id":"2601.04633","version":2},"attestation_state":"computed","paper":{"title":"MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anyang Song, Rui Feng, Ying Cheng, Yiqian Xu","submitted_at":"2026-01-08T06:07:07Z","abstract_excerpt":"Machine-Generated Text (MGT) is becoming increasingly difficult to distinguish from Human-Written Text (HWT). This trend has exacerbated malicious activities such as fake news and online fraud. The generalization ability of fine-tuned detectors relies heavily on dataset quality, and simply expanding the sources of MGT may become increasingly insufficient. Further augmentation of the generation process is required. Based on HC-Var's theory, enhancing the human-like alignment of MGT not only facilitates robustness testing of existing detectors but also boosts the generalization ability of detect"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2601.04633","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-01-08T06:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"50b10a20c353743c09bd57ec0bbc3e8bc287398cd1a79c4011aade92b58e50b1","abstract_canon_sha256":"dad9661872f494e0ba713735a8da2034998a834c24e8d29c86c3819f1ba7c9ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:01.489873Z","signature_b64":"7eJM3pycIZUkC/5dUkFEgd8EF9hDDOo0cVWkodWUqzkOYKkj5C75mLL8X7Gbm26tI5bi3Hk2mLgz4JBEVy8lCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"845c184c080c3ef6d648e3bc7595169019671ac5d99d734bf533918034cda1d9","last_reissued_at":"2026-05-29T01:05:01.489070Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:01.489070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anyang Song, Rui Feng, Ying Cheng, Yiqian Xu","submitted_at":"2026-01-08T06:07:07Z","abstract_excerpt":"Machine-Generated Text (MGT) is becoming increasingly difficult to distinguish from Human-Written Text (HWT). This trend has exacerbated malicious activities such as fake news and online fraud. The generalization ability of fine-tuned detectors relies heavily on dataset quality, and simply expanding the sources of MGT may become increasingly insufficient. Further augmentation of the generation process is required. Based on HC-Var's theory, enhancing the human-like alignment of MGT not only facilitates robustness testing of existing detectors but also boosts the generalization ability of detect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.04633","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/2601.04633/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2601.04633","created_at":"2026-05-29T01:05:01.489198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.04633v2","created_at":"2026-05-29T01:05:01.489198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.04633","created_at":"2026-05-29T01:05:01.489198+00:00"},{"alias_kind":"pith_short_12","alias_value":"QROBQTAIBQ7P","created_at":"2026-05-29T01:05:01.489198+00:00"},{"alias_kind":"pith_short_16","alias_value":"QROBQTAIBQ7PNVSI","created_at":"2026-05-29T01:05:01.489198+00:00"},{"alias_kind":"pith_short_8","alias_value":"QROBQTAI","created_at":"2026-05-29T01:05:01.489198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA","json":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA.json","graph_json":"https://pith.science/api/pith-number/QROBQTAIBQ7PNVSI4O6HLFIWSA/graph.json","events_json":"https://pith.science/api/pith-number/QROBQTAIBQ7PNVSI4O6HLFIWSA/events.json","paper":"https://pith.science/paper/QROBQTAI"},"agent_actions":{"view_html":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA","download_json":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA.json","view_paper":"https://pith.science/paper/QROBQTAI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.04633&json=true","fetch_graph":"https://pith.science/api/pith-number/QROBQTAIBQ7PNVSI4O6HLFIWSA/graph.json","fetch_events":"https://pith.science/api/pith-number/QROBQTAIBQ7PNVSI4O6HLFIWSA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA/action/storage_attestation","attest_author":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA/action/author_attestation","sign_citation":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA/action/citation_signature","submit_replication":"https://pith.science/pith/QROBQTAIBQ7PNVSI4O6HLFIWSA/action/replication_record"}},"created_at":"2026-05-29T01:05:01.489198+00:00","updated_at":"2026-05-29T01:05:01.489198+00:00"}