{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XWCVBZB2F5VRRQDVO2IOWJKOVK","short_pith_number":"pith:XWCVBZB2","schema_version":"1.0","canonical_sha256":"bd8550e43a2f6b18c0757690eb254eaa8f3e8e34faca5f7bc06622bf93d54cf4","source":{"kind":"arxiv","id":"2412.16525","version":2},"attestation_state":"computed","paper":{"title":"One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alberto Bacchelli, Bohan Liu, He Zhang, Jinwei Xu, Jun Lyu, Lanxin Yang, Thiam Kian Chiew, Xin Zhou, Yanjing Yang, Yin Kia Chiam, Zeru Cheng","submitted_at":"2024-12-21T08:02:58Z","abstract_excerpt":"Large language model-generated code (LLMgCode) has become increasingly common in software development. So far LLMgCode has more quality issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully examined. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on detection accuracy. Yet it is unclear whethe"},"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":"2412.16525","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-12-21T08:02:58Z","cross_cats_sorted":[],"title_canon_sha256":"aa5c23eb92eb8ab06e7c33fe0a61bc2a08ea8fac08b9935989f398f1884706c5","abstract_canon_sha256":"5b98d49ef3ed92570564f1ab0fb76ae07d890bbd549947e2e860d69ca4a9eba5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:14.186025Z","signature_b64":"QH/pCZP3kHfHdi1IjXoCLM6Onz+x4m7Z1dDdQhOtpbcqHw975B/kvoMWQ1M/mcXCmuGRfLnstoA0wqj3wUEwDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd8550e43a2f6b18c0757690eb254eaa8f3e8e34faca5f7bc06622bf93d54cf4","last_reissued_at":"2026-07-05T11:37:14.185537Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:14.185537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Alberto Bacchelli, Bohan Liu, He Zhang, Jinwei Xu, Jun Lyu, Lanxin Yang, Thiam Kian Chiew, Xin Zhou, Yanjing Yang, Yin Kia Chiam, Zeru Cheng","submitted_at":"2024-12-21T08:02:58Z","abstract_excerpt":"Large language model-generated code (LLMgCode) has become increasingly common in software development. So far LLMgCode has more quality issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully examined. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on detection accuracy. Yet it is unclear whethe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16525","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/2412.16525/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":"2412.16525","created_at":"2026-07-05T11:37:14.185597+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.16525v2","created_at":"2026-07-05T11:37:14.185597+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16525","created_at":"2026-07-05T11:37:14.185597+00:00"},{"alias_kind":"pith_short_12","alias_value":"XWCVBZB2F5VR","created_at":"2026-07-05T11:37:14.185597+00:00"},{"alias_kind":"pith_short_16","alias_value":"XWCVBZB2F5VRRQDV","created_at":"2026-07-05T11:37:14.185597+00:00"},{"alias_kind":"pith_short_8","alias_value":"XWCVBZB2","created_at":"2026-07-05T11:37:14.185597+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/XWCVBZB2F5VRRQDVO2IOWJKOVK","json":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK.json","graph_json":"https://pith.science/api/pith-number/XWCVBZB2F5VRRQDVO2IOWJKOVK/graph.json","events_json":"https://pith.science/api/pith-number/XWCVBZB2F5VRRQDVO2IOWJKOVK/events.json","paper":"https://pith.science/paper/XWCVBZB2"},"agent_actions":{"view_html":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK","download_json":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK.json","view_paper":"https://pith.science/paper/XWCVBZB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.16525&json=true","fetch_graph":"https://pith.science/api/pith-number/XWCVBZB2F5VRRQDVO2IOWJKOVK/graph.json","fetch_events":"https://pith.science/api/pith-number/XWCVBZB2F5VRRQDVO2IOWJKOVK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK/action/storage_attestation","attest_author":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK/action/author_attestation","sign_citation":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK/action/citation_signature","submit_replication":"https://pith.science/pith/XWCVBZB2F5VRRQDVO2IOWJKOVK/action/replication_record"}},"created_at":"2026-07-05T11:37:14.185597+00:00","updated_at":"2026-07-05T11:37:14.185597+00:00"}