{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QA3WQKMR3YF4N7736C7QR24VFH","short_pith_number":"pith:QA3WQKMR","schema_version":"1.0","canonical_sha256":"8037682991de0bc6fffbf0bf08eb9529d263cdc5035a124697aa78ba1e72f9db","source":{"kind":"arxiv","id":"2411.07529","version":1},"attestation_state":"computed","paper":{"title":"Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Bhaskar Krishnamachari, Minda Li","submitted_at":"2024-11-12T04:01:09Z","abstract_excerpt":"ChatGPT and other large language models (LLMs) promise to revolutionize software development by automatically generating code from program specifications. We assess the performance of ChatGPT's GPT-3.5-turbo model on LeetCode, a popular platform with algorithmic coding challenges for technical interview practice, across three difficulty levels: easy, medium, and hard. We test three main hypotheses. First, ChatGPT solves fewer problems as difficulty rises (Hypothesis 1). Second, prompt engineering improves ChatGPT's performance, with greater gains on easier problems and diminishing returns on h"},"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":"2411.07529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-11-12T04:01:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"739c8747d6e7436c2031a275152cf4fb61df906c8ec8ef69593097e9b93539b0","abstract_canon_sha256":"898c684512ec0a44b7f2ac1f7591af76817fdf7a53ef95aa53682b1ff9bc3856"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:25.095138Z","signature_b64":"gWC5ca0HUsoAq2kWATNHcPtustNzmyqphB6WMM1pDsC/1ifidPhjrdOMKcPEtfUHyeanHjH9BdXxV7x2j0p3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8037682991de0bc6fffbf0bf08eb9529d263cdc5035a124697aa78ba1e72f9db","last_reissued_at":"2026-07-05T09:34:25.094761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:25.094761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Bhaskar Krishnamachari, Minda Li","submitted_at":"2024-11-12T04:01:09Z","abstract_excerpt":"ChatGPT and other large language models (LLMs) promise to revolutionize software development by automatically generating code from program specifications. We assess the performance of ChatGPT's GPT-3.5-turbo model on LeetCode, a popular platform with algorithmic coding challenges for technical interview practice, across three difficulty levels: easy, medium, and hard. We test three main hypotheses. First, ChatGPT solves fewer problems as difficulty rises (Hypothesis 1). Second, prompt engineering improves ChatGPT's performance, with greater gains on easier problems and diminishing returns on h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07529","kind":"arxiv","version":1},"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/2411.07529/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":"2411.07529","created_at":"2026-07-05T09:34:25.094816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.07529v1","created_at":"2026-07-05T09:34:25.094816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07529","created_at":"2026-07-05T09:34:25.094816+00:00"},{"alias_kind":"pith_short_12","alias_value":"QA3WQKMR3YF4","created_at":"2026-07-05T09:34:25.094816+00:00"},{"alias_kind":"pith_short_16","alias_value":"QA3WQKMR3YF4N773","created_at":"2026-07-05T09:34:25.094816+00:00"},{"alias_kind":"pith_short_8","alias_value":"QA3WQKMR","created_at":"2026-07-05T09:34:25.094816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.15080","citing_title":"Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH","json":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH.json","graph_json":"https://pith.science/api/pith-number/QA3WQKMR3YF4N7736C7QR24VFH/graph.json","events_json":"https://pith.science/api/pith-number/QA3WQKMR3YF4N7736C7QR24VFH/events.json","paper":"https://pith.science/paper/QA3WQKMR"},"agent_actions":{"view_html":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH","download_json":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH.json","view_paper":"https://pith.science/paper/QA3WQKMR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.07529&json=true","fetch_graph":"https://pith.science/api/pith-number/QA3WQKMR3YF4N7736C7QR24VFH/graph.json","fetch_events":"https://pith.science/api/pith-number/QA3WQKMR3YF4N7736C7QR24VFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH/action/storage_attestation","attest_author":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH/action/author_attestation","sign_citation":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH/action/citation_signature","submit_replication":"https://pith.science/pith/QA3WQKMR3YF4N7736C7QR24VFH/action/replication_record"}},"created_at":"2026-07-05T09:34:25.094816+00:00","updated_at":"2026-07-05T09:34:25.094816+00:00"}