{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IO4ZESJ54XR7XFDTHQAB2FGLLU","short_pith_number":"pith:IO4ZESJ5","canonical_record":{"source":{"id":"2402.07844","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-12T17:53:22Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"f0750fef0643fd3dada7e794c5cb5272fdcaa7f93b744225465a529a02bf577c","abstract_canon_sha256":"b2ea03c31f057c670d4b3c3f7265ea05fc2bebd6ef557da8d56e4f222e49396f"},"schema_version":"1.0"},"canonical_sha256":"43b992493de5e3fb94733c001d14cb5d05894272107236254bfbbf1379894ce3","source":{"kind":"arxiv","id":"2402.07844","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.07844","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"arxiv_version","alias_value":"2402.07844v4","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07844","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_12","alias_value":"IO4ZESJ54XR7","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_16","alias_value":"IO4ZESJ54XR7XFDT","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_8","alias_value":"IO4ZESJ5","created_at":"2026-07-05T08:30:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IO4ZESJ54XR7XFDTHQAB2FGLLU","target":"record","payload":{"canonical_record":{"source":{"id":"2402.07844","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-12T17:53:22Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"f0750fef0643fd3dada7e794c5cb5272fdcaa7f93b744225465a529a02bf577c","abstract_canon_sha256":"b2ea03c31f057c670d4b3c3f7265ea05fc2bebd6ef557da8d56e4f222e49396f"},"schema_version":"1.0"},"canonical_sha256":"43b992493de5e3fb94733c001d14cb5d05894272107236254bfbbf1379894ce3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:30:08.521185Z","signature_b64":"CYBpL1FBj71mlQLdpY5P0isiLowA8Xhiiv5veTWSwZ27ZVwPiOV1aviZIVCvNggP605HwMz9OiP/khsmHdWXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43b992493de5e3fb94733c001d14cb5d05894272107236254bfbbf1379894ce3","last_reissued_at":"2026-07-05T08:30:08.520711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:30:08.520711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.07844","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:30:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ibhWBQDPu+pp+j5Awfhj3q3X3Rxd0sCDYJVzVV8PAYo5EBrSQGVR4RHVFqyrC6SQzC7z/shVeFlJdaj01wttBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:55:09.685658Z"},"content_sha256":"5830d47ff649fb7366989cce3aa911fa5c23e2f40bd5f905031233b5bcaf925f","schema_version":"1.0","event_id":"sha256:5830d47ff649fb7366989cce3aa911fa5c23e2f40bd5f905031233b5bcaf925f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IO4ZESJ54XR7XFDTHQAB2FGLLU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mercury: A Code Efficiency Benchmark for Code Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.SE","authors_text":"Anh Tuan Luu, Bin Ji, Mingzhe Du, Qian Liu, See-kiong Ng","submitted_at":"2024-02-12T17:53:22Z","abstract_excerpt":"Amidst the recent strides in evaluating Large Language Models for Code (Code LLMs), existing benchmarks have mainly focused on the functional correctness of generated code, neglecting the importance of their computational efficiency. To fill the gap, we present Mercury, the first code efficiency benchmark for Code LLMs. It comprises 1,889 Python tasks, each accompanied by adequate solutions that serve as real-world efficiency baselines, enabling a comprehensive analysis of the runtime distribution. Based on the distribution, we introduce a new metric Beyond, which computes a runtime-percentile"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07844","kind":"arxiv","version":4},"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/2402.07844/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:30:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sQp33aV1txjEqRylUaEvW4hHZ4Fksh9segfvIpPI+fwL7FFF4Qfy+Nt/OHxE19i3r7tqfznIO+LgHBKu4wnKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:55:09.686377Z"},"content_sha256":"268902ce826492768c92c5f8ee05010a1b1423535540afbfb32b8592ca2dafb7","schema_version":"1.0","event_id":"sha256:268902ce826492768c92c5f8ee05010a1b1423535540afbfb32b8592ca2dafb7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/bundle.json","state_url":"https://pith.science/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T19:55:09Z","links":{"resolver":"https://pith.science/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU","bundle":"https://pith.science/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/bundle.json","state":"https://pith.science/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IO4ZESJ54XR7XFDTHQAB2FGLLU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IO4ZESJ54XR7XFDTHQAB2FGLLU","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":"b2ea03c31f057c670d4b3c3f7265ea05fc2bebd6ef557da8d56e4f222e49396f","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-12T17:53:22Z","title_canon_sha256":"f0750fef0643fd3dada7e794c5cb5272fdcaa7f93b744225465a529a02bf577c"},"schema_version":"1.0","source":{"id":"2402.07844","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.07844","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"arxiv_version","alias_value":"2402.07844v4","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07844","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_12","alias_value":"IO4ZESJ54XR7","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_16","alias_value":"IO4ZESJ54XR7XFDT","created_at":"2026-07-05T08:30:08Z"},{"alias_kind":"pith_short_8","alias_value":"IO4ZESJ5","created_at":"2026-07-05T08:30:08Z"}],"graph_snapshots":[{"event_id":"sha256:268902ce826492768c92c5f8ee05010a1b1423535540afbfb32b8592ca2dafb7","target":"graph","created_at":"2026-07-05T08:30:08Z","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/2402.07844/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Amidst the recent strides in evaluating Large Language Models for Code (Code LLMs), existing benchmarks have mainly focused on the functional correctness of generated code, neglecting the importance of their computational efficiency. To fill the gap, we present Mercury, the first code efficiency benchmark for Code LLMs. It comprises 1,889 Python tasks, each accompanied by adequate solutions that serve as real-world efficiency baselines, enabling a comprehensive analysis of the runtime distribution. Based on the distribution, we introduce a new metric Beyond, which computes a runtime-percentile","authors_text":"Anh Tuan Luu, Bin Ji, Mingzhe Du, Qian Liu, See-kiong Ng","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-12T17:53:22Z","title":"Mercury: A Code Efficiency Benchmark for Code Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07844","kind":"arxiv","version":4},"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":"sha256:5830d47ff649fb7366989cce3aa911fa5c23e2f40bd5f905031233b5bcaf925f","target":"record","created_at":"2026-07-05T08:30:08Z","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":"b2ea03c31f057c670d4b3c3f7265ea05fc2bebd6ef557da8d56e4f222e49396f","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-02-12T17:53:22Z","title_canon_sha256":"f0750fef0643fd3dada7e794c5cb5272fdcaa7f93b744225465a529a02bf577c"},"schema_version":"1.0","source":{"id":"2402.07844","kind":"arxiv","version":4}},"canonical_sha256":"43b992493de5e3fb94733c001d14cb5d05894272107236254bfbbf1379894ce3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43b992493de5e3fb94733c001d14cb5d05894272107236254bfbbf1379894ce3","first_computed_at":"2026-07-05T08:30:08.520711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:30:08.520711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CYBpL1FBj71mlQLdpY5P0isiLowA8Xhiiv5veTWSwZ27ZVwPiOV1aviZIVCvNggP605HwMz9OiP/khsmHdWXDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:30:08.521185Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.07844","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5830d47ff649fb7366989cce3aa911fa5c23e2f40bd5f905031233b5bcaf925f","sha256:268902ce826492768c92c5f8ee05010a1b1423535540afbfb32b8592ca2dafb7"],"state_sha256":"15e43feae32801c727d55cb6234dffe06ee5928bc4f2a1749d7f0f13c649c086"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tzfmMoKtCCIMt5vPBAPshgQOenqBGhZQKH7UfvDhJXqO6zFgdmzQ2FGcvn+L4DJho7JGsJiU+riF/CWzVasFBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T19:55:09.695060Z","bundle_sha256":"2201b17ea6e0f3780b537d810feb5a81804d9d4dd6b6d93a2641e91017fa310e"}}