{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PCRWPFQ3BO7PHMNHL7X3RZKTUA","short_pith_number":"pith:PCRWPFQ3","schema_version":"1.0","canonical_sha256":"78a367961b0bbef3b1a75fefb8e553a011ce408cde85509f827b81c11cba503c","source":{"kind":"arxiv","id":"2110.12894","version":2},"attestation_state":"computed","paper":{"title":"The Efficiency Misnomer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anurag Arnab, Ashish Vaswani, Lucas Beyer, Mostafa Dehghani, Yi Tay","submitted_at":"2021-10-25T12:48:07Z","abstract_excerpt":"Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts. Although there are numerous well-established metrics (cost indicators) for measuring model efficiency, researchers and practitioners often assume that these metrics are correlated with each other and report only few of them. In this paper, we thoroughly discuss common cost indicators, their advant"},"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":"2110.12894","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-25T12:48:07Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV","stat.ML"],"title_canon_sha256":"6458d3957a14d14602f299bf1b64715c1a5dc2985b27dca057568f6c2f173094","abstract_canon_sha256":"75d53d80cda209ba40baa41c4fd2bd108962d53cac2a2ea4facab23dabbc4c50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:05:35.963326Z","signature_b64":"+eDqWf/cze2u3f+5NqpdNa1392sFgwVv+rF0MmrUwp2qB6GI8M4gDYjV5zXE/t3v2w3pUX/+lxg99Cg3+75bDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78a367961b0bbef3b1a75fefb8e553a011ce408cde85509f827b81c11cba503c","last_reissued_at":"2026-07-05T04:05:35.962903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:05:35.962903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Efficiency Misnomer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Anurag Arnab, Ashish Vaswani, Lucas Beyer, Mostafa Dehghani, Yi Tay","submitted_at":"2021-10-25T12:48:07Z","abstract_excerpt":"Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts. Although there are numerous well-established metrics (cost indicators) for measuring model efficiency, researchers and practitioners often assume that these metrics are correlated with each other and report only few of them. In this paper, we thoroughly discuss common cost indicators, their advant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.12894","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/2110.12894/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":"2110.12894","created_at":"2026-07-05T04:05:35.962958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.12894v2","created_at":"2026-07-05T04:05:35.962958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.12894","created_at":"2026-07-05T04:05:35.962958+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCRWPFQ3BO7P","created_at":"2026-07-05T04:05:35.962958+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCRWPFQ3BO7PHMNH","created_at":"2026-07-05T04:05:35.962958+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCRWPFQ3","created_at":"2026-07-05T04:05:35.962958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09401","citing_title":"Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models","ref_index":233,"is_internal_anchor":false},{"citing_arxiv_id":"2510.18091","citing_title":"Accelerating Vision Transformers with Adaptive Patch Sizes","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2211.17192","citing_title":"Fast Inference from Transformers via Speculative Decoding","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2407.21787","citing_title":"Large Language Monkeys: Scaling Inference Compute with Repeated Sampling","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA","json":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA.json","graph_json":"https://pith.science/api/pith-number/PCRWPFQ3BO7PHMNHL7X3RZKTUA/graph.json","events_json":"https://pith.science/api/pith-number/PCRWPFQ3BO7PHMNHL7X3RZKTUA/events.json","paper":"https://pith.science/paper/PCRWPFQ3"},"agent_actions":{"view_html":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA","download_json":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA.json","view_paper":"https://pith.science/paper/PCRWPFQ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.12894&json=true","fetch_graph":"https://pith.science/api/pith-number/PCRWPFQ3BO7PHMNHL7X3RZKTUA/graph.json","fetch_events":"https://pith.science/api/pith-number/PCRWPFQ3BO7PHMNHL7X3RZKTUA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA/action/storage_attestation","attest_author":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA/action/author_attestation","sign_citation":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA/action/citation_signature","submit_replication":"https://pith.science/pith/PCRWPFQ3BO7PHMNHL7X3RZKTUA/action/replication_record"}},"created_at":"2026-07-05T04:05:35.962958+00:00","updated_at":"2026-07-05T04:05:35.962958+00:00"}