{"schema":"https://assignee.net/schemas/benchmark-evidence-v1","schema_version":"1.0","contract_version":"benchmark-evidence-v1.0","contract_updated":"2026-06-01","schema_documentation":"https://assignee.net/schemas","changelog_url":"https://assignee.net/changelog","publisher":{"name":"Assignee Research","url":"https://assignee.net"},"html_url":"https://assignee.net/benchmarks/evidence?model=Phi-3&bench=GSM8K","json_url":"https://assignee.net/benchmarks/evidence.json?model=Phi-3&bench=GSM8K","model":"Phi-3","benchmark":"GSM8K","source_count":2,"source_coverage":{"record_count":2,"distinct_source_count":2,"coverage_level":"LIMITED","basis":"distinct public paper URLs or titles in this evidence cluster"},"source_profile":{"source_url_count":2,"missing_source_url_count":0,"domains":["arxiv.org"],"year_min":2024,"year_max":2026,"basis":"public source URLs, source titles, and reported publication years in this evidence cluster"},"reported_range":{"min_score_pct":73.5,"max_score_pct":74.5},"spread_pp":1.0,"severity":"NONE","entries":[{"model":"Phi-3","benchmark":"GSM8K","score_pct":74.5,"source_title":"SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models","source_url":"http://arxiv.org/abs/2403.07384v2","source_domain":"arxiv.org","year":2024},{"model":"Phi-3","benchmark":"GSM8K","score_pct":73.54,"source_title":"Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models","source_url":"http://arxiv.org/abs/2603.21389v1","source_domain":"arxiv.org","year":2026}],"interpretation":"This record groups score claims extracted from papers for the same model and benchmark label. A nonzero spread means the public literature reports different values for this cluster.","limitations":["Differences are not automatically errors.","Reported values may differ because of prompts, dataset versions, evaluation protocols, scoring rules, preprocessing, fine-tuning, or reporting conventions.","Source papers remain authoritative for their own claims."]}