{"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=Qwen2.5&bench=AIME","json_url":"https://assignee.net/benchmarks/evidence.json?model=Qwen2.5&bench=AIME","model":"Qwen2.5","benchmark":"AIME","source_count":3,"source_coverage":{"record_count":3,"distinct_source_count":3,"coverage_level":"MODERATE","basis":"distinct public paper URLs or titles in this evidence cluster"},"source_profile":{"source_url_count":3,"missing_source_url_count":0,"domains":["arxiv.org"],"year_min":2025,"year_max":2026,"basis":"public source URLs, source titles, and reported publication years in this evidence cluster"},"reported_range":{"min_score_pct":27.5,"max_score_pct":45.6},"spread_pp":18.1,"severity":"HIGH","entries":[{"model":"Qwen2.5","benchmark":"AIME","score_pct":45.65,"source_title":"MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning","source_url":"http://arxiv.org/abs/2601.17006v1","source_domain":"arxiv.org","year":2026},{"model":"Qwen2.5","benchmark":"AIME","score_pct":42.79,"source_title":"LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs","source_url":"http://arxiv.org/abs/2510.16552v1","source_domain":"arxiv.org","year":2025},{"model":"Qwen2.5","benchmark":"AIME","score_pct":27.5,"source_title":"SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning","source_url":"http://arxiv.org/abs/2506.08989v1","source_domain":"arxiv.org","year":2025}],"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."]}