{
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      "title": "算法解读：RMM——TopK 列范数切片：公式、1B~70B 实测与注意力/MLP 不对称性",
      "url": "/posts/deep-code-rmm/",
      "permalink": "https://hackcv.com/posts/deep-code-rmm/",
      "date": "2026-08-23",
      "author": "hackcv",
      "description": "RMM 完整拆解：收缩维 TopK 列范数选择算法、minimax 最优性证明、retention-ratio 权衡；8 个基准 × 4 档保留比实测、注意力 vs MLP 不对称数据、A100 端到端 1.40× 加速。",
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    {
      "title": "算法解读：SkillForge——四步合成 issue + 双层技能库，SWE-bench 实测 +5.8%",
      "url": "/posts/deep-code-skillforge/",
      "permalink": "https://hackcv.com/posts/deep-code-skillforge/",
      "date": "2026-08-23",
      "author": "hackcv",
      "description": "SkillForge 完整拆解：strict-mask 合成 issue 四步流程、全局诊断/局部干预双层技能库、BM25+JIT 两阶段检索；SWE-bench Verified 72.2%（+5.8%）、Pro 34.1%（+5.8%）。",
      "categories": ["研究简报"],
      "tags": ["AI","Agent","技能蒸馏","SkillForge","算法解读"],
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