Original Summary

It started as an idea around finding where numerical/scientific computations stop being trustworthy. Now it has its own DSL, runtime, 5 evidence channels, adaptive search, Trust Atlas, counterexample minimisation, replayable experiment archives, run comparison, and a benchmark system. Currently: - 13 Rust crates - 622 tests - 22 benchmark problems - replayable/provenance-preserving runs - ablation + strategy experiments The interesting part is that the experiments didn't give me the result I initially expected. The adaptive search wasn't clearly better than the level-set baseline on the current corpus, so I'm not claiming a breakthrough there. I'm now more interested in the evidence + provenance/replayability side of it. Repo: https://github.com/bhogesararam23/aporia-engine Would love to hear what people think, especially anyone working with numerical/scientific computing or verification.   submitted by   /u/Rambhogesara [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/8 08:25:18