Original Summary

I've been building Pythos, a free AI mathematics & physics tutor, and I wanted to solve a problem that bothered me with AI tutors: What happens when the model is confidently wrong? Instead of treating the LLM's response as the answer, I built Pythos around a different architecture: LLM proposes → deterministic systems verify → revise if necessary → verify again → delivery gate The LLM handles the conversational reasoning and explanation. It does not get the final say on whether its mathematical claims are acceptable. The verification pipeline currently uses: • Math.js for deterministic numerical/expression verification • SymPy for symbolic mathematics • A proprietary Pythos verification/orchestration layer • Prompt-to-claim fidelity checking • Revision + re-verification • Deterministic supremacy when the LLM and verified mathematics disagree • A delivery gate that can safely withhold an answer instead of guessing I recently ran my largest blind validation campaign: 110,000 fresh problems 110,000 verified correct 0 incorrect answers delivered 0 verification escapes 0 false-positive rejections 100% verified-correct 10,000 problems were generated for each of 11 domains, including arithmetic, fractions, linear equations, systems, quadratics, functions, geometry/trig, calculus, probability/statistics, and physics. I also separately tested things like vision problems, multi-turn conversations, adversarial/kid-safety cases, backup-model recovery, and the verification pipeline itself. The interesting part isn't really the 100% number by itself. It's the architecture behind it. I'm deliberately not claiming that Pythos is mathematically infallible. A benchmark can only demonstrate what was tested. The goal is to make an incorrect answer difficult to deliver, rather than simply making the model more confident. The project is currently free to use, and I'm building it as an education project because I'd like something like this to actually be useful to students rather than just another chatbot wrapper. I'd particularly be interested in feedback from other developers: How would you approach testing/verifying an LLM that is being used for mathematical reasoning? What failure modes would you attack next? Pythos: https://pythos.lanzar.me/ Validation evidence: https://pythos.lanzar.me/validation/   submitted by   /u/DMF-GRiM [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/25 08:00:44