Settle: Learning When to Stop Reasoning

Hey everyone, I do research in neuro-symbolic AI, and like many of you, I was amazed by OpenAI’s recent formal proof of the 3D Navier-Stokes blow-up in Lean 4. Having an AI build a full mathematical proof that compiles with zero errors is a huge milestone for automated reasoning. The math is 100% valid. But out of curiosity, our team wanted to see what this solution would look like in the real world. If you map their solution to real water, the fluid would literally vaporize from friction at 0.7 nanometers, just picoseconds before hitting the mathematical singularity. In machine learning, we see this all the time: it is classic specification gaming . When an AI agent is given a strict goal, it will exploit any unconstrained loophole in the rules to solve the problem. In this case, the AI f

🤯 Harvard gave students an AI tutor and the results are wild What happens when you replace a traditional physics lesson with a custom AI tutor? Harvard tested exactly that and students using the AI system learned more than twice as much as those in an active-learning classroom, while spending less time studying. The experiment involved 194 undergraduates taking introductory physics. Students used a purpose-built AI tutor designed around learning science, rather than a general chatbot. The AI guided students with questions, explanations and reasoning checks instead of simply handing them answers. The median post-test score was 4.5 for the AI group, compared with 3.5 for the classroom group. Students also spent about 49 minutes with the AI tutor versus 60 minutes in the classroom. The really interesting part is that this wasn’t just “ChatGPT teaches physics.” The system was specifically engineered to behave like a personalized tutor, adapting explanations, challenging students with questions and checking their reasoning. The study was limited to two physics topics at Harvard, so it doesn’t prove AI can replace universities or teachers. Source. @aipost 🏴

OpenAI says Moonshot-linked operators used thousands of accounts to extract protected reasoning from its models for adversarial distillation. No encryption broken. No database compromised. Just systematic querying designed to make one model teach another. OpenAI says this is dangerous because competitors can reproduce capabilities without making the same investment in safety. Which is a serious security issue. But you have to appreciate the timing: after years of “we learned from the internet,” the frontier-model industry has reached the “please stop learning from us” phase. submitted by /u/Haunting_Ganache_850 [link] [comments]

pi-llama-skip-reasoning is an extension for the Pi.dev harness that forces a local llama.cpp model to stop reasoning and answer / act immediately. When you are deep into the ctx session and ask 27B a simple question about a fact or need a direct action, the model may still feel the urge to indulge in copious deliberation in the reasoning trace. This extension allows the user to force the model to snap out of the reasoning stage and provide the answer immediately. Disclaimer: don't skip the reasoning for important problem-solving, that would hurt quality. This uses the same mechanism the llama.cpp web interface uses to skip reasoning , so it's native to llama.cpp, this extension is meant for Pi.dev yet the same mechanism could work for other harnesses. Usage : /skip-reasoning command or sho