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This correct answer-incorrect reasoning is also evidenced in the low score in the PutnamBench where the correct approach is rewarded, and in the recent IneqMath benchmark. x.com/lupantech/stat…

👀can your language model solve this inequality? 👋check out ineqmath, our new challenging benchmark containing 200 high-school olympiad inequalities, with leading models scoring under half! also fun for humans to try😝

[LG] Solving Inequality Proofs with Large Language Models J Sheng, L Lyu, J Jin, T Xia... [Stanford University & UC Berkeley] (2025) arxiv.org/abs/2506.07927

Wrapped up Stanford CS336 (Language Models from Scratch), taught with an amazing team Tatsunori Hashimoto Marcel Rød Neil Band Rohith Kuditipudi. Researchers are becoming detached from the technical details of how LMs work. In CS336, we try to fix that by having students build everything:

Today, I’m launching a deeply personal project. I’m betting $100M that we can help computer scientists create more upside impact for humanity. Built for and by researchers, including Jeff Dean & Joelle Pineau on the board, Laude Institute catalyzes research with real-world impact.

🎉Thrilled to receive the 2025 Google Research Scholar Award together with Zhengzhong Tu ! Grateful for the support. Stay tuned for our exciting work on privacy, safety, and security in multimodal LLMs!

Excited to share two advances that bring us closer to real-world impact in healthcare AI: SDBench introduces a new benchmark that transforms 304 NEJM cases into interactive diagnostic simulations. AI must ask questions, order tests, and weigh costs, mirroring the complexity of

🧵 1/ 🚀 Excited to share our latest work: Fractional Reasoning. We introduce a new way to continuously control the depth of reasoning and reflection in LLM for scaling test time compute, not just switch between “on” and “off” prompts. 💻 Website: shengliu66.github.io/fractreason/ #AI

Excited to share Fractional Reasoning, a new work led by Sheng Liu! By scaling a latent "reasoning vector," it continuously and reliably controls the reasoning intensity of LLMs at inference time. 📄 arxiv.org/abs/2506.15882 💻 shengliu66.github.io/fractreason/

Introducing Fractional Reasoning: a mechanistic method to quantitatively control how much thinking a LLM performs. tldr: we identify latent reasoning knobs in transformer embedding ➡️ better inference compute approach that mitigates under/over-thinking arxiv.org/pdf/2506.15882

Excited to share our recent work DreamPRM, a multi-modal LLM reasoning method achieving first place on the MathVista leaderboard. DreamPRM is an LLM-agnostic framework that can be applied to any multi-modal LLM for improving its reasoning capabilities. It is a bi-level

Congratulations, Prof. Zhou!🎉

I’ve joined AIX Ventures as a General Partner, working on investing in deep AI startups. Looking forward to working with founders on solving hard problems in AI and seeing products come out of that!  Thank you Yuliya Chernova at The Wall Street Journal for covering the news: wsj.com/articles/ai-re…

📢New conference where AI is the primary author and reviewer! agents4science.stanford.edu Current venues don't allow AI-written papers, so it's hard to assess the +/- of such works🤔 #Agents4Science solicits papers where AI is the main author w/ human advisors. 💡Initial reviews by

🚀 Excited to share that the Workshop on Mathematical Reasoning and AI (MATH‑AI) will be at NeurIPS 2025! 📅 Dec 6 or 7 (TBD), 2025 🌴 San Diego, California