Excited to release SimpleTES: a better open-sourced AlphaEvolve! With gpt-oss, SimpleTES discovers SOTA solutions across 21 tasks: quantum compilation, LASSO speedup, scaling law discovery, kenel optimization... Project: wizardquant.com/will/simplet… Code: github.com/wq-will/SimpleTES

📣 Announcing Terminal-Bench Science: benchmarking AI agents on real scientific workflows – now open for task contributions👇 tbench.ai/news/tb-science-an… @AnthropicAI, @OpenAI, and @GoogleDeepMind use Terminal-Bench to evaluate AI on coding tasks. We're now extending it to scientific workflows. 1/6🧵

Excited to see real-world CL is more and more popular and feasible!

Excited to share that SLD has been accepted to ICLR 2026! 🎉 Also: SLDBench is now integrated into Harbor (github.com/laude-institute/h…), so you can quickly run any agents × LLMs with minimal setup. It’s much lighter-weight than SWE-Bench / PaperBench. Give it a try!

Brilliant paper from Stanford Tsinghua Peking University Wizard Quant Shows an evolution style LLM agent can discover scaling laws that predict performance better than humans. The big deal is that it turns scaling law writing from slow expert guesswork into an automated search that can guide expensive training and fine tuning decisions. Scaling laws are simple formulas that guess how an LLM will do as it gets bigger, but experts still craft them by hand and they can fail in new settings. The authors build SLDBench from 5,000 or more past training runs, and each task asks for 1 formula that predicts well on larger, unseen runs. They propose SLDAgent, which keeps rewriting both the formula code and the parameter fitting code, testing each new version and keeping the best like an evolution loop. This helps because the formula and the fitting method depend on each other, so improving only 1 often gives shaky predictions. Across 8 tasks it beats human formulas on extrapolation, meaning prediction beyond the seen scale, and with GPT-5 its average R2 rises from 0.517 to 0.748. The payoff is practical because it helps pick learning rate (step size) and batch size (examples per update) with fewer sweeps, and it helps choose which pretrained model to fine tune from small trial runs.

We created a new AI4Science task: AI as the science. It’s interesting to observe AI scientist explore its own mechanisms in the future.

The Terminal-Bench paper is here! Read it to learn where frontier models still fail and the secrets of how we sourced hundreds of high quality environments from our open source community. 🧵

🤔Want a principled way to RL your diffusion model? Check Data-regularized Reinforcement Learning (DDRL)! Post-train @nvidia #Cosmos World Foundation models with a million GPU hours! 🤯 Novel formulation ➡️ Theoretically integrates SFT into RL ➡️ Robust to Reward Hacking 🛑 Details: research.nvidia.com/labs/dir… #DDRL #Diffusion #RL #NVIDIA #Cosmos

Check our new blog post in collaboration with Algorithmic SuperIntelligence Labs about using OpenEvolve to discover scaling laws: algorithmicsuperintelligence…