Steven Dillmann ✈️ ICML 2026

210 posts

Steven Dillmann ✈️ ICML 2026 banner
Steven Dillmann ✈️ ICML 2026

Steven Dillmann ✈️ ICML 2026

@StevenDillmann

Stanford PhD working on #AI4Science and maintaining Terminal-Bench Science @StanfordAILab 🧬🤖🪐

Stanford, CA Katılım Ocak 2020
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Steven Dillmann ✈️ ICML 2026
Wonderful to see so many people reaching out to contribute to Terminal-Bench Science after my contribution call at the @icmlconf AI for Physics Workshop in Seoul! #ICML2026⚛️🇰🇷
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Ethan Hersch
Ethan Hersch@EthanHersch·
🚨🚨Announcing CertJudge🚨🚨 But who judges the judge? … New work from Stanford University, Harvard University, Hong Kong Baptist University
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Rylan Schaeffer
Rylan Schaeffer@RylanSchaeffer·
This was my last week at TBD / @Meta Superintelligence Labs (MSL) It's been an incredible experience watching the lab assemble and accelerate. I learned an enormous amount and had the privilege of working with some of the best ML/AI researchers on the planet 1/2
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Steven Dillmann ✈️ ICML 2026
Come contribute to Terminal-Bench Science with your hardest scientific problems!
Ben Blaiszik@BenBlaiszik

I've been working on a mechanics of materials benchmark for the Terminal-Bench Science effort led by @StevenDillmann, and I'm genuinely shocked how hard the problems have to be for the agents to fail. Deep, PhD-level capabilities in mechanics are already included in the capabilities of GPT 5.5 and Opus 4.8, and are especially accessible via web search and agent code implementation. No details on the mechanics benchmark yet to avoid leak into the training set. But, I would have loved to have had access to this for my own work :)

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Steven Dillmann ✈️ ICML 2026
Steven Dillmann ✈️ ICML 2026@StevenDillmann·
Just landed in Seoul for #ICML2026 🇰🇷 Reach out if you want to chat about Terminal-Bench Science, AI for Science or anything else!
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Alex Shaw
Alex Shaw@alexgshaw·
An under-appreciated aspect of Harbor is that results are auditable and reproducible so you can make trust-less claims about your agent or model. In the era of SWE, code was the source of truth so OSS built trust. In the era of agents, evals are the source of truth and reproducible evals build trust.
Monk Zero@NoCommas

@alexgshaw @lakshyaag @harborframework It is a good framework. We use it to benchmark all releases we shipped (Public ones at antigma.ai/eval with HarborHub links as provenance)

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