Matthew Siegel @LargerLanguage
AI technical writer and poet. Not simultaneously. Research, warm takes, and cool stuff we're doing at @Scale_AI. For poems: @MatthewSiegel_ Joined December 2024-
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Interested in collaboration or using Scale’s Physical AI data engine? Contact us at [email protected]. Thanks @LargerLanguage for the help with the blog on Scale Labs!
Big things happening. SEAL is now Scale Labs. Congrats, @scale_AI and @ScaleAILabs teams!
Welcome to the home of all things @scale_AI research — focused on data, evaluation, safety, and post-training that moves frontier models forward. We’ll share benchmarks, insights, and work intended to be useful to the broader research community. labs.scale.com/?utm_source=hu…
People talk about “AI Risk” like it’s one giant shoggoth with a single scary face. It’s not. It’s six different monsters, each with its own speed, intent, and way of doing damage: • Adversaries • Unforced Errors • Misaligned Goals • Dependencies • Societal Impact • Emergent Behavior If we treat them like one blob, we’ll overreact to everything or prepare for nothing. I break them down — clearly — in my latest @scale_AI post with @LargerLanguage scale.com/blog/shoggoth-…
Lots of people are talking about an AI bubble. Sure. I don't think these people are reading the same research I'm reading.
New @Scale_AI paper! The culprit behind reward hacking? We trace it to misspecification in high-reward tail. Our fix: rubric-based rewards to tell “excellent” responses apart from “great.” The result: Less hacking, stronger post-training! arxiv.org/pdf/2509.21500
TutorBench helps provide a path to help LLMs better support student learning. Read more about it out on the Scale Blog! scale.com/blog/tutorbench
AI tutors are clearly not about to replace human tutors, and they have a lot of homework to do. But they also aren’t going anywhere.
If you want to get a sense of where AI is going, pay attention to what students are doing. Most of them, it turns out, are using AI for to help them learn. But are the models they’re using ready to replace human tutors? It turns out, not really.
This was a HUGE lift from our research team, huge thanks to everyone who contributed to this benchmark. ...and the blog doesn't look so bad either: scale.com/blog/swe-bench…
🚀 Introducing SWE-Bench Pro — a new benchmark to evaluate LLM coding agents on real, enterprise-grade software engineering tasks. This is the next step beyond SWE-Bench: harder, contamination-resistant, and closer to real-world repos.
Working on this leaderboard page and blog was a LIFT! Huge thank you to every cook in the kitchen!! 🧑🍳👩🍳👨🍳
Introducing our Agentic Leaderboards. These new leaderboards test AI agents in real-world, high-complexity environments, setting a new standard for completing end-to-end digital tasks.
Read more about this work on the @Scale_AI blog! scale.com/blog/smoothing…
What changed? Variance dropped by about 50% across dimensions, so true ~5% improvements show up again. A total game changer for agent evals.
Is your team using LLMs as judges to evaluate AI agents? You might want to take another look at that. When @Scale_AI researchers ran the same evaluation suite on back-to-back days, they saw metrics swing wildly.
Sanket Prajapati @SanketP33817397
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