Joined June 2026
Sid Patllollu retweeted
Autoresearch Bench is our first attempt at measuring a model's ability to make meaningful progress on research problems over extended time horizons. Very excited to get this out, and so proud of the hard work from our team!
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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Sid Patllollu retweeted
It was very interesting seeing the different behaviors for long-horizon tasks: - GPT-5.6 and Grok iterated fast & often - Opus liked to make larger, more substantial changes We'll be sharing the 12 full hour runs soon! Certain models even plan according to how much time they're told they have
incredible set of tasks and pretty much long horizon has very long way to go across domains.
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Sid Patllollu retweeted
This is pretty cool - though I am curious about what happens with Fable 5.1's classifiers on this benchmark due to Ant's previous discussions regarding ML tasks. It seems like their latest classifiers stuff is focused more on bio and cyber sec, but yeah.
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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Sid Patllollu retweeted
some really good tasks here. i had an opportunity to look at some of these earlier. benchmaxxing these should lead to better models for sure.
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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Sid Patllollu retweeted
incredible set of tasks and pretty much long horizon has very long way to go across domains.
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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So much fun working on this, proud of the team!
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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Sid Patllollu retweeted
Multiple companies are betting on automating the experimental loop. But which model is the best? - Opus 5 is the best overall - Grok 4.6 is second and efficient
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Sid Patllollu retweeted
Announcing Autoresearch Bench A benchmark for coding agents autonomously tackling research problems While existing coding benchmarks are saturating quickly, the difference between models in autoresearch loops is stark
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Sid Patllollu retweeted
At @aiDotEngineer, we spoke about the unique challenges we're tackling at @emulated_ai: - Simulation of infra at scale - Novel research - Sandbox infra for clouds youtube.com/watch?v=zkX03APV…
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Sid Patllollu retweeted
i made some updates to pavlov’s list and am pleased with them! thanks to @xeophon @ybenpan @phoebeyao @amit05prakash @kate_shapova @timshi_ai @kevinhou22 @madiator @dlbydq @daljeet_v @ingmariusX @davidstutz92 & Dylan Rogers for feedback lately (pavlov's = list of rl env cos)
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Sid Patllollu retweeted
Proud to be featured and have @chrisbarber as our angel! He was one of the first to back our work automating infra and MLE. More to come soon!
i made some updates to pavlov’s list and am pleased with them! thanks to @xeophon @ybenpan @phoebeyao @amit05prakash @kate_shapova @timshi_ai @kevinhou22 @madiator @dlbydq @daljeet_v @ingmariusX @davidstutz92 & Dylan Rogers for feedback lately (pavlov's = list of rl env cos)
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