AI High Signal

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Justin Lo @loresjpg · 01:37
Nice DeepSWE score! Does it survive a lowercase filename? Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement. Models clustered around 74% on DeepSWE end up 23 pp apart. Good
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Julian Schrittwieser @Mononofu a cité · 01:37
Making novel variations of evals is very useful, public evals tend to get memorized!
Hamel Husain @HamelHusain a cité · 01:16
I see some are drawing the wrong inference from this study: they are focusing on this meta benchmark as yet another benchmark for model picking, i.e. open vs closed models, model families etc. That’s too narrow of an interpretation! You need to do your own private evals on
Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex a cité · 08/11 22:00
holy shit, an art form of scientific grift next METR/Epoch/AA employee right here you all need to work on your epistemic hygiene
Ofir Press @OfirPress a cité · 08/11 21:26
GPT-5.6 Sol and Luna were built by roughly the same people using roughly the same infra, I don't think we can blame the 20 point difference in Luna just on memorization. There's something else being tracked here in addition to memorization. Maybe MaxxBench questions are slightly
Mat Velloso @matvelloso a cité · 07/11 21:20
I'm both surprised and also not at all
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Richard Sutton @RichardSSutton · 08/11 14:26
Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO
incompleteideas.net https://incompleteideas.net/IncIdeas/BitterLesson.html
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elie @eliebakouch a cité · 08/11 09:58
i disagree with the first point, there is nothing that says that scaling up current transformer recipe is not enough for "intelligence". you take kimi K3 or your favorite open arch + some stability fixes, you scale it to 50T parameters, you do classical training end to end, you
François Fleuret @francoisfleuret a cité · 08/11 07:44
We need a model that learns *in spite* of its inductive bias, not thanks to it. We have to stop being cross-entropy fetishists.
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M Saiful Bari (MARUF) @sbmaruf a cité · 08/11 07:02
True! This is the era of the RL objective. The biggest economy on this earth (US stock market) runs on subscriptions. Subscriptions require vendor lock-in. Currently, the frontier is pushing “Variance” learning from customer data, like how Google won the search market. But I
Denny Zhou @denny_zhou a cité · 08/11 06:26
The Bitter Lesson 2.0
Satya Mallick @LearnOpenCV a cité · 08/11 05:00
That’s what I call a bitter sense of humor.