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AI slop detectors: why filtering training data backfires
Directions in ML: Taking Advantage of Randomness in Expensive Optimization Problems
Why Randomize
Why rank-1 LoRA is enough for RL: Thinking Machines' research, explained
Benjamin Pierce: Backtracking Generators for Random Testing
Why Deep Learning Finally Works on Tables | Frank Hutter (Prior Labs)
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Last Updated: October 1, 2026
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Summary
I will illustrate the features of “ Welcome back to the Plausibly Deniable Experience. Lukas and Saila enter Soycast mode to reconstruct cooking from first ... Should you strip suspected AI-generated text out of a training set? The position here is no, not blind. Slop detectors are classifiers ... This video explains why it is important to Ask an LLM the same question at temperature 0 and you can still get different answers. Thinking Machines traced why, and in four ... QuickCheck-style property-based testing relies on In this episode, Frank Hutter joins us to talk about TabPFN and why tabular data is suddenly the hottest problem in deep learning.
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