26 Mar 2026
#Machine learning3
26 Apr 2026
AI models encode real-world plausibility
Researchers at Brown University tested whether modern AI language models can tell if events are common, unlikely, impossible or nonsensical. They used mechanistic interpretability and found internal patterns that match human judgments in several open-source models.
Photo by Zach M, Unsplash
29 Dec 2025
New training method helps models do long multiplication
Researchers studied why modern language models fail at long multiplication and compared standard fine-tuning with an Implicit Chain of Thought (ICoT) method. ICoT models learned to store intermediate results and reached perfect accuracy.
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