OpenAI contacted me to say “Study Mode is still live and accessible via /study and /learn shortcuts” so that’s good, although the official study mode page doesn’t mention that. (I don’t think slash commands are a natural thing for the vast majority of people).
RESEARCH
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Debating Geoffrey Hinton’s Perspective on LLM Memorization Processes
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this quote does not mean the same thing. Hinton is trying to saddle me with saying the memorization is the only operative process and I never said that and don’t say it in this quote. there is not “that is all” here putting together bits of text is not the same pure
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Technical Analysis of Attention Drift in Speculative Decoding Models
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“Attention Drift: What Autoregressive Speculative Decoding Models Learn” Speculative decoding makes LLM inference faster, but drafters break under small template changes and long context. But why? This paper shows that as the drafter predicts more tokens, its attention drifts
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Improving LLM Embedding Representations via Mean-Pooling of Generated Tokens
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“The Truth Lies Somewhere in the Middle of the Generated Tokens” LLMs don’t store the meaning of a prompt in one hidden state. As they generate, the meaning gets spread across many token embeddings. So this paper propose a mean-pool over generated token embeddings instead of
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Subquadratic Unveils New SubQ Model Using Subquadratic Sparse Attention
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What if LLMs could read a million tokens without exploding in cost? Subquadratic, an AI research startup, just unveiled SubQ — the first model built on fully subquadratic sparse attention (SSA). Instead of comparing every token pair, SSA routes attention only to the truly
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LLMs Memorization vs Overfitting Clarification
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Memorization does not imply overfitting. Overfitting is strictly about what happens on non-training data. So, e.g., just because LLMs memorize data doesn’t make them stochastic parrots. What matters is what they do with it, and they typically paraphrase it quite appropriately.
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Memorization vs Overfitting in Large Language Models
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Memorization does not imply overfitting. Overfitting is strictly about what happens on non-training data. So, e.g., just because LLMs memorize data doesn’t make them stochastic parrots. What matters is what they do with it, and they typically paraphrase it quite appropriately.
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DeepMind’s AI pointer understands context and responds to voice
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Google DeepMind just reinvented the mouse pointer.
— Chubby♨️ (@kimmonismus) 12 mai 2026
Since Doug Engelbart's demo in 1968, the little arrow on your screen has barely changed. Until now.
The new AI pointer sees what you're pointing at, understands the context, and responds to your voice. You point at an image of… https://t.co/z9cNgtMvOMGoogle DeepMind just reinvented the mouse pointer. Since Doug Engelbart's demo in 1968, the little arrow on your screen has barely changed. Until now. The new AI pointer sees what you're pointing at, understands the context, and responds to your voice. You point at an image of