DropthePDF.Watchthepaperthink.
Prism refracts dense research into an interactive experience — animated diagrams, plain-language narration, and figures you can touch. Understand any paper in minutes, not weekends.
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The problem
Every breakthrough starts as 30 pages almost nobody finishes.
Dense notation. Buried results. Figures that assume you already understand them. Great ideas die inside PDFs — not because they're wrong, but because they're unreadable.
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Living figures
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Step-through mechanisms
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Featured papers
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Ask a big model to show its work — and it starts to reason
Big language models were surprisingly bad at multi-step problems — grade-school math, logic puzzles — often blurting a wrong final answer. This paper found a shockingly simple fix that requires no training at all: in the few examples you show the model (the “prompt”), don't just show it question-and-answer pairs — show it the *reasoning in between*. Write out the step-by-step thinking that leads to each answer. The model then imitates that pattern on new problems, producing its own “chain of thought” before answering — and accuracy leaps. The headline result: prompting a 540-billion-parameter model with just eight worked examples hit 56.9% on the GSM8K math benchmark — up from 17.9% with ordinary prompting — beating the previous best (a model specially fine-tuned for the task and paired with a separate verifier, at 55%). The twist: this only works in *large* models. In small ones, showing the steps does little or even hurts. Reasoning-by-prompting is an ability that *emerges* with scale — a discovery that reshaped how people use LLMs.
Attention Is All You Need
The Transformer — the architecture behind modern AI
Before this paper, the best translation systems read sentences one word at a time, carrying a running memory forward (recurrent networks). That was slow — you can't process word 5 until word 4 is done — and forgetful over long sentences. The 2017 Transformer threw out that sequential machinery entirely and replaced it with attention: a mechanism that lets every word look directly at every other word and decide which ones matter. Because nothing waits its turn, the model trains fully in parallel on GPUs, captures long-range relationships in a single step, and set new translation records while training in a fraction of the time. This design became the foundation of GPT, BERT, Claude, and essentially all modern AI.
LoopCoder-v2
Only loop once for efficient test-time computation scaling
Looped transformers make a model “think longer” by running the same block of layers over and over — reusing the same weights to get deeper computation for free. But each extra loop normally makes inference slower and hungrier for memory. The Parallel Loop Transformer (PLT) fixes that cost, so the real question becomes: how many loops actually help? Training a family of 7B code models from scratch (on 18 trillion tokens) with 1, 2, 3, and 4 loops, the authors find a surprising, sharply non-monotonic answer: two loops is the sweet spot. One extra loop lifts SWE-bench Verified from 43.0% to 64.4% — competitive with far larger models — but a third loop makes things worse, dropping to 27.6%. Their diagnostics explain why: the second loop does the real refinement, while later loops mostly add noise, and PLT's parallelism trick charges a small fixed “tax” every loop that eventually outweighs the shrinking benefit.
Fresh off the press
Recently added
BERT
Pre-training deep bidirectional transformers for language understanding
Before BERT, the strongest language models read text in one direction — left to right — so when understanding a word they could only use the words before it. BERT's insight was to read in both directions at once, letting every word draw on its full context, left and right. To train a model that way, the authors invented a clever fill-in-the-blank game: randomly hide 15% of the words and have the model guess them from everything around them (the Masked Language Model), plus a second task of judging whether one sentence naturally follows another. After this self-supervised “pre-training” on 3.3 billion words of books and Wikipedia — no human labels needed — the same model can be lightly “fine-tuned” for almost any language task by adding just one small output layer. This one recipe set new records on eleven benchmarks at once, pushing the GLUE score to 80.5% and SQuAD question-answering to a 93.2 F1.
Improving Language Understanding by Generative Pre-Training
GPT-1 — pre-train once, fine-tune for anything
In 2018, most language AI was built one task at a time: to do sentiment analysis you built a sentiment model, for question answering a question-answering model, each needing its own pile of hand-labeled examples — which are scarce and expensive. This paper (the original GPT) showed a better recipe. First, take a single Transformer and train it on a mountain of ordinary unlabeled text with one dead-simple goal: predict the next word, over and over, across 7,000+ books. That teaches it a surprising amount about grammar, facts, and reasoning — for free, no labels needed. Then, to specialize it for a real task, just keep that same model almost entirely unchanged and fine-tune it briefly on a small labeled set, feeding structured inputs (like a premise and hypothesis) as one plain sequence with a divider token. This one general model beat purpose-built, task-specific architectures on 9 of 12 benchmarks. It's the blueprint every GPT since has followed.
GPT-3
Language Models are Few-Shot Learners
People can learn a new task from just a couple of examples. If someone shows you two or three sentences and their French translations, you can usually translate the next one — no long training needed. Computers used to be bad at this. To teach a model a new task, you normally had to feed it thousands of labeled examples and retrain it. This paper asks a simple question: what if we just make the model really, really big? The authors built GPT-3, a text model with 175 billion “knobs” (parameters) — about 10 times bigger than anything like it before. Then they did something surprising: they gave it new tasks with only a few examples typed right into the prompt, and changed nothing inside the model. No retraining. It just read the examples and did the task. This is called few-shot learning, and GPT-3 turned out to be shockingly good at it — even at things like simple arithmetic and writing news articles people couldn't tell were fake.
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