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PAMIT’s new research paper talks about Recursive Language Models. Giving a new perspective to AI’s memory issue. #ai #education #tech #learn #science
@parthknowsai
#Recursive Language Models
شاهد فيديو ريلز عن Recursive Language Models من أشخاص حول العالم.
شاهد بشكل مجهول دون تسجيل الدخول.
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ريلز رائجة
(12)
413.1K
ARThe AI memory problem might finally be solved.
MIT just dropped a method that lets GPT-5 handle 10M+ tokens without RAG or massive context windows.
It’s called Recursive Language Models — and instead of force-feeding AI entire documents, it teaches them to search, slice, and recurse like a programmer.
No retraining. No fine-tuning.
Just a Python REPL and smarter inference.
The result?
AI that navigates knowledge instead of drowning in it.
Think RAG is done? Drop your take in the comments.
PS - If you’re still reading, you’re a champ!
#ai #artificialintelligence #chatgpt #technews #mit
@arnitly
63.6K
AKRecursive Language Models (RLMs) introduce a powerful idea: let models recursively reason over long inputs instead of being limited by context windows.
By treating text as an external environment they can inspect and decompose, RLMs handle dramatically longer inputs with better efficiency.
A promising step toward scalable long-context AI without brute-force token expansion. #ai #RecursiveLanguageModel
@akshatharshetty
12.0K
RAContext engineering works, until it doesn’t. Recursive Language Models ask a sharper question: why are humans managing memory, search, and pruning at inference time? RLMs let the model operate over context with code, not tokens. The Bitter Lesson, applied to inference. arXiv:2512.24601v1
@rajistics
21.4K
GELarge Language Models (LLMs) such as ChatGPT are built to process and generate human language by modeling the statistical structure of text. They take in input text, break it into smaller units called tokens (which may represent words, pieces of words, or characters), and convert these tokens into numerical vectors through an embedding layer.
These token embeddings are then passed through multiple layers of a transformer architecture, which is central to the model’s ability to understand context and generate coherent output.
Within each transformer layer, the most important component is the self-attention mechanism. Self-attention allows the model to assign different weights to different tokens in the input sequence based on their relevance to each other, regardless of their position.
This is crucial for capturing relationships between words across long distances in the text. After the attention mechanism, the token representations are processed by a multi-layer perceptron (MLP), which applies nonlinear transformations to help the model learn more complex patterns. By stacking many such layers, LLMs like ChatGPT can build deep contextual representations that allow them to generate fluent and relevant text, one token at a time.
C: @3blue1brown
#datascientist #computerengineering #deeplearning #computerscience #math #mathematics #ml #logisticregression #machinelearning #datascience #education #coding #programming #learning #courses #bootcamp #course
@getintoai
82.5K
AILarge Language Models (LLMs), such as GPT-4, use deep learning to understand and generate human-like text.
When predicting the next word in a sentence, they output a probability distribution over a vast vocabulary. This distribution assigns a likelihood to each possible word, reflecting how likely each word is to come next based on the given context.
The model uses patterns learned from vast amounts of text data to make these predictions.
C: @3blue1brown
Join our AI community for more posts like this @aibutsimple 🤖
#gpt #chatgpt #deeplearning #datascience #machinelearning
@aibutsimple
36.8K
DIRecursive Language Model is a paper by Alex L Zhang . Hype around the paper is expected as this is a very unique approach but Claude code kinda works same ..this is just agentic with some extra unique steps ..Very interesting tho give it a try !
[ai dev , development , RLM , LLMS , context engineering , engineering , agentic rag , godsplan , artificial intelligence , ai summit , research paper , recursive language model]
@divyaanshiee7
441.4K
5ASmall language models will be just as important a large language models. Here’s why
@5aitec
4.5K
HAIf you want to learn large language models properly, this book is the most complete resource I’ve found. In this video, I break down exactly what’s inside, what you’ll learn, and why the free GitHub repository that comes with it makes it even better.
The book takes you from the ground up tokenization, embeddings, transformer architecture all the way through prompt engineering and hands-on projects like semantic search and building a RAG model from scratch. Every chapter has a corresponding code notebook in the GitHub repo that opens directly in Google Colab, so you can code along as you learn.
Whether you’re coming from a machine learning background or just getting serious about AI development, this is the resource that fills the gap between theory and actually building with LLMs.
Comment “BOOK” on the video and I’ll send you the link directly.
#llm #machinelearning #learnai #datascience #promptengineering
@hasantoxr
149.1K
HETransformer-based language models use a self-attention mechanism where each token attends to every other token in the input, leading to a computational cost that grows quadratically with the number of tokens. To keep inference tractable, these models operate within a fixed-size context window and can only attend to tokens inside that window. As conversations grow longer, older tokens fall outside the context window and are no longer accessible to the model, which can appear as the model “forgetting” earlier information.
@heydevanand
424.3K
CO#patterns #patterndesign #aiprogramming #patternmaking #cpptutorial #cppcompiler #javafullstack #javafullstackdeveloper #javainterviewprep #javacoding #javaprogramming #JavaDeveloper #java
@computer_science_engineers
30.7K
KEThe whitepaper states that RLM is “general inference strategy that treats long prompts as
part of an external environment and allows the LLM to programmatically examine,
decompose, and recursively call itself over snippets of the prompt.”
#whitepapers #aiml #mitresearch #softwaredevelopers
@keerti.purswani
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