MemGPT (MemGPT)

LLM in a Flash: Efficient Large Language Model Inference with Limited Memory

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks…

Large language model

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots.[1] Biased or inaccurate training data can make an LLM's output less reliable.[2]

MemGPT

Memory-GPT (MemGPT) - Towards LLMs as Operating Systems - Teach LLMs to manage their own memory for unbounded context!

Scaling Laws Across Model Architectures: A Comparative Analysis of...

The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment. Our work investigates the transferability and...

Beyond Model Collapse: Scaling Up with Synthesized Data Requires...

Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because synthetized data is...

Context Rot: How Increasing Input Tokens Impacts LLM Performance

Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks. In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows.

LLMs and World Models, Part 1

How do Large Language Models Make Sense of Their “Worlds”?

The Case Against LLMs as Rerankers

Authors: Apoorva Joshi, Zhenmei Shi, Akshay Goindani, Hong LiuResearch Leads: Zhenmei Shi, Akshay Goindani, Hong Liu Large language models are increasingly being used for a broad range of tasks, in…

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...

Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...

Understanding memory management

Learn MemGPT memory management techniques for controlling LLM context windows with in-context and external storage.

Wikipedia:LLM-assisted translation

This guideline applies to machine translation tools that include a large language model ("LLM"). Assume that it applies to any online translation tool unless you have confirmed there is no LLM element.

Hypothesis generation and updating in large language models

Large language models (LLMs) increasingly help people solve problems, from debugging code to repairing machinery. This process requires generating plausible hypotheses from partial descriptions,...

I Benchmarked OpenAI Memory vs LangMem vs Letta (MemGPT) vs Mem0 for Long-Term Memory: Here’s How They Stacked Up

145 votes, 53 comments. Lately, I’ve been testing memory systems to handle long conversations in agent setups, optimizing for: Factual consistency…

On the Way to LLM Personalization: Learning to Remember User Conversations

This paper was accepted at the Workshop on Large Language Model Memorization (L2M2) 2025. Large Language Models (LLMs) have quickly become…

How To Scale Your Model

Training LLMs often feels like alchemy, but understanding and optimizing the performance of your models doesn't have to. This book aims to demystify the science of scaling language models: how TPUs (and GPUs) work and how they communicate with each other, how LLMs run on real hardware, and how to parallelize your models during training and inference so they run efficiently at massive scale. If you've ever wondered “how expensive should this LLM be to train” or “how much memory do I need to serve this model myself” or “what's an AllGather”, we hope this will be useful to you.