State of Open Models: Summer 2026 Observations
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
More from the blog
How mobility gives language models a deeper understanding of place
Algorithms & Theory
Measuring benchmark optimization in speech recognition
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
When AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Related papers
A comparative review of modern large language model paradigms: GPT-4, BERT, Gemini, and DeepSeek
Comparison of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing on architectures, training methodologies, and real-world applications reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI).
TestifAI: Tomography-Based Testing for Deep Learning Systems
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
LLMs Leak Training Data Beyond Verbatim Memorization: Extraction via Membership Decoding
The Membership Decoding method is a plug-and-play replacement for standard decoding that requires only black-box token probabilities, and a new token-level membership inference method is proposed by leveraging likelihood from reference models, shifting the generation from the original token distribution to the member token distribution.
A Multiagent Large Language Model–Based System for Early-Stage Building Layout Planning
A multiagent large language model (LLM)–based system for early-stage building layout planning, which enables flexible design requirement inputs and robust spatial reasoning and demonstrated significant improvements in both geometric quality and semantic alignment over a baseline LLM-only system.