What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning.
Read original on Berkeley AI Research (BAIR) ↗
Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.
Read original on Berkeley AI Research (BAIR) ↗
... government of the people, by the people, for the people ... — Abraham Lincoln, Gettysburg Address (1863)
Read original on Berkeley AI Research (BAIR) ↗
Overview of ABBEL compared to traditional recursive summarization. Beliefs replace the full interaction history as the agent’s working context, and belief grading improves performance by supervising the contents of each belief state..
Read original on Berkeley AI Research (BAIR) ↗
Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction.
Read original on Berkeley AI Research (BAIR) ↗
Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query.
Read original on Apple Machine Learning Research ↗
Genie 4: our new world model unlocking unprecedented realism in real-time simulation.
Read original on Google DeepMind Blog ↗
Gemini 4 Carbon is our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
Read original on Google DeepMind Blog ↗
Google DeepMind and UK government work together to reduce planning assessment times
Read original on Google DeepMind Blog ↗
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Read original on Google DeepMind Blog ↗
Angelica Willis, Research Engineer, Google Research, and Nichole Young-Lin, Clinical Lead, Google for Health
Read original on Google Research Blog ↗
Preeti Singh, Lead, Technical Program Manager, and Shravya Shetty, Distinguished Software Engineer, Google Research
Read original on Google Research Blog ↗
We introduce a sleep phase in continual learning, enabling large language models to continuously integrate new facts and enhance reasoning while mitigating catastrophic forgetting or static knowledge cutoffs.
Read original on Google Research Blog ↗
Otilia Stretcu, Research Scientist, and Leijie Wang, Student Researcher, Google Research
Read original on Google Research Blog ↗
Paolo Di Achille, Staff Research Scientist, and Ming-Zher Poh, Senior Staff Research Scientist, Google Research
Read original on Google Research Blog ↗
I’m not the sort of person who typically pays for a house cleaner. I’m too frugal, and I kind of enjoy putting on a podcast and going to town on my tub with a Magic Eraser. But when I saw an X post about a new humanoid-robot cleaning service in San Francisco, I immediately signed…
Read original on MIT Technology Review – AI ↗
OpenAI is partnering with Hacktron to find vulnerabilities, test connections with third-party services, and strengthen defenses with frontier AI.
Read original on OpenAI News ↗
OpenAI introduces BioBasicsBench, a 100-task benchmark measuring how well AI agents interpret raw experimental data and handle everyday wet-lab science.
Read original on OpenAI News ↗
