Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research
The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and…
AGI Is Not Multimodal
The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture…
After Orthogonality: Virtue-Ethical Agency and AI Alignment
This essay argues that rational people don’t have goals, and that rational AIs shouldn’t have goals. Human actions are rational…
Forking-Sequences — Part II: Multi-Horizon Forecast Ensembling with Reduced Volatility
Based on: Potosnak, W., Wolff, M., Cao, M., Ma, R., Konstantinova, T., Efimov, D., Mahoney, M.W., Oreshkin, B., & Olivares,…
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads…
2026 BAIR Graduate Showcase
Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of…
Intelligence is Free, Now What? Data Systems for, of, and by Agents
... government of the people, by the people, for the people ... — Abraham Lincoln, Gettysburg Address (1863)
Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction
Overview of ABBEL compared to traditional recursive summarization. Beliefs replace the full interaction history as the agent’s working context, and…
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
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.
Faster Retrieval for Better, More Efficient RAG
Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query…