preference embeddings for collective decisions
standard text embeddings capture semantics, not agreement, so new methods are needed to map free-text opinions for fair clustering and facility location.
topic
ai
thinking machines lab unveils ai that talks and listens at once
thinking machines lab announced interaction models, a full-duplex ai that processes input and generates responses simultaneously, aiming for natural conversation speed.
rl-kirigami speeds inverse design of shape-shifting structures
a new framework combines flow matching and reinforcement learning to quickly generate valid kirigami cut patterns for target shapes.
pathboost: gradient boosting learns graph features from paths
pathboost is a gradient tree boosting method for graph-level prediction that learns path-based features directly from graph structure, outperforming graph neural networks on half of benchmark datasets.
high-dimensional multi-component ica phase structure
mean-field theory reveals initialization-driven competition and decoupling regimes in online independent component analysis.
build an ai lms with ollama, fastapi, and react
a step-by-step guide to creating a learning management system that adapts to each learner using local ai models.
gitlab restructures for agentic era, cuts management layers
gitlab announces workforce reduction, flatter org, and new values as it bets on agentic engineering expanding software demand.
how attention helps signal recovery in sequence models
a random matrix analysis reveals how attention weights affect signal detection in high-dimensional sequence representations.
cowboy space raises $275m to build rockets for orbital data centers
cowboy space corporation raised $275 million to develop its own rockets for launching data centers into orbit, aiming to bypass a shortage of launch capacity.
gm cuts it staff to hire ai builders
general motors laid off 600 it workers to replace them with hires skilled in ai-native development, data engineering, and model building.
kernel selection as model selection for two-sample tests
a new method treats kernel choice in mmd tests as a model selection problem, using a complexity penalty to avoid overfitting and scale to deep kernels.
ai writing is breaking my brain
jason koebler describes the zombie internet where ai-generated content blends with human writing, making it exhausting to filter and distorting communication.
smarter json alternative for llm pipelines
toon reduces token waste in llm prompts by compacting repeated json structure into a tabular format.
aws foundation model building blocks
aws infrastructure for foundation model training and inference integrates accelerated compute, high-bandwidth networking, and distributed storage with open-source orchestration and ml frameworks.
build vector search from scratch in python
a step-by-step guide to creating a vector search engine using only numpy, covering embeddings, normalization, cosine similarity, and visualization.
ai guides chemists to design molecules with plain language
a new ai system called synthegy lets chemists describe synthesis goals in everyday language, then scores and explains the best reaction pathways.
in-kernel broadcast optimization for recsys inference
meta's ikbo eliminates redundant user embedding replication in recommendation models by fusing broadcast logic directly into gpu kernels, cutting latency by up to two-thirds.
daily brief: 2026-05-11
hugging face fights benchmark gaming, reasoning models show more bias with longer thinking, and google deepmind's alphaevolve scales algorithm design across fields.