Encoding models using word embeddings or artificial neural network (ANN) features reliably predict brain responses to naturalistic stimuli, yet interpreting these models remains challenging. A central limitation is superposition: distinct semantic features become entangled along correlated directions in dense embeddings when latent features outnumber embedding dimensions. This entanglement renders regression weights non-identifiable--different combinations of semantic directions can produce identical predictions, precluding principled interpretation of voxel selectivity. To address this, we introduce the Sparse Concept Encoding Model, which transforms dense embeddings into a higher-dimensional, sparse, non-negative space of learned concept atoms.

Plus: Amazon workers who backed data center limits face potential termination. A startup claims it broke through a bottleneck that's holding back LLMs AI startup Subquadratic came out of stealth last month with a huge claim: it had solved a mathematical bottleneck that had held back large language models for almost a decade. The purported breakthrough comes from slashing the number of computations transformers need to carry out to generate answers. The result is a faster and cheaper LLM that uses far less energy than any other model on the market. Many experts remained skeptical--but Subquadratic has started to share the receipts. They suggest that their approach might be worth paying attention to.

We address the problem of looking into the water from the air, where we seek to remove image distortions caused by refractions at the water surface. Our approach is based on modeling the different water surface structures at various points in time, assuming the underlying image is constant. To this end, we propose a model that consists of two neural-field networks. The first network predicts the height of the water surface at each spatial position and time, and the second network predicts the image color at each position. Using both networks, we reconstruct the observed sequence of images and can therefore use unsupervised training.

Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization--where expert labels guide representations--remains underexplored, as most supervised approaches prioritize classification over visualization. Recently, RF-PHATE, a diffusion-based manifold learning method leveraging random forests and information geometry, marked significant progress in supervised visualization.

They were testifying at a Seattle city council meeting. Five members of Amazon Employees for Climate Justice (AECJ) previously testified at Seattle city council meetings about AI data centers . Now, three of them are apparently under investigation by the company. The AECJ has filed a civil rights complaint against the company on behalf of the three engineers, according to CNBC and GeekWire, accusing Amazon of violating a Seattle law that prohibits companies from discriminating against employees based on their political ideology, race, religion and age. The engineers spoke at Seattle city council hearings over whether to put a pause on AI data center buildouts.

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A new standard called Agentic Resource Discovery (ARD), backed by Google, Microsoft, and Nvidia, aims to create a "Google Search for AI" to help agents find online services. This standardization could transform the chaotic "agentic web" into an efficient system where AI agents perform complex tasks reliably and effectively. AI is coming for us, they keep saying. We'll all have AI agents acting on our behalf, doing everything from our weekly grocery shopping to booking airline tickets. It's gonna change everything, just like the web did!

As language models scale, their performance improves dramatically across a wide range of tasks, but so does their tendency to memorize and regurgitate parts of their training data verbatim. This tradeoff poses serious legal, ethical, and safety concerns, especially in real-world deployments. Existing mitigation techniques, such as differential privacy or model unlearning, often require retraining or access to internal weights making them impractical for most users. In this work, we introduce TOKENSWAP, a lightweight, post-hoc defense designed for realistic settings where the user can only access token-level outputs. Our key insight is that while large models are necessary for high task performance, small models (e.g., DistilGPT-2) are often sufficient to assign fluent, grammatically plausible probabilities to common function words - and crucially, they memorize far less. By selectively swapping token probabilities between models, TOKENSWAP preserves the capabilities of large models while reducing their propensity for verbatim reproduction.

Recent Graphical User Interface (GUI) agents replicate the R1-Zero paradigm, coupling online Reinforcement Learning (RL) with explicit chain-of-thought reasoning prior to object grounding and thereby achieving substantial performance gains.

Retrieval-Augmented Generation (RAG) based on knowledge graphs (KGs) enhances large language models (LLMs) with structural and textual external knowledge. Yet, existing KG-based RAG methods struggle to retrieve accurate and diverse information when handling complex queries. By modeling KG-based retrieval as a multi-step decision process, Process Reward Models (PRMs) offer a promising solution to align the retrieval behavior with the query-specific knowledge requirements. However, PRMs heavily rely on process-level supervision signals that are expensive and hard to obtain on KGs. To address this challenge, we propose GraphFlow, a framework that efficiently retrieves accurate and diverse knowledge required for complex queries from text-rich KGs. GraphFlow employs a detailed balance objective with local exploration to jointly optimize a retrieval policy and a flow estimator.