The Unscented Kalman Filter
State estimation for a nonlinear system Continue reading on Towards AI »
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Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling
arXiv:2604.13054v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less predictable than that of text-only LLMs. Increasing model size and task diversity often yields diminishing returns. In this work, we argue that the primary bottleneck in multimodal scaling is not task format, but knowledge density in training data. We first show that task-specific supervision such as Visual Question Answering (VQA) contributes little incremental […]
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AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis
arXiv:2603.03378v2 Announce Type: new Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action execution under permission-governed environments, and the inability of closed systems to improve from failures. We present AOI (Autonomous Operations Intelligence), a trainable multi-agent framework formulating automated operations as a structured trajectory learning problem under security constraints. Our approach integrates three key […]
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This Image Converter Runs Entirely in Your Browser
Most image conversion tools work the same way: You upload an image → it goes to a server → gets processed → then you download it. That works. But it introduces two problems: Upload time slows everything down Files leave the user’s device (privacy issue) While working on small browser-based utilities, I wanted to test a different approach: Can image conversion happen entirely inside the browser? The Problem With Traditional Image Conversion Tools Most online tools rely on […]
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Intuit beats FTC in court, ending restrictions on “free” TurboTax ads
An appeals court invalidated the Biden-era Federal Trade Commission’s attempt to punish Intuit for allegedly deceptive ads that pitched TurboTax as free. Under then-Chair Lina Khan, the FTC determined in 2024 that the TurboTax maker violated US law with deceptive advertising and ordered it to stop telling consumers, without more obvious disclaimers, that TurboTax or other products are free. The FTC’s chief administrative law judge had previously found that Intuit’s ads violated prohibitions on deceptive advertising because the […]
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Topological Causal Effects
arXiv:2603.02289v1 Announce Type: cross Abstract: Estimating causal effects is particularly challenging when outcomes arise in complex, non-Euclidean spaces, where conventional methods often fail to capture meaningful structural variation. We develop a framework for topological causal inference that defines treatment effects through differences in the topological structure of potential outcomes, summarized by power-weighted silhouette functions of persistence diagrams. We develop an efficient, doubly robust estimator in a fully nonparametric model, establish functional weak convergence, and construct a formal test […]
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Exact closed-form Gaussian moments of residual layers
arXiv:2601.22307v1 Announce Type: new Abstract: We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment matching. We close a longstanding gap by deriving exact moment matching for the probit, GeLU, ReLU (as a limit of GeLU), Heaviside (as a limit of probit), and sine activation functions; for both feedforward and generalized residual layers. On random networks, we find orders-of-magnitude improvements in the KL […]
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XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators
Emerging memory technologies have gained significant attention as a promising pathway to overcome the limitations of conventional computing architectures in deep learning applications. By enabling computation directly within memory, these technologies – built on nanoscale devices with tunable and nonvolatile conductance – offer the potential to drastically reduce energy consumption and latency compared to traditional von Neumann systems. This paper introduces XBTorch (short for CrossBarTorch), a novel simulation framework that integrates seamlessly with PyTorch and provides specialized tools […]
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Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety
Driver drowsiness significantly impairs the ability to accurately judge safe braking distances and is estimated to contribute to 10%-20% of road accidents in Europe. Traditional driver-assistance systems lack adaptability to real-time physiological states such as drowsiness. This paper proposes a deep reinforcement learning-based autonomous braking system that integrates vehicle dynamics with driver physiological data. Drowsiness is detected from ECG signals using a Recurrent Neural Network (RNN), selected through an extensive benchmark analysis of 2-minute windows with varying segmentation […]
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Prediction of Fault Slip Tendency in CO${_2}$ Storage using Data-space Inversion
arXiv:2601.05431v1 Announce Type: new Abstract: Accurately assessing the potential for fault slip is essential in many subsurface operations. Conventional model-based history matching methods, which entail the generation of posterior geomodels calibrated to observed data, can be challenging to apply in coupled flow-geomechanics problems with faults. In this work, we implement a variational autoencoder (VAE)-based data-space inversion (DSI) framework to predict pressure, stress and strain fields, and fault slip tendency, in CO${_2}$ storage projects. The main computations required by […]
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