Learning a stationary diffusion amounts to estimating the parameters of a stochastic differential equation whose stationary distribution matches a target distribution. We build on the recently introduced kernel deviation from stationarity (KDS), which enforces stationarity by evaluating expectations of the diffusion’s generator in a reproducing kernel Hilbert space. Leveraging the connection between KDS and Stein discrepancies, we introduce the Stein-type KDS (SKDS) as an alternative formulation. We prove that a vanishing SKDS guarantees alignment of the learned diffusion’s […]

Ver mais

Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are sparse, enabling superior on-chip resource […]

Ver mais

When you go to the doctor, one thing you may have noticed is how trash is handled. Usually, there are multiple trash cans for regular waste and biohazard waste. While this is done to prevent contamination to other individuals, it is also very costly for medical providers if they fail to properly dispose of their waste. For every violation where a sharp object is thrown away improperly, there is a $13,653 fine per violation. This does not include […]

Ver mais

arXiv:2602.05078v1 Announce Type: new Abstract: Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual’s health, making it a vital mechanism in the prevention and care of chronic diseases and obesity. However, image-based dietary assessment suffers from estimating the three dimensional size of food from 2D image inputs. Many strategies have been devised to overcome this critical limitation such as the use of auxiliary inputs like depth maps, multi-view inputs, or […]

Ver mais

Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computation across the network boundary. However, existing SL systems predominantly rely on first-order (FO) optimization, which requires clients to store intermediate quantities such as activations for backpropagation. This results in substantial memory overhead, largely negating benefits of model partitioning. In contrast, zeroth-order (ZO) optimization eliminates backpropagation and significantly reduces memory usage, but often suffers from slow convergence […]

Ver mais

arXiv:2602.04903v1 Announce Type: new Abstract: Feature steering has emerged as a promising approach for controlling LLM behavior through direct manipulation of internal representations, offering advantages over prompt engineering. However, its practical effectiveness in real-world applications remains poorly understood, particularly regarding potential trade-offs with output quality. We show that feature steering methods substantially degrade model performance even when successfully controlling target behaviors, a critical trade-off. Specifically, we evaluate Goodfire’s Auto Steer against prompt engineering baselines across 14 steering queries […]

Ver mais

arXiv:2601.12231v1 Announce Type: new Abstract: Insider threat detection is a key challenge in enterprise security, relying on user activity logs that capture rich and complex behavioral patterns. These logs are often multi-channel, non-stationary, and anomalies are rare, making anomaly detection challenging. To address these issues, we propose a novel framework that integrates wavelet-aware modulation, multi-resolution wavelet decomposition, and resolution-adaptive attention for robust anomaly detection. Our approach first applies a deviation-aware modulation scheme to suppress routine behaviors while amplifying […]

Ver mais

There’s a question that quietly predicts whether an AI agent will feel sharp or strangely distant: How long does the agent remain misaligned when a new domain term enters the conversation? It sounds almost trivial — a glossary issue, a maintenance task. But in practice, this single latency window reveals more about an agent’s reliability than most teams realize. Because language doesn’t evolve politely. It arrives mid‑conversation. A finance analyst introduces a new internal metric. A product team […]

Ver mais