arXiv:2510.11903v1 Announce Type: new Abstract: User event modeling plays a central role in many machine learning applications, with use cases spanning e-commerce, social media, finance, cybersecurity, and other domains. User events can be broadly categorized into personal events, which involve…
arXiv:2506.15065v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user instructions in the observed physical environment, can lead to navigation…
arXiv:2510.11917v1 Announce Type: new Abstract: Dementia disorders such as Alzheimer’s disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG that challenge accurate diagnosis. Existing EEG-based methods are limited by full-band frequency analysis that hinders precise differentiation of…
arXiv:2509.04154v3 Announce Type: replace Abstract: We introduce Adaptive Filter Attention (AFA), a novel attention mechanism that incorporates a learnable dynamics model directly into the computation of attention weights. Rather than comparing queries and keys directly, we model the input sequence…
arXiv:2510.11926v1 Announce Type: new Abstract: Indoor Wi-Fi positioning remains a challenging problem due to the high sensitivity of radio signals to environmental dynamics, channel propagation characteristics, and hardware heterogeneity. Conventional fingerprinting and model-based approaches typically require labor-intensive calibration and suffer…
arXiv:2510.09719v2 Announce Type: replace Abstract: Large language models (LLMs) often exhibit complementary strengths. Model routing harnesses these strengths by dynamically directing each query to the most suitable model, given a candidate model pool. However, routing performance relies on accurate model…
arXiv:2510.11933v1 Announce Type: new Abstract: In this work, we propose three efficient restart paradigms for model-free non-stationary reinforcement learning (RL). We identify two core issues with the restart design of Mao et al. (2022)’s RestartQ-UCB algorithm: (1) complete forgetting, where…
arXiv:2410.11857v2 Announce Type: replace-cross Abstract: Today’s Internet infrastructure is centered around content retrieval over HTTP, with middleboxes (e.g., HTTP proxies) playing a crucial role in performance, security, and cost-effectiveness. We envision a future where Internet communication will be dominated by…
arXiv:2510.11942v1 Announce Type: new Abstract: We show that emph{efficient Turing computability} at any fixed input/output precision implies the existence of emph{compositionally sparse} (bounded-fan-in, polynomial-size) DAG representations and of corresponding neural approximants achieving the target precision. Concretely: if $f:[0,1]^dtoR^m$ is computable…
arXiv:2507.00546v2 Announce Type: replace-cross Abstract: Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition-driven or iterative optimization methods struggle with the inherently high-dimensional, non-convex…
