arXiv:2604.15328v1 Announce Type: new Abstract: Virtual reality (VR) is increasingly used across psychology, from research and assessment to counseling, psychological treatment, and psychotherapy, with growing applications for children and adolescents. In these contexts, VR is often treated as a relatively neutral delivery medium. This assumption may be misleading. Most consumer head-mounted displays (HMDs) have been designed primarily for adult anthropometry, including adult interpupillary distance (IPD) ranges. As a result, some children may be excluded from participation or may […]

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SpyderBot is a cutting-edge LLM analytics platform that reveals exactly how AI models like ChatGPT, Grok, and Gemini see your brand and your competitors. Using a network of over 20,000 distributed LLM-bots, SpyderBot tracks real-time Share of Voice, competitor visibility scores, sentiment analysis, and LLM crawler activity on your website — insights that traditional analytics tools completely miss. For brands and agencies, it answers critical questions: → Where do we rank in AI answers? → Which competitors are […]

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arXiv:2509.22613v2 Announce Type: replace-cross Abstract: Recent reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs), yet the theoretical basis for their effectiveness remains elusive. In this work, we investigate RL’s benefits and limitations through a tractable graph-based abstraction, focusing on policy gradient (PG) and Q-learning methods. Our theoretical analyses reveal that supervised fine-tuning (SFT) may introduce co-occurrence-based spurious solutions, whereas RL achieves correct planning primarily through exploration, underscoring exploration’s role in enabling […]

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Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to manipulate model behavior by injecting poisoned data with hidden triggers. These attacks can lead to severe consequences, especially in critical applications such as autonomous driving, healthcare, and finance. Detecting and mitigating backdoor attacks is crucial across the lifespan of model’s phases, including pre-training, in-training, and post-training. In this paper, we propose Pre-Training Backdoor Mitigation for Federated Learning (FL-PBM), a […]

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arXiv:2603.24495v1 Announce Type: cross Abstract: While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data restricted to bounded domains. This paper provides a statistical analysis of reflected diffusion models on the hypercube $[0,1]^D$ for target distributions supported on $d$-dimensional linear subspaces. A primary challenge in this setting is the absence of Gaussian transition kernels, which play a central role in standard theory in $mathbb{R}^D$. By employing an […]

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I’m learning the foundations of RL in alignment with the Alberta Plan for AI research and have been running through sets of experiments to both learn and experiment. To that end I spent the last month validating different methods for streaming deep RL on a non-stationary, adversarial dataset of real SSH honeypot observations. This work focuses on prediction and is in line with steps 1 & 2 of the Alberta Plan (Sutton, Bowling, & Pilarski 2022). After implementing […]

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