This paper studies continuous-time stochastic control problems whose controlled states are fully non-Markovian and depend on unknown model parameters. Such problems arise naturally in path-dependent stochastic differential equations, rough-volatility hedging, and systems driven by fractional Brownian motion. Building on the discrete skeleton approach developed in earlier work, we propose a Monte Carlo learning methodology for the associated embedded backward dynamic programming equation. Our main contribution is twofold. First, we construct explicit dominating training laws and Radon--Nikodym weights for several representative classes of non-Markovian controlled systems. This yields an off-model training architecture in which a fixed synthetic dataset is generated under a reference law, while the dynamic programming operators associated with a target model are recovered by importance sampling. Second, we use this structure to design an adaptive update mechanism under parametric model uncertainty, so that repeated recalibration can be performed by reweighting the same training sample rather than regenerating new trajectories. For fixed parameters, we establish non-asymptotic error bounds for the approximation of the embedded dynamic programming equation via deep neural networks. For adaptive learning, we derive quantitative estimates that separate Monte Carlo approximation error from model-risk error. Numerical experiments illustrate both the off-model training mechanism and the adaptive importance-sampling update in structured linear-quadratic examples.

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'The encroachment of technology can feel inevitable.' 'The encroachment of technology can feel inevitable.' The fight to see clearly through big tech's echo chambers Today, I'm mulling over whether to upgrade my iPhone 11 Pro. How to see through Silicon Valley's narrative The encroachment of technology can feel inevitable. It may have always, but increasingly it's a perception bolstered by big tech's own friendly media bubble. But at the same time as big tech's echo chambers are growing louder, so do critical voices from within.

This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Exogenous predictors are incorporated by convexly fusing feature-specific kernels. For both quantum and classical models, the only tuned quantities are the feature-mixing weights and the KRR ridge α; classical hyperparameters (γ, r, d) are fixed, with the same validation set size for all models. Experiments are conducted on a noiseless simulator (5 qubits; window length L=32). Limitations and ablations are discussed, and paths toward NISQ execution are outlined. Introduction Quantum Machine Learning (QML) is an emerging discipline that combines the principles of quantum physics with traditional machine learning (ML) to exploit the distinctive characteristics of quantum systems, including superposition and entanglement phenomena [1]. This distinction facilitates the expeditious execution of certain tasks [2], such as classification and dimensionality reduction, where QML has demonstrated significant acceleration [3]. QML applications have extended to time-series data, leveraging quantum phenomena to model complex temporal dependencies. The goal is to enhance the results of traditional tasks by performing computations on qubits, which can process data more efficiently than classical bits [4, 5]. For example, Thakkar et al. [6] demonstrated that quantum machine-learning methods could enhance financial forecasting by improving both churn prediction and credit-risk assessment. Likewise, Kea et al. [7] developed a hybrid quantum-classical Long Short-Term Memory (QLSTM) to improve stock-price forecasting by leveraging quantum data encoding and high-dimensional quantum representations.

The site, a few kilometers from her own hut in Ipatsé, a Kuikuro village in the Xingu Indigenous territory, was once the backyard of her great-grandparents' house. As she scrapes the brown earth with a trowel, she soon spots a black ceramic shard. It is only about the size of her palm, and this is her first day ever on an archaeological excavation. But she immediately recognizes what the object once was. "It's an alato," she says, showing the piece to a group of archaeologists and other Kuikuro who have gathered to watch the excavation in the village of Anitahagu. An alato, Yamána explains, is a large pan used to cook beiju, a white flatbread made with yucca flour that's eaten almost every day in her village. Her grandmother still has one in the backyard fire pit where she prepares most meals, just as countless Kuikuro women did before her. This alato likely belonged to her great-grandmother on her mother's side.

Applying complex legal rules characterized by multiple, heterogeneously weighted criteria presents a fundamental challenge in judicial decision-making, often hindering the consistent realization of legislative intent. This challenge is particularly evident in the quantification of non-pecuniary damages in personal injury cases. This paper introduces Soppia, a structured prompting framework designed to assist legal professionals in navigating this complexity. By leveraging advanced AI, the system ensures a comprehensive and balanced analysis of all stipulated criteria, fulfilling the legislator's intent that compensation be determined through a holistic assessment of each case. Using the twelve criteria for non-pecuniary damages established in the Brazilian CLT (Art. 223-G) as a case study, we demonstrate how Soppia (System for Ordered Proportional and Pondered Intelligent Assessment) operationalizes nuanced legal commands into a practical, replicable, and transparent methodology. The framework enhances consistency and predictability while providing a versatile and explainable tool adaptable across multi-criteria legal contexts, bridging normative interpretation and computational reasoning toward auditable legal AI.

The ability of language models in RAG systems to selectively refuse to answer based on flawed context is critical for safety, yet remains a significant failure point. Our large-scale study reveals that even frontier models struggle in this setting, with refusal accuracy dropping below 50% on multi-document tasks, while exhibiting either dangerous overconfidence or overcaution. Static benchmarks fail to reliably evaluate this capability, as models exploit dataset-specific artifacts and memorize test instances. We introduce RefusalBench, a generative methodology that programmatically creates diagnostic test cases through controlled linguistic perturbation. Our framework employs 176 distinct perturbation strategies across six categories of informational uncertainty and three intensity levels. Evaluation of over 30 models uncovers systematic failure patterns: refusal comprises separable detection and categorization skills, and neither scale nor extended reasoning improves performance. We find that selective refusal is a trainable, alignment-sensitive capability, offering a clear path for improvement. We release two benchmarks -- RefusalBench-NQ (single document) and RefusalBench-GaRAGe (multi-document) -- and our complete generation framework to enable continued, dynamic evaluation of this critical capability.

In these lecture notes, we first recall the connection between graph neural networks, Weisfeiler-Lehman tests and logics such as first-order logic and graded modal logic. We then present a modal logic in which counting modalities appear in linear inequalities in order to solve verification tasks on graph neural networks. We describe an algorithm for the satisfiability problem of that logic. It is inspired from the tableau method of vanilla modal logic, extended with reasoning in quantifier-free fragment Boolean algebra with Presburger arithmetic.