arXiv:2511.11817v2 Announce Type: replace Abstract: Time series forecasting is essential in a wide range of real world applications. Recently, frequency-domain methods have attracted increasing interest for their ability to capture global dependencies. However, when applied to non-stationary time series, these methods encounter the $textit{spectral entanglement}$ and the computational burden of complex-valued learning. The $textit{spectral entanglement}$ refers to the overlap of trends, periodicities, and noise across the spectrum due to $textit{spectral leakage}$ and the presence of non-stationarity. However, existing […]

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We present a unified information-theoretic framework to analyze the generalization performance of differentially private (DP) quantum learning algorithms. By leveraging the connection between privacy and algorithmic stability, we establish that $(varepsilon, δ)$-Quantum Differential Privacy (QDP) imposes a strong constraint on the mutual information between the training data and the algorithm’s output. We derive a rigorous, mechanism-agnostic upper bound on this mutual information for learning algorithms satisfying a 1-neighbor privacy constraint. Furthermore, we connect this stability guarantee to generalization, […]

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arXiv:2601.18993v1 Announce Type: new Abstract: Camera redirection aims to replay a dynamic scene from a single monocular video under a user-specified camera trajectory. However, large-angle redirection is inherently ill-posed: a monocular video captures only a narrow spatio-temporal view of a dynamic 3D scene, providing highly partial observations of the underlying 4D world. The key challenge is therefore to recover a complete and coherent representation from this limited input, with consistent geometry and motion. While recent diffusion-based methods achieve […]

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arXiv:2601.17160v3 Announce Type: replace Abstract: We develop a data-driven information-theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrictive assumptions, such as bounded or discrete outcomes; require external inputs (for example, instrumental variables, proxies, or user-specified sensitivity parameters); necessitate full structural causal model specifications; or focus solely on population-level averages while neglecting covariate-conditional effects. We overcome all four limitations simultaneously by establishing novel information-theoretic, data-driven divergence bounds. Our key theoretical […]

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On-policy distillation (OPD), which aligns the student with the teacher’s logit distribution on student-generated trajectories, has demonstrated strong empirical gains in improving student performance and often outperforms off-policy distillation and reinforcement learning (RL) paradigms. In this work, we first theoretically show that OPD is a special case of dense KL-constrained RL where the reward function and the KL regularization are always weighted equally and the reference model can by any model. Then, we propose the Generalized On-Policy Distillation […]

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In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender for safety defense, and an evaluator for response assessment. In this paper, we propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative and co-improving collaboration among […]

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Built a reservoir computing system (Liquid State Machine) as a learning experiment. Instead of a standard static reservoir, I added biological simulation layers on top to see how constraints affect behavior. What it actually does (no BS): – LSM with 2000+ reservoir neurons, Numba JIT-accelerated – Hebbian + STDP plasticity (the reservoir rewires during runtime) – Neurogenesis/atrophy reservoir can grow or shrink neurons dynamically – A hormone system (3 floats: dopamine, cortisol, oxytocin) that modulates learning rate, reflex […]

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Monotone optimisation problems admit specialised global solvers such as the Polyblock Outer Approximation (POA) algorithm, but these methods typically require explicit objective and constraint functions. In many applications, these functions are only available through data, making POA difficult to apply directly. We introduce an algorithm-aware learning approach that integrates learned models into POA by directly predicting its projection primitive via the radial inverse, avoiding the costly bisection procedure used in standard POA. We propose Homogeneous-Monotone Radial Inverse (HM-RI) […]

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California’s recent wildfires were fought by over 780 inmate firefighters who received rigorous taxpayer-funded training. But until recent reforms, many of these trained firefighters faced insurmountable barriers to employment after release—state licensing laws blocked them from the very careers they’d prepared for. lead , Cato scholar Stephen Slivinski examines this issue in a new Washington Post opinion piece, revealing how occupational licensing laws create employment barriers for 70 million Americans with criminal records. The evidence is clear: employment is […]

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