arXiv:2601.05279v1 Announce Type: new Abstract: Policy Space Response Oracles (PSRO) combines game-theoretic equilibrium computation with learning and is effective in approximating Nash Equilibrium in zero-sum games. However, the computational cost of PSRO has become a significant limitation to its practical application. Our analysis shows that game simulation is the primary bottleneck in PSRO’s runtime. To address this issue, we conclude the concept of Simulation-Free PSRO and summarize existing methods that instantiate this concept. Additionally, we propose a novel […]

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Existing approaches to multi-view learning are particularly effective when the views are either independent (i.e, multi-kernel approaches) or fully dependent (i.e., shared latent spaces). However, in real scenarios, these assumptions are almost never truly satisfied. Recently, two methods have attempted to tackle this problem by factorizing the information and learn separate latent spaces for modeling the shared (i.e., correlated) and private (i.e., independent) parts of the data. However, these approaches are very sensitive to parameters setting or initialization. […]

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arXiv:2512.03851v2 Announce Type: replace Abstract: Neural networks have become a widely adopted tool for modeling nonlinear dynamical systems from data. However, the choice of training strategy remains a key design decision, particularly for simulation tasks. This paper compares two predominant strategies: parallel and series-parallel training. The conducted empirical analysis spans five neural network architectures and two examples: a pneumatic valve test bench and an industrial robot benchmark. The study reveals that, even though series-parallel training dominates current practice, […]

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Geometric deep learning by starting with the XOR affair I’ve just spent two days at an AI school (ANAIS ) in Kathmandu listening to lectures on geometric deep learning from bright folks from Oxford, TU Wien, and top institutions. Geometric deep learning from Michael Bronstein  (who also coined the term; needs no introduction really). The program is super organised, covering symmetry groups, graph neural networks and fundamental principles underlying modern AI before it eventually catches up to modern trends. […]

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arXiv:2601.02725v1 Announce Type: cross Abstract: We study the problem of sampling from a target distribution $pi(q)propto e^{-U(q)}$ on $mathbb{R}^d$, where $U$ can be non-convex, via the Hessian-free high-resolution (HFHR) dynamics, which is a second-order Langevin-type process that has $e^{-U(q)-frac12|p|^2}$ as its unique invariant distribution, and it reduces to kinetic Langevin dynamics (KLD) as the resolution parameter $alphato0$. The existing theory for HFHR dynamics in the literature is restricted to strongly-convex $U$, although numerical experiments are promising for non-convex […]

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Recent advances in logistics optimization have enabled AI-driven solutions for demand forecasting, route planning, and supply chain management, yet most existing systems rely on fixed feature engineering and centralized decision-making, limiting adaptability, scalability, and effective human–AI collaboration in dynamic environments. To address these challenges, we propose Adaptive Multi-Agent Logistics Optimization with Human–AI Coordination Learning, a three-stage framework that integrates context-aware feature generation, hybrid multi-agent decision-making with intelligent caching, and adaptive coordination learning. The proposed system dynamically adjusts feature […]

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arXiv:2601.07982v1 Announce Type: new Abstract: We develop a new statistical ideal observer model that performs holistic visual search (or gist) processing in part by placing thresholds on minimum extractable image features. In this model, the ideal observer reduces the number of free parameters thereby shrinking down the system. The applications of this novel framework is in medical image perception (for optimizing imaging systems and algorithms), computer vision, benchmarking performance and enabling feature selection/evaluations. Other applications are in target […]

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Federated Learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, IoT systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation […]

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