arXiv:2602.20221v1 Announce Type: new Abstract: This paper aims to diversify the existing critical discourse by introducing new perspectives for the poetic, expressive, and ethical features of tactical media art that involves artificial intelligence (AI). It explores diverse approaches of AI artists and their effectiveness in critiquing the epistemic, phenomenological, and political aspects of AI science and technology. Focusing on the three representative thematic areas – sociocultural, existential, and political – it discusses the works that exemplify poetic complexity […]

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Lagrangian Neural Networks (LNNs) can learn arbitrary Lagrangians from trajectory data, but their unusual optimization objective leads to significant training instabilities that limit their application to complex systems. We propose several improvements that address these fundamental challenges, namely, a Hessian regularization scheme that penalizes unphysical signatures in the Lagrangian’s second derivatives with respect to velocities, preventing the network from learning unstable dynamics, activation functions that are better suited to the problem of learning Lagrangians, and a physics-aware coordinate […]

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arXiv:2601.12280v1 Announce Type: new Abstract: Home-based music therapy devices require accessible and cost-effective solutions for users to understand and track their therapeutic progress. Traditional physiological signal analysis, particularly EEG interpretation, relies heavily on domain experts, creating barriers to scalability and home adoption. Meanwhile, few experts are capable of interpreting physiological signal data while also making targeted music recommendations. While large language models (LLMs) have shown promise in various domains, their application to automated physiological report generation for music […]

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arXiv:2603.00710v1 Announce Type: new Abstract: This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the same spiking population encoder. The proxy is motivated by leaky integrate-and-fire E/I circuit models with three-factor delayed reward modulation. The hybrid update is local in pre x post rates but uses supervised labels and no timing-based credit assignment. On sklearn digits, fixed-seed evaluation […]

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Hi everyone, I’m a beginner who is just starting with Python and slowly learning about Reinforcement Learning (RL). I have a basic doubt and wanted guidance from people already in the field: Is a PhD or Master’s degree mandatory to get a job in Reinforcement Learning? Are there industry roles where a Bachelor’s + strong skills/projects are enough? Which type of RL roles usually require PhD, and which don’t? I’m not aiming for research right now — more […]

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arXiv:2603.06901v1 Announce Type: new Abstract: As machine learning (ML) systems increasingly shape access to credit, jobs, and other opportunities, the fairness of algorithmic decisions has become a central concern. Yet it remains unclear when enforcing fairness constraints in these systems genuinely improves outcomes for affected groups or instead leads to “leveling down,” making one or both groups worse off. We address this question in a unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions. Our […]

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arXiv:2602.02532v1 Announce Type: new Abstract: Transfer learning promises to reduce the high sample complexity of deep reinforcement learning (RL), yet existing methods struggle with domain shift between source and target environments. Policy distillation provides powerful tactical guidance but fails to transfer long-term strategic knowledge, while automaton-based methods capture task structure but lack fine-grained action guidance. This paper introduces Context-Aware Distillation with Experience-gated Transfer (CADENT), a framework that unifies strategic automaton-based knowledge with tactical policy-level knowledge into a coherent […]

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arXiv:2601.09838v1 Announce Type: new Abstract: Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved […]

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arXiv:2603.20538v1 Announce Type: cross Abstract: Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Autoregressive models like transformer have proven remarkably effective, from large language models (LLMs) to vision-language-action systems (VLAs). However, applying autoregressive models to continuous control requires discretizing actions through quantization, a practice widely adopted yet poorly understood theoretically. This paper provides theoretical foundations for this practice. We analyze how quantization error propagates […]

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