There’s a lot of focus right now on whether AI can write “perfect” code and what this will mean. As models will get better and context windows get bigger, will code quality improve? Will we soon reach a point where AI produces production-ready software on the first try? If the answer is “Yes, AI can get it right first time”, we should be focused on giving AI the perfect context, all our rules and standards, doing upfront planning on requirements, specifications […]

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arXiv:2603.20235v1 Announce Type: new Abstract: We qualitatively compared literature reviews produced with varying degrees of AI assistance. The same LLM, given the same corpus of 280 papers but different selections, produced dramatically different reviews, from mainstream and politically neutral to critical and post-colonial, though neither orientation was intended. LLM outputs always appear at first glance to be well written, well informed and thought out, but closer reading reveals gaps, biases and lack of depth. Our comparison of six […]

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arXiv:2602.21316v1 Announce Type: new Abstract: Soft robots were introduced in large part to enable safe, adaptive interaction with the environment, and this interaction relies fundamentally on contact. However, modeling and planning contact-rich interactions for soft robots remain challenging: dense contact candidates along the body create redundant constraints and rank-deficient LCPs, while the disparity between high stiffness and low friction introduces severe ill-conditioning. Existing approaches rely on problem-specific approximations or penalty-based treatments. This letter presents a unified complementarity-based framework […]

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Drones are becoming popular as a complementary system for ac{ems}. Although several pilot studies and flight trials have shown the feasibility of drone-assisted ac{aed} delivery, running a full-scale operational network remains challenging due to high capital expenditure and environmental uncertainties. In this paper, we formulate a reliability-informed Bayesian learning framework for designing drone-assisted ac{aed} delivery networks under environmental and operational uncertainty. We propose our objective function based on the survival probability of ac{ohca} patients to identify the ideal […]

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arXiv:2602.11194v1 Announce Type: new Abstract: Post-wildfire mudflows are increasingly hazardous due to the prevalence of wildfires, including those on the wildland-urban interface. Upon burning, soil on the surface or immediately beneath becomes hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded soil blanket the downslope, leading to catastrophic debris flows. Soil hydrophobicity enhances erosion, resulting in post-wildfire debris flows that differ from natural mudflows in intensity, duration, and destructiveness. Thus, it is crucial to understand […]

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arXiv:2601.02380v1 Announce Type: new Abstract: Recent reports claim that Large Language Models (LLMs) have achieved the ability to derive new science and exhibit human-level general intelligence. We argue that such claims are not rigorous scientific claims, as they do not satisfy Popper’s refutability principle (often termed falsifiability), which requires that scientific statements be capable of being disproven. We identify several methodological pitfalls in current AI research on reasoning, including the inability to verify the novelty of findings due […]

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arXiv:2603.12406v1 Announce Type: new Abstract: textbf{Background:} Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision-making processes. While fairness is often examined at the algorithmic or data level, there is limited understanding of how it is addressed during the early stages of software development. Moreover, little is known about how team diversity affects fairness-related decisions in software projects. textbf{Aims:} This study investigates how diversity in software teams influences […]

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arXiv:2604.08664v1 Announce Type: new Abstract: Developing autonomous physical human-robot interaction (pHRI) systems is limited by the scarcity of large-scale training data to learn robust robot behaviors for real-world applications. In this paper, we introduce a zero-shot “text2sim2real” generative simulation framework that automatically synthesizes diverse pHRI scenarios from high-level natural-language prompts. Leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), our pipeline procedurally generates soft-body human models, scene layouts, and robot motion trajectories for assistive tasks. We utilize this […]

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arXiv:2512.23087v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) for Large Language Models (LLMs) faces a fundamental tension: the numerical divergence between high-throughput inference engines and numerically precise training engines. Although these systems share the same parameters, they produce slightly different probability distributions, creating a training-inference mismatch. We prove that the bound on the log-probability divergence arising from this mismatch scales as $(1-p)$, where $p$ is the token probability. This scaling induces a highly asymmetric effect: the bound vanishes […]

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Hi everyone, I’m a CS student aiming to master Reinforcement Learning (RL) for industry roles and startup building. I’ve designed the following roadmap and would really appreciate feedback from experienced practitioners. My background: Comfortable with Python, NumPy, Pandas Basic ML & Deep Learning knowledge Long-term goal: RL Engineer / Agentic AI systems 🛣️ My RL Roadmap 1️⃣ Foundations Python (OOP, decorators, multiprocessing) Math: Linear Algebra, Probability, Calculus Markov Processes (MDP, Bellman equations) 2️⃣ Classical RL Multi-armed bandits Dynamic […]

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