arXiv:2602.22259v1 Announce Type: new Abstract: Recognizing the substantial computational cost of backpropagation (BP), non-BP methods have emerged as attractive alternatives for efficient learning on emerging neuromorphic systems. However, existing non-BP approaches still face critical challenges in efficiency and scalability. Inspired by neural representations and dynamic mechanisms in the brain, we propose a perturbation-based approach called LOw-rank Cluster Orthogonal (LOCO) weight modification. We find that low-rank is an inherent property of perturbation-based algorithms. Under this condition, the orthogonality constraint […]

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This survey (re)introduces reinforcement learning methods to economists. The curse of dimensionality limits how far exact dynamic programming can be effectively applied, forcing us to rely on suitably "small" problems or our ability to convert "big" problems into smaller ones. While this reduction has been sufficient for many classical applications, a growing class of economic models resists such reduction. Reinforcement learning algorithms offer a natural, sample-based extension of dynamic programming, extending tractability to problems with high-dimensional states, continuous […]

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In 2026, China is establishing itself as a formidable competitor, achieving success with its Kimi AI tool. The advanced assistant developed by Moonshot AI, Kimi, has emerged as one of the most innovative AI products currently in the market. What makes Kimi AI’s Rise particularly noteworthy is its focus on long-context understanding, productivity, and agent-based workflows, which are areas where competition is intensifying globally. It serves as a strategic initiative to compete with ChatGPT and Gemini because intelligent […]

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TPS (transactions per second) has become a symbol of performance in the blockchain industry. Networks are often compared by a single number, with claims of “tens of thousands of TPS.” The problem is that TPS by itself says very little about a system’s real throughput. TPS is highly dependent on transaction types, block configuration, and measurement methodology. The same blockchain can report radically different TPS values without any architectural changes — simply by tuning execution parameters such as […]

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Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks. Despite its popularity, its theoretical foundations and behavior under distributional shift remain poorly understood. In this work, we provide a principled statistical interpretation by proving that EDL training corresponds to amortized variational inference in a hierarchical Bayesian model with a tempered pseudo-likelihood. This perspective reveals a major drawback: standard EDL conflates epistemic and aleatoric uncertainty, leading […]

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arXiv:2603.03315v1 Announce Type: new Abstract: Internet memes are a powerful form of online communication, yet their nature and reliance on commonsense knowledge make toxicity detection challenging. Identifying key features for meme interpretation and understanding, is a crucial task. Previous work has been focused on some elements contributing to the meaning, such as the Textual dimension via OCR, the Visual dimension via object recognition, upper layers of meaning like the Emotional dimension, Toxicity detection via proxy variables, such as […]

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arXiv:2603.22307v1 Announce Type: new Abstract: Seismic full-waveform inversion is a core technology for obtaining high-resolution subsurface model parameters. However, its highly nonlinear characteristics and strong dependence on the initial model often lead to the inversion process getting trapped in local minima. In recent years, generative diffusion models have provided a way to regularize full-waveform inversion by learning implicit prior distributions. However, existing methods mostly use unconditional diffusion processes, ignoring the inherent physical coupling relationship between velocity and density […]

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Hi everyone, I’m a 3rd-year undergraduate student currently working on a reinforcement learning project. My goal is to train a WLKATA Mirobot (6-DOF) in NVIDIA Isaac Lab for a “reach and stop” task and successfully transfer the policy to the real robot (Sim-to-Real). I am specifically focusing on overcoming the mechanical limitations (such as backlash and joint friction) of the Mirobot through Domain Randomization and System Identification. Before I dive deeper into designing the environment, I wanted to […]

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