arXiv:2601.13448v2 Announce Type: replace-cross Abstract: Despite their promise, fair machine learning methods often yield Pareto-inefficient models, in which the performance of certain groups can be improved without degrading that of others. This issue arises frequently in traditional in-processing approaches such as fairness-through-regularization. In contrast, existing Pareto-efficient approaches are biased towards a certain perspective on fairness and fail to adapt to the broad range of fairness metrics studied in the literature. In this paper, we present BADR, a simple […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Crawl, Walk, Run, Fly – The Four Phases of AI Agent Maturity By @denisp [ 5 Min read ] Don’t let your AI fly before it can walk. Why jumping straight to fully autonomous systems is a recipe for disaster (and what to do […]

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This short article compiles a few notes I previously shared when readers ask how to get the most out of my building large language model from scratch books. I follow a similar approach when I read technical books myself. It is not meant as a universal recipe, but it may be a helpful starting point. For this particular book, I strongly suggest reading it in order since each chapter depends on the previous one. And for each chapter, […]

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arXiv:2602.11319v1 Announce Type: new Abstract: In this paper, we propose a distributed flexible coupler (FC) array to enhance communication performance with low hardware cost. At each FC antenna, there is one fixed-position active antenna and multiple passive couplers that can move within a designated region around the active antenna. Moreover, each FC antenna is equipped with a local processing unit (LPU). All LPUs exchange signals with a central processing unit (CPU) for joint signal processing. We study an […]

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arXiv:2601.21812v2 Announce Type: replace Abstract: We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such as seasonality more effectively than standard diffusion. Unlike prior work that modifies the network architecture or diffuses directly in the frequency domain, our proposed method alters only the diffusion process itself, making it compatible with existing diffusion backbones (e.g., DiffWave, TimeGrad, CSDI). By staging noise injection according to component energy, it maintains […]

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arXiv:2602.18532v1 Announce Type: new Abstract: Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understanding for general-purpose policy learning. Yet, the current VLA landscape remains fragmented and exploratory. Although many groups have proposed their own VLA models, inconsistencies in training protocols and evaluation settings make it difficult to identify which design choices truly matter. To bring structure to this evolving space, we reexamine the VLA design space under a unified framework […]

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arXiv:2602.06276v1 Announce Type: cross Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable. Motivated by privacy-preserving browser APIs and the deprecation of third-party cookies, we study a setting where the learner observes a sequence of clicks and a sequence of conversions, but can only link a conversion to a set of candidate clicks (an attribution set) rather than a unique source. […]

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arXiv:2603.06608v1 Announce Type: new Abstract: The research community lacks a middle ground between StarCraft IIs full game and its mini-games. The full-games sprawling state-action space renders reward signals sparse and noisy, but in mini-games simple agents saturate performance. This complexity gap hinders steady curriculum design and prevents researchers from experimenting with modern Reinforcement Learning algorithms in RTS environments under realistic compute budgets. To fill this gap, we present the Two-Bridge Map Suite, the first entry in an open-source […]

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