February 2026

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. ## The Hidden Problem With Group Rewards in Multi-Agent AI By @praveenmyakala [ 3 Min read ] Group rewards are breaking your multi-agent RL training. Decoupled normalization keeps coordination intact while stopping gradient collapse. Read More. Architecting Trustworthy Healthcare Data Platforms Using Declarative Pipelines By […]

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If one looks attentively, without superfluous emotions, at the technological market of the beginning of 2026, one can sense a strange, I would even say, paradoxical cognitive dissonance. The situation is twofold. On the one hand, we observe incredible, simply cosmic technical progress: models have become smarter, they have become faster and, what is critically important thanks to aggressive price pressure from the side of new ambitious players like DeepSeek, they have become dramatically cheaper. Computational capacities, which […]

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A few weeks ago I posted here asking for advice about a Dueling DDQN chess agent that completely collapsed after I pretrained it with supervised learning. Several people pointed out that the issue might be the transition from supervised learning to value-based RL, and that actor-critic methods might be a better fit. They were right. I had been treating the Q-values as logits. Using cross-entropy loss during supervised learning meant that the “correct” Q-value (the expert move) was […]

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I think I have 2 options for my next project , either build something like my passion project to showcase my skills or build a project that solves a real problem but I won’t be able to show my skills as much as the latter .. which do you think should be more impactful and good for portfolio(Rl-project) and tbh I can only create a protype I was thinking some rl project for my college .. or do […]

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The dominant story about AI is told like a weather report: it’s coming, it’s inevitable, it will accelerate, take over work, reshape society and government, and the only smart move is to adapt. It puts technology at the center of gravity and pushes humans out of the frame, turning the tool into the main character and leaving the rest of us as scenery. We keep framing the future as “technology-driven.” We accept that AI will speed up work […]

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arXiv:2602.04795v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique. Given a nonnegative data matrix $X$, it aims at finding two lower dimensional matrices, $W$ and $H$, such that $Xapprox WH$, where the factors $W$ and $H$ are constrained to be element-wise nonnegative. The factor $W$ serves as a basis for the columns of $X$. In order to obtain more interpretable and unique solutions, minimum-volume NMF (MinVol NMF) minimizes the volume of $W$. […]

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arXiv:2602.04408v2 Announce Type: replace-cross Abstract: We study the Pareto frontier (optimal trade-off) between utility and separation, a fairness criterion requiring predictive independence from sensitive attributes conditional on the true outcome. Through an information-theoretic lens, we prove a characterization of the utility-separation Pareto frontier, establish its concavity, and thereby prove the increasing marginal cost of separation in terms of utility. In addition, we characterize the conditions under which this trade-off becomes strict, providing a guide for trade-off selection in […]

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arXiv:2602.02819v2 Announce Type: replace-cross Abstract: Membership Inference Attacks (MIAs) are widely used to quantify training data memorization and assess privacy risks. Standard evaluation requires repeated retraining, which is computationally costly for large models. One-run methods (single training with randomized data inclusion) and zero-run methods (post hoc evaluation) are often used instead, though their statistical validity remains unclear. To address this gap, we frame MIA evaluation as a causal inference problem, defining memorization as the causal effect of including […]

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arXiv:2602.01607v2 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors. A central challenge is to achieve strong utility guarantees for meaningful downstream analysis. Many existing methods ensure uniform accuracy over broad query classes, such as all Lipschitz functions, but this level of generality often leads to suboptimal rates for statistics of practical interest. Since many common data analysis queries exhibit smoothness beyond what […]

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arXiv:2511.08667v2 Announce Type: replace-cross Abstract: The first tabular foundation model, TabPFN, and its successor TabPFNv2 have impacted tabular AI substantially, with dozens of methods building on it and hundreds of applications across different use cases. This report introduces TabPFN-2.5, the next generation of our tabular foundation model, built for datasets with up to 50,000 data points and 2,000 features, a 20x increase in data cells compared to TabPFNv2. TabPFN-2.5 is now the leading method for the industry standard […]

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