arXiv:2604.02491v1 Announce Type: new Abstract: We study a problem of online targets coverage by a drone or a sensor that is equipped with a camera or an antenna of fixed half-angle of view $alpha$. The targets to be monitored appear at arbitrary positions on a line barrier in an online manner. When a new target appears, the drone has to move to a location that covers the newly arrived target, as well as already existing targets. The objective […]

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by worth, i mean, not only in introductory learning context. I think the answer is depending on a target business problem. honestly almost practical RL business problems require a continuous state/action space, so DQN is not competitive. but for example, in video games, will value learning methods still work effectively even compared to policy gradient and/or actor-critic methods? (assumption: the input is not raw pixel data, the reward is neither sparse nor raw score.) submitted by /u/Gloomy-Status-9258 [link] […]

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Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning […]

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arXiv:2603.02266v1 Announce Type: new Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to post-training for direct answering. To investigate it, we introduce CAFE, an evaluation framework designed to precisely quantify audio reasoning errors. Evaluation results reveal LALMs struggle with perception during reasoning and encounter a critical bottleneck: […]

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arXiv:2601.06478v1 Announce Type: cross Abstract: Gradient descent on log-sum-exp (LSE) objectives performs implicit expectation–maximization (EM): the gradient with respect to each component output equals its responsibility. The same theory predicts collapse without volume control analogous to the log-determinant in Gaussian mixture models. We instantiate the theory in a single-layer encoder with an LSE objective and InfoMax regularization for volume control. Experiments confirm the theory’s predictions. The gradient–responsibility identity holds exactly; LSE alone collapses; variance prevents dead components; decorrelation […]

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The ML stack is being rebuilt. In 2026, developers need to master foundation model routing (frontier vs. efficient), multi-agent orchestration, on-device inference with SLMs, rigorous LLMOps pipelines, AI governance for compliance, and keep an eye on physical AI. The winning teams won’t have the fanciest models – they’ll have the best evaluation frameworks and observability from day one.

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Chelsea Follett The debut of the robot butler NEO has drawn widespread ridicule. Unable to perform many chores without a remote human operator, the machine has become a target of social media backlash. Videos circulating online show the robot struggling with basic tasks, such as closing a dishwasher. , But don’t underestimate the potential of robotic housekeepers just yet. The technology is dawning at an opportune time. Consider the growing concerns about plummeting birth rates. Last year saw […]

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Gen Z isn’t “the future” audience anymore. They are the growth engine right now. By 2026, the brands that win with Gen Z won’t just market products—they’ll build communities that shape culture and commerce together. As a CMO working with Gen Z–first brands, I’ve seen a consistent pattern emerge: campaigns can still go viral, but the brands that build fandoms, not funnels, grow more efficiently and stick around longer. For years, marketers treated Gen Z like a puzzle: […]

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Hi, I’ll be doing research at Mila Quebec this summer, and I’d love some advice on how to and what to prepare. The topic is Causal models for continual reinforcement learning. More specifically, the project hypothesizes that agents whose goal is to maximize empowerment gains will construct causal models of their actions and generalize better in agentic systems. For some background, I’m a last semester McGill undergraduate majoring in Statistics and Software Eng. I’ve done courses about: -PGMs: […]

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