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Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies
arXiv:2608.07546v1 Announce Type: new Abstract: Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics. However, DRL methods often require extensive training time, and the resulting policies do not generalize well to different robot configurations or varying numbers of actuators. In this article, we introduce a novel DRL approach for controlling CDPRs that does not depend on the specific robot configuration. Our method trains an actuator-level policy that controls each motor to achieve its target cable length, in contrast to conventional DRL approaches that learn to control the entire robot to reach a desired end-effector position. To the best of our knowledge, this is the first work to apply DRL to control CDPRs using an actuator-level policy. This approach offers two main advantages: (i) a single shared policy can be applied to any CDPR configu
Towards an Argumentative Foundation for Evaluative AI
arXiv:2608.07473v1 Announce Type: new Abstract: Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each. In this position paper, we advocate (computational) argumentation as a particularly suitable paradigm to provide a formal, computable foundation for forms of EAI that are explainable and contestable, setting the ground for a long-term research agenda towards distributed and human-centred EAI systems.
Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
arXiv:2608.07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions. We challenge this paradigm by showing that how data is discretized matters more than which model is used. We propose an unsupervised fire-zone segmentation algorithm combining watershed detection with K-means clustering to define prediction units directly from historical fire patterns. Experiments across six French departments and six forecasting models show that fire-zone segmentation consistently outperforms grid-based approaches, with mean IoU improvements of +3--6% depending on spatial scale. The method is computationally lightweight (<10s per configuration) and fully parallelizable. Our results demonstrate that optimizing spatial discretization yields significant, reproducible performance gains for short-term wildfire forecasting.
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OpenAI reportedly completed a $7 billion employee tender offer
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As AI-led attacks multiply, OpenAI launches a new cyber model
OpenAI is expanding its AI cybersecurity defense program Daybreak, and rolling out a new cyber-trained AI model with it.
OpenAI launches GPT-5.6-Cyber with reduced refusals, 95% completion on advanced cybersecurity tasks
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