In 1973, engineers at General Dynamics rolled out a prototype for a new fighter jet. It was called the YF-16. It resembled a standard combat plane. It had cropped-delta wings. It had a massive engine. It had a sleek cockpit. But it harbored a deeply counterintuitive secret. It was intentionally designed to be impossible to fly. Prior to the F-16, a pilot was physically connected to the airplane. When a pilot pulled the stick back, mechanical cables physically […]

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arXiv:2603.16964v1 Announce Type: new Abstract: Approval of ADS depends on evaluating its behavior within representative real-world traffic scenarios. A common way to obtain such scenarios is to extract them from real-world data recordings. These can then be grouped and serve as basis on which the ADS is subsequently tested. This poses two central challenges: how scenarios are extracted and how they are grouped. Existing extraction methods rely on heterogeneous definitions, hindering scenario comparability. For the grouping of scenarios, […]

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People think that becoming a writer is easy. In some ways, it is. All you need is a pen and paper or a computer, an idea, and the determination to write. Voila, you’re a writer now. Writing is easy; becoming serious about your writing is a whole lot harder and more complex. However, why should it be? Writers should be helping each other navigate this crazy world whenever possible. So, if you’re reading this right now, then that […]

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arXiv:2601.23031v1 Announce Type: new Abstract: We study a class of iterated empirical risk minimization (ERM) procedures in which two successive ERMs are performed on the same dataset, and the predictions of the first estimator enter as an argument in the loss function of the second. This setting, which arises naturally in active learning and reweighting schemes, introduces intricate statistical dependencies across samples and fundamentally distinguishes the problem from classical single-stage ERM analyses. For linear models trained with a […]

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arXiv:2603.25750v1 Announce Type: new Abstract: As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing […]

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Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structured prediction. In many practical settings, the graphical model remains fixed while inference must be performed repeatedly for varying evidence patterns. Stochastic Local Search (SLS) algorithms scale to large models but rely on myopic best-improvement rule that prioritizes immediate likelihood gains and often stagnate in poor local optima. Heuristics such as Guided Local […]

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