Residential proxies are one of the most misunderstood tools in modern web development.  Some developers associate them only with scraping, others think they are a niche security trick, and many simply avoid them because the terminology sounds complex.  In reality, residential proxies are a practical infrastructure component that solves real engineering problems related to access, reliability, testing, and data collection.  If you build applications that interact with the public internet, understanding how residential proxies work can help you […]

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Every entrepreneur talks about vision, resilience, and execution. But beneath all of these traits lies a quieter, less visible skill that ultimately determines success or failure: decision-making under uncertainty. Markets shift, data is incomplete, and outcomes are rarely guaranteed. The leaders who endure are not those who wait for certainty—but those who learn how to think clearly without it. According to Alessio Vinassa, serial entrepreneur and business advisor, uncertainty is not an obstacle to leadership—it is the environment […]

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arXiv:2603.07230v1 Announce Type: cross Abstract: Intergenerational mobility quantifies the transmission of socio-economic outcomes from parents to children. While rank-rank regression (RRR) is standard, adding covariates directly (RRRX) often yields parameters with unclear interpretation. Conditional rank-rank regression (CRRR) resolves this by using covariate-adjusted (conditional) ranks to measure within-group mobility. We improve and extend CRRR by estimating conditional ranks with a deep conditional transformation model (DCTM) and cross-fitting, enabling end-to-end conditional distribution learning with structural constraints and strong performance under […]

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arXiv:2602.03889v1 Announce Type: new Abstract: Finite mixture models are widely used for unsupervised learning, but maximum likelihood estimation via EM suffers from degeneracy as components collapse. We introduce transcendental regularization, a penalized likelihood framework with analytic barrier functions that prevent degeneracy while maintaining asymptotic efficiency. The resulting Transcendental Algorithm for Mixtures of Distributions (TAMD) offers strong theoretical guarantees: identifiability, consistency, and robustness. Empirically, TAMD successfully stabilizes estimation and prevents collapse, yet achieves only modest improvements in classification accuracy-highlighting […]

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arXiv:2506.06446v2 Announce Type: replace-cross Abstract: Providers of LLM-as-a-service have predominantly adopted a simple pricing model: users pay a fixed price per token. Consequently, one may think that the price two different users would pay for the same output string under the same input prompt is the same. In our work, we show that, surprisingly, this is not (always) true. We find empirical evidence that, particularly for non-english outputs, both proprietary and open-weights LLMs often generate the same (output) […]

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Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challenging due to the high communication cost of federated optimization and the limited local data on clients. Federated prompt learning mitigates this issue by keeping the VLPM backbone frozen and collaboratively training lightweight prompt parameters. However, existing approaches typically enforce a unified prompt structure and length across clients, which is inadequate under practical client heterogeneity in both data distributions and system resources, and […]

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The evaluation of machine learning models typically relies mainly on performance metrics based on loss functions, which risk to overlook changes in performance in relevant subgroups. Auditing tools such as SliceFinder and SliceLine were proposed to detect such groups, but usually have conceptual disadvantages, such as the inability to directly address continuous covariates. In this paper, we introduce FairTree, a novel algorithm adapted from psychometric invariance testing. Unlike SliceFinder and related algorithms, FairTree directly handles continuous, categorical, and […]

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7B Models: Cheap, Fast… and Brutally Honest About Your Prompting If you’ve deployed a 7B model locally (or on a modest GPU), you already know the trade: Pros low cost low latency easy to self-host Cons patchy world knowledge weaker long-chain reasoning worse instruction-following unstable formatting (“JSON… but not really”) The biggest mistake is expecting 7B models to behave like frontier models. They won’t. But you can get surprisingly high-quality output if you treat prompting like systems design, […]

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