arXiv:2602.20370v1 Announce Type: new Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitative approximation rates for ReLU networks on the class of $alpha$-H”older functions $f: [0,1]^N to mathbb{R}$. The goal of this paper is to provide similar quantitative approximation results in the context of group equivariant learning, where the learned $alpha$-H”older function is known to obey certain group symmetries. While there has […]

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I finally did it. Last week, I built a prompt that generated a flawless documentation site from a GitHub repo. It was beautiful. I felt like a wizard. I even bookmarked it as my “Gold Standard” prompt. Then, yesterday happened. I ran the exact same prompt on a new repo—similar structure, similar size—and it was a total disaster. The AI started ignoring the CSS requirements, forgot to link the sub-pages, and kept trying to write the docs in […]

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arXiv:2510.22031v2 Announce Type: replace-cross Abstract: Causal discovery from observational data is a fundamental task in artificial intelligence, with far-reaching implications for decision-making, predictions, and interventions. Despite significant advances, existing methods can be broadly categorized as constraint-based or score-based approaches. Constraint-based methods offer rigorous causal discovery but are often hindered by small sample sizes, while score-based methods provide flexible optimization but typically forgo explicit conditional independence testing. This work explores a third avenue: developing differentiable $d$-separation scores, obtained through […]

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arXiv:2510.22068v2 Announce Type: replace-cross Abstract: Learning mappings between functional spaces, also known as function-on-function regression, is a fundamental problem in functional data analysis with broad applications, including spatiotemporal forecasting, curve prediction, and climate modeling. Existing approaches often struggle to capture complex nonlinear relationships and/or provide reliable uncertainty quantification when data are noisy, sparse, or irregularly sampled. To address these challenges, we propose Deep Gaussian Processes for Functional Maps (DGPFM). Our method constructs a sequence of GP-based linear and […]

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arXiv:2604.08609v1 Announce Type: new Abstract: Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts may contain explicit or implicit expressions of harm, hate, threat, violence, or intimidation, yet existing automated approaches often assume clean text input or apply vision models without forensic justification. This paper presents a case-driven multimodal approach for hate and threat detection in forensic analysis. The proposed framework explicitly determines the presence and source of textual […]

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Published on December 3, 2025 6:37 PM GMT Tl;dr AI alignment has a culture clash. On one side, the “technical-alignment-is-hard” / “rational agents” school-of-thought argues that we should expect future powerful AIs to be power-seeking ruthless consequentialists. On the other side, people observe that both humans and LLMs are obviously capable of behaving like, well, not that. The latter group accuses the former of head-in-the-clouds abstract theorizing gone off the rails, while the former accuses the latter of mindlessly assuming […]

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Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are impractical in production due to latency constraints and operational complexity. Instead of discarding them, Knowledge Distillation offers a smarter approach: keep the ensemble as a teacher and train a smaller student model using its soft probability outputs. This allows the student to inherit much of the ensemble’s performance while being lightweight and fast enough […]

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arXiv:2603.22404v1 Announce Type: new Abstract: Consider a market of competing model providers selling query access to models with varying costs and capabilities. Customers submit problem instances and are willing to pay up to a budget for a verifiable solution. An arbitrageur efficiently allocates inference budget across providers to undercut the market, thus creating a competitive offering with no model-development risk. In this work, we initiate the study of arbitrage in AI model markets, empirically demonstrating the viability of […]

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