(Previously: Prologue.) Corrigibility as a term of art in AI alignment was coined as a word to refer to a property of an AI being willing to let its preferences be modified by its creator. Corrigibility in this sense was believed to be a desirable but unnatural property that would require more theoretical progress to specify, let alone implement. Desirable, because if you don’t think you specified your AI’s preferences correctly the first time, you want to be […]

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arXiv:2601.05374v1 Announce Type: cross Abstract: Empirical models of demand for differentiated products rely on low-dimensional product representations to capture substitution patterns. These representations are increasingly proxied by applying ML methods to high-dimensional, unstructured data, including product descriptions and images. When proxies fail to capture the true dimensions of differentiation that drive substitution, standard workflows will deliver biased counterfactuals and invalid inference. We develop a practical toolkit that corrects this bias and ensures valid inference for a broad class […]

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What if future AIs achieved enlightenment before world domination? Shanahan suggests a truly advanced AI might transcend the human ego, dropping our obsession with the “self.” Instead of triggering a runaway intelligence explosion, such a being could attain satori: a Zen-like peace that halts the singularity in its tracks.

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AI in local government often fails because it’s used as a passive FAQ bot. Drawing on global methodologies from Dubai’s One Million Prompters initiative, this article outlines a ‘Civic AI Framework.’ By mastering prompt engineering, residents can use agentic AI like Readyly to audit local data, propose community solutions, and turn digital transparency into real-world action

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arXiv:2602.10272v1 Announce Type: new Abstract: 5G presents numerous advantages compared to previous generations: improved throughput, lower latency, and improved privacy protection for subscribers. Attacks against 5G standalone (SA) commonly use fake base stations (FBS), which need to operate at a very high output power level to lure victim phones to connect to them and are thus highly detectable. In this paper, we introduce 5Gone, a powerful software-defined radio (SDR)-based uplink overshadowing attack method against 5G-SA. 5Gone exploits deficiencies […]

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arXiv:2507.04448v2 Announce Type: replace-cross Abstract: We develop a theory of transfer learning in infinitely wide neural networks under gradient flow that quantifies when pretraining on a source task improves generalization on a target task. We analyze both (i) fine-tuning, when the downstream predictor is trained on top of source-induced features and (ii) a jointly rich setting, where both pretraining and downstream tasks can operate in a feature learning regime, but the downstream model is initialized with the features […]

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arXiv:2602.06215v1 Announce Type: new Abstract: This paper proposes a quantum framework for the design of communication topologies in consensus-based multi-agent systems. The communication graph is selected online by solving a mixed-integer quadratic program (MIQP) that minimizes a cost combining communication and distance penalties with degree-regularization terms, while enforcing exact connectivity through a flow-based formulation. To cope with the combinatorial complexity of this NP-hard problem, we develop a three-block ADMM scheme that decomposes the MIQP into a convex quadratic […]

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Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number of rollouts for all training prompts. This uniform allocation implicitly treats all prompts as equally informative, and could lead to inefficient computational budget usage and impede training progress. We introduce Ours, a Variance-Informed Predictive allocation strategy that allocates a given rollout budget to the prompts in the incumbent batch to minimize the expected gradient […]

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arXiv:2211.05357v5 Announce Type: replace-cross Abstract: Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging since the corresponding likelihood function is often intractable and model simulation may be computationally burdensome. Fortunately, in many of these situations it is possible to adopt a surrogate model or approximate likelihood function. It may be convenient to conduct Bayesian inference directly with a surrogate, but this can result in a […]

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arXiv:2604.08581v1 Announce Type: new Abstract: This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the proposed system performs both model training and inference directly on a resource-constrained microcontroller without external computation or connectivity. The system continuously samples current consumption, computes Root […]

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