Most breach and attack simulation tools tell you what they found. VANGUARD tells you what it found, shows you the exact reasoning it used to find it, then writes the Elasticsearch detection rules you need to catch it next time – and deploys them automatically. Here’s how it works under the hood and what I learned building it. The Problem With Current BAS Tools Breach and Attack Simulation (BAS) tools like Cymulate, Pentera, and AttackIQ work by replaying […]

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Low-rank adaptation (LoRA) is a machine learning technique that modifies a pretrained model (for example, an LLM or vision transformer) to better suit a specific, often smaller, dataset by adjusting only a small, low-rank subset of the model’s parameters. In this article, we will take a look at both LoRA and DoRA, which is a new promising alternative to LoRA.

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arXiv:2507.00402v3 Announce Type: replace Abstract: Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data — particularly at the node level — remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based approaches, which restrict output to pre-specified network statistics, or fail to preserve key structural properties of the network. In this work, we propose GRAND (Graph Release with Assured Node Differential privacy), […]

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arXiv:2505.12096v4 Announce Type: replace-cross Abstract: The statistical properties of deep neural networks (DNNs) at initialization play an important role to comprehend their trainability and the intrinsic architectural biases they possess before data exposure Well established mean field (MF) theories have uncovered that the distribution of parameters of randomly initialized networks strongly influences the behavior of the gradients, dictating whether they explode or vanish. Recent work has showed that untrained DNNs also manifest an initial guessing bias (IGB), in […]

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Gwtar: a static efficient single-file HTML format Fascinating new project from Gwern Branwen and Said Achmiz that targets the challenge of combining large numbers of assets into a single archived HTML file without that file being inconvenient to view in a browser. The key trick it uses is to fire window.stop() early in the page to prevent the browser from downloading the whole thing, then following that call with inline tar uncompressed content. It can then make HTTP […]

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I’ve been thinking a lot about the relationship between reinforcement learning and neuroscience lately, and something about the usual framing doesn’t quite capture it. People often say the two fields developed in parallel. But historically it feels more like a spiral. Ideas move from neuroscience into computational models, then back again. Each turn sharpens the other. I’m considering writing a deep dive series about this, tentatively called “The RL Spiral.” The goal would be to trace how ideas […]

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arXiv:2603.19808v2 Announce Type: replace-cross Abstract: Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation with slower population-level adaptation. Despite their empirical success, a general mathematical description of the resulting collective training dynamics remains incomplete. We introduce a theoretical framework for neural network training based on two-time-scale population dynamics. We model a population of neural networks as an interacting agent system in which network parameters evolve through fast noisy gradient updates […]

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John M. Smart suggests the universe may behave like a growing organism. It mixes random creativity (evolution) with guided development, gradually producing more complex intelligence—eventually technology and perhaps cosmic-scale minds. In this view, instead of being mere accidents, civilizations may actually be tiny steps as the universe learns how to think.

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arXiv:2410.05263v2 Announce Type: replace Abstract: Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Prediction (CP) provides a robust framework for producing calibrated prediction intervals, but standard CP formulations face a critical challenge in the imaging pipeline: common mismatches between image reconstruction objectives and downstream metrics can introduce systematic prediction deviations from ground truth values, known as bias. These biases in turn compromise the efficiency […]

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arXiv:2601.04222v1 Announce Type: new Abstract: Many documentaries on early house and techno music exist. Here, protagonists from the scenes describe key elements and events that affected the evolution of the music. In the research community, there is consensus that such descriptions have to be examined critically. Yet, there have not been attempts to validate such statements on the basis of audio analyses. In this study, over 9,000 early house and techno tracks from Germany and the United States […]

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