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The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, recent theoretical analyses suggest that reinforcement learning-based training does not acquire new capabilities but merely redistributes the utilization probabilities of existing ones. In this study, we propose “compound jailbreaks” targeting OpenAI gpt-oss-20b, which exploit the generalization failures of alignment. This approach combines multiple attack techniques — each individually defended against — to saturate the instruction hierarchy maintenance process. […]

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Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably precipitates feature drift. As a nascent alternative, Analytic Continual Learning (ACL) capitalizes on the intrinsic generalization properties of frozen pre-trained models to bolster continual learning performance. Nonetheless, a key drawback resides in the pronounced attenuation of model plasticity. To surmount these challenges, we […]

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In conversations about customer data, one question comes up again and again: if both CDPs and MDMs help create a more complete view of the customer, are they basically doing the same thing? It is an understandable question. After all, both technologies are often positioned around customer unification, identity resolution, and creating better visibility across systems. On the surface, they can sound very similar. But while CDPs and MDMs do overlap in some areas, they are not the […]

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Fetch, parse, and store is a web scraping order traditionally effective for most data pipelines. Up until recently, it was the dominating way to collect data, even at scale. With the rise of AI crawlers, however, more sophisticated anti-scraping strategies have become prevalent across the web. Websites have the right to defend themselves from malicious bots, but legitimate public data collection is affected as well. The traditional web scraping process must be rethought, with parsing becoming a part […]

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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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Local LLMs are great – until you have to manage them on multiple machines. Ollama makes it easy to run models like Qwen, Mistral, and Gemma on consumer hardware, but most tools stop at “chat UI”. This tutorial shows how to build a production-ready admin dashboard for Ollama that runs on Android and Desktop with about 95% shared Kotlin Multiplatform code. You will: Build a Compose Multiplatform app with a shared UI layer for Android and Desktop. Implement […]

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How controlled DNS, segmented connectivity, and deterministic routing reduce financial risk in distributed pricing systems 1. The Missing Layer in Financial Automation Automated pricing systems are typically discussed in terms of: pricing algorithms competitive strategies machine learning models data pipelines However, at scale, a critical component is often overlooked: the network layer. In distributed pricing infrastructure, financial safety depends not only on how prices are calculated, but also on how reliably and predictably the system can: reach external […]

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There’s a conversation happening right now in every professional services firm, every consultancy, every law office, every policy shop in the country. It goes something like this: “We should be using AI. But what should we be using it for?” And the answers, at least at an enterprise level, are mostly the same. Summarize this document. Draft this email. Research this topic. The tasks are real, the outputs are fine, and the productivity gains are modest enough that […]

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Reinforcement Learning (RL) has been shown to substantially improve the reasoning capability of small and large language models (LLMs), but existing approaches typically rely on verifiable rewards, hence ground truth labels. We propose an RL framework that uses rewards from an LLM that acts as a judge evaluating model outputs over large amounts of unlabeled data, enabling label-free knowledge distillation and replacing the need of ground truth supervision. Notably, the judge operates with a single-token output, making reward […]

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