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The GigaOm Radar for Attack Surface Management v5, published February 2026 and authored by analysts Chris Ray and Whit Walters, is one of the most comprehensive independent assessments of the EASM market. Now in its fifth year, the report evaluated 32 vendors against a rigorous set of key features, emerging capabilities and business criteria. This year’s findings reflect a decisive market shift from raw asset discovery toward exposure management and alignment with continuous threat exposure management (CTEM) frameworks. […]

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State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this work, we explore a long CoT setting where the model makes up to K successive attempts at solving a problem, in which each attempt is allowed to build on earlier ones after the model receives a hard verifier feedback. This motivates RL methods that can harness per-attempt rewards by carefully weighting individual attempts. We study optimizing the Verification@K reward (the […]

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Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. While this paradigm has shown strong performance, prevailing VLA training procedures do not explicitly supervise hard physical constraints such as obstacle avoidance or kinematic feasibility. As a result, the geometric structure underlying physically feasible behavior must be inferred only implicitly from demonstrations. In this paper, we study whether introducing explicit feasibility supervision can provide effective structured guidance for VLA policies. […]

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Since the beginning of modern computer history, the Turing machine has been a dominant architecture for most computational devices, which consists of three essential components: an infinite tape for input, a read/write head, and finite control. In this structure, what the head can read (i.e., binary digits and symbols) is the same as what it has written/outputted. This is actually different from the ways in which humans think or do thought/tool experiments, where inputs can be abstract symbols […]

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In this, Part 3 of the paper, analytic solution of all six integrals for determination of the area and moments of arbitrary focal sector of ellipse is given, for ellipse defined in parametrical form by trigonometric functions, based on its corresponding integrals of its central sector from Part 1 of the paper, similar to Part 2 for arbitrary segment. Those integrals are here again built from the general moment form of Green’s theorem curve integral for the calculation […]

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I have done some research in RL and have some problem statements which I would love to do a PhD on instead of my sde job. I also have the money to be able to go abroad and pursue it. However I can’t make decisions solely based on interest and without giving zero thought about the future. Hence the confusion. On one hand I feel like its a good idea to pursue this even from future prospects because […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## SEO Isn’t Dead But Your Strategies Have to Change By @deeflect [ 10 Min read ] CTRs are down 30%, and AI Overviews are everywhere. SEO isn’t dead, but the rules shifted. Here’s what GEO is and why brand mentions now outrank backlinks 3:1 […]

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Healthcare analytics is often limited by amounts of data and strict privacy requirements, which make it difficult to share patient-level records across organisations and to build robust predictive models. Federated learning (FL) provides an alternative by keeping data local and exchanging model updates instead of raw records. However, many existing FL solutions remain difficult to deploy in healthcare settings, as they provide limited support for auditability, governance-oriented evidence, and system-level transparency. This paper presents MediVault, an auditable and […]

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This paper presents a hybrid model that combines linguistic data summarization techniques, algorithms for constructing probabilistic trees, and various Generative Artificial Intelligence models for learning and generating linguistic summaries to aid decision-making. The proposal is validated using methodological triangulation techniques that demonstrate high consistency in the knowledge discovered. The proposal also compares different Generative Artificial Intelligence models; among the evaluated models, Gemini achieved the best performance. However, it is evident that in certain contexts and tasks, small language […]

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