AI Tools for an Overburdened Regulatory System - RealClearPolicy
<a href="https://news.google.com/rss/articles/CBMisAFBVV95cUxOVnZQYWFoQnRzU0hNZExHTTZubVdVZThNZVRqUTFqUkpIWXRXenl4Uk9PYjNhUTVMS0U2M202b2JmYUY3czdUREZ3cGV1ZGxyRTFmZ0c2YjZpS09kNlRKeWJXQVRWc1pJUzB1N1pJaG5DTFBnLWJacHkxTUs1VWJSRTFJUW90Z1J3Q19jc19pRFpGNVgtX0Y5WFRmX1k3eFI5UTltR3VPNkJZeng3M2ZGTw?oc=5" target="_blank">AI Tools for an Overburdened Regulatory System</a> <font color="#6f6f6f">RealClearPolicy</font>
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From edge to enterprise: How the endpoint became IT’s most strategic layer and why Lenovo is joining the conversation at IGEL Now & Next Miami
For years, the enterprise endpoint was treated as a commodity: a device to deploy, patch, and eventually replace. The real innovation was expected to happen in the data center or the cloud. That assumption is changing. In today’s distributed environments, endpoints have become a critical part of the digital workspace architecture. It is where users authenticate, where security policies are enforced, and where the experience of modern work is ultimately delivered. As organizations rethink hybrid work, zero trust security, and cloud-first applications, the endpoint is evolving from a simple access device into a strategic platform. This shift is why Lenovo is joining the conversation at IGEL Now & Next Miami 2026 , where technology leaders are exploring how the next generation of secure digit
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chat : add Granite 4.0 chat template with correct tool_call role mapping ( #20804 ) chat : add Granite 4.0 chat template with correct tool_call role mapping Introduce LLM_CHAT_TEMPLATE_GRANITE_4_0 alongside the existing Granite 3.x template (renamed LLM_CHAT_TEMPLATE_GRANITE_3_X ). The Granite 4.0 Jinja template uses XML tags and maps the assistant_tool_call role to assistant . Without a matching C++ handler, the fallback path emits the literal role assistant_tool_call which the model does not recognize, breaking tool calling when --jinja is not used. Changes: Rename LLM_CHAT_TEMPLATE_GRANITE to LLM_CHAT_TEMPLATE_GRANITE_3_X (preserves existing 3.x behavior unchanged) Add LLM_CHAT_TEMPLATE_GRANITE_4_0 enum, map entry, and handler Detection: + ( or ) → 4.0, otherwise → 3.x Add production Gr
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Relax prefill parser to allow space. ( #21240 ) Relax prefill parser to allow space. Move changes from prefix() to parser generation Only allow spaces if we're not having a pure content parser next macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
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arXiv:2506.02768v2 Announce Type: replace Abstract: When developing control laws for robotic systems, the principle factor when examining their performance is choosing inputs that allow smooth tracking to a reference input. In the context of robotic manipulation, this involves translating an object or end-effector from an initial pose to a target pose. Robotic manipulation control laws frequently use vision systems as an error generator to track features and produce control inputs. However, current control algorithms don't take into account the probabilistic features that are extracted and instead rely on hand-tuned feature extraction methods. Furthermore, the target features can exist in a static pose thus allowing a combined pose and feature error for control generation. We present a geo
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