February 2026

In multi-intent intent-based networks, a single fault can trigger co-drift where multiple intents exhibit symptomatic KPI degradation, creating ambiguity about the true root-cause intent. We present MILD, a proactive framework that reformulates intent assurance from reactive drift detection to fixed-horizon failure prediction with intent-level disambiguation. MILD uses a teacher-augmented Mixture-of-Experts where a gated disambiguation module identifies the root-cause intent while per-intent heads output calibrated risk scores. On a benchmark with non-linear failures and co-drifts, MILD provides 3.8%–92.5% longer […]

Ver mais

This article is from August 2024, some of its contents might be outdated and no longer accurate.You can find up-to-date information about the engine in the official documentation. In Godot 4.3 we are adding a new node called SkeletonModifier3D. It is used to animate Skeleton3Ds outside of AnimationMixer and is now the base class for several existing nodes. As part of this we have deprecated (but not removed) some of the pose override functionality in Skeleton3D including: set_bone_global_pose_override() […]

Ver mais

Self-supervised learning aims to learn maximally informative representations, but explicit information maximization is hindered by the curse of dimensionality. Existing methods like VCReg address this by regularizing first and second-order feature statistics, which cannot fully achieve maximum entropy. We propose Radial-VCReg, which augments VCReg with a radial Gaussianization loss that aligns feature norms with the Chi distribution-a defining property of high-dimensional Gaussians. We prove that Radial-VCReg transforms a broader class of distributions towards normality compared to VCReg and […]

Ver mais

With the rapid increase in residential heat pump (HP) installations, optimizing hot water production in households is essential, yet it faces major technical and scalability challenges. Adapting production to actual household needs requires accurate forecasting of hot water demand to ensure comfort and, most importantly, to reduce energy waste. However, the conventional approach of training separate machine learning models for each household becomes computationally expensive at scale, particularly in cloud-connected HP deployments. This study introduces DELTAiF, a transfer […]

Ver mais

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 […]

Ver mais

Understanding an agent’s goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-oriented policy representation, which may differ from the actor’s true behavior and hinder the accurate recognition of their goal. To address this gap, this paper introduces Goal Recognition Alignment through Imitation Learning (GRAIL), which leverages imitation learning and inverse reinforcement learning to learn one goal-directed policy for each candidate goal directly from (potentially […]

Ver mais

Pinching antennas systems (PASSs) have recently been proposed as a novel flexible-antenna technology. These systems are implemented by attaching low-cost pinching elements to dielectric waveguides. As the direct link is bypassed through waveguides, PASSs can effectively compensate large-scale effects of the wireless channel. This work explores the potential gains of employing PASSs for over-the-air federated learning (OTA-FL). For a PASS-assisted server, we develop a low-complexity algorithmic approach, which jointly tunes the PASS parameters and schedules the mobile devices […]

Ver mais

Federated Learning provides a privacy-preserving paradigm for distributed learning, but suffers from statistical heterogeneity across clients. Personalized Federated Learning (PFL) mitigates this issue by considering client-specific models. A widely adopted approach in PFL decomposes neural networks into a shared feature extractor and client-specific heads. While effective, this design induces a fundamental tradeoff: deep or expressive shared components hinder personalization, whereas large local heads exacerbate overfitting under limited per-client data. Most existing methods rely on rigid, shallow heads, and […]

Ver mais

It’s wild that the first commit to OpenClaw was on November 25th 2025, and less than three months later it’s hit 10,000 commits from 600 contributors, attracted 196,000 GitHub stars and sort-of been featured in an extremely vague Super Bowl commercial for AI.com. Quoting AI.com founder Kris Marszalek, purchaser of the most expensive domain in history for $70m: ai.com is the world’s first easy-to-use and secure implementation of OpenClaw, the open source agent framework that went viral two […]

Ver mais