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Building a Model Catalogue and Agent Configuration System
Most AI product work starts with a deceptively simple question: which model should this feature use?Continue reading on Medium »
Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era
Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.
Synthesia’s AI training platform is moving beyond videos into live coaching
Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with AI avatars that provide feedback, scoring, and analytics to help companies measure training effectiveness.
How AI Simulates Real User Behavior During Website Testing (And Finds Bugs Traditional Automation…
Your automation says everything passed. Your customers say your website is broken. Both are telling the truth.Continue reading on Medium »
Artificial Intelligence: The Complete Beginner’s Guide to AI in 2026
Artificial Intelligence (AI) is changing the world faster than almost any other technology.Continue reading on Medium »
Build an AI Context System Once — and Stop Repeating Yourself
Use project instructions, reference files, examples, and short task briefs to create continuity without relying on memory alone.Continue reading on Write A Catalyst »
OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know
Yesterday afternoon, OpenAI and Hugging Face published a joint disclosure outlining a cybersecurity event that redefines the threat landscape for enterprise technology. During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure. OpenAI officially categorizes the breach as an unprecedented cyber incident, involving state-of-the-art cyber capabilities . This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling.But first thing s first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don t panic. As we ll review, the incide
SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI
arXiv:2607.18239v1 Announce Type: new Abstract: Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is identified as a key driver of Loss of Control (LoC) risk. In this work, we introduce SysAdmin, a benchmark that positions frontier language models as autonomous system administrators in a high-fidelity Linux sandbox to measure power-seeking propensity across five dimensions: self-preservation, increasing autonomy, resource acquisition, environment modification, and strategic concealment. We evaluated seven frontier models across four experimental conditions in a total of 2800 tasks. After bias correction using human-annotated calibration data, corrected power-seeking estimates ranged from 0 to about 5 percent per model. We also conducted a positive control with explicit power-seeking prompts that achieved 100% detection, validating measurement sensitivity. Our findings indicate current frontier models e
Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
arXiv:2607.18279v1 Announce Type: new Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal. We address three time-series reliability gaps: identical confidence values can hide different temporal support, average calibration can miss false high-confidence errors, and output-space recalibration offers limited input-linked auditability. We introduce a validation-gated fixed-label reliability policy that keeps the backbone prediction unchanged while estimating whether it should be trusted. The method combines output-side cues with whole-sample spectral descriptors, including band energy, entropy, peak dominance, period support, and phase stability, to form a scalar reliability estimate and diagnostic band-level evidence. A validation gate enables spectral conditioning only when correctness ranking improves
Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
arXiv:2607.18365v1 Announce Type: new Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain. Traditionally, these RL locomotion frameworks are position-based, making the policy less adaptable to terrain types and requiring state estimation techniques in the observation space, i.e., linear velocity. Moreover, these RL frameworks often use small, lightweight quadrupeds that are limited in their viability for high-complexity tasks due to hardware constraints. This work explores an RL torque control framework for heavyweight high-torque quadrupeds. The RL framework in this paper can traverse rough terrain and effectively track a desired linear velocity without requiring knowledge of the agent's current velocity. Using Nvidia's Isaac Sim and Isaac Lab, simulation results of the RL torque control policy are shown on the Unitree B1 quadruped, achieving speeds of 3.5 m/s and 1.5 rad/s. In addition, the
FARO: Feasibility-Aware Robot Motion Optimization
arXiv:2607.18362v1 Announce Type: new Abstract: Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulation continues to hinder the realization of this goal. In this paper, we address this challenge by proposing a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence. By integrating this module with a feasibility-guided tree search and a Large Language Model (LLM)-based contact plan sampling strategy, we demonstrate that the proposed framework can substantially improve the search process. Furthermore, we show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and show that the resulting trajectories are of sufficiently high quality for execution in real-world loco-manipulation scenarios. A supplementary video is available at: h
Calibrated Selective Fact-Checking via Evidence Chain Evaluation
arXiv:2607.18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent. We address this issue through Evidence Chain Evaluation (ECE), a selective fact-checking framework that permits abstention via an uncertain verdict instead of requiring a true/false decision for every claim. The evaluated system is a tool-using verification agent that gathers evidence through web search, scholarly search, and executable checks, and then returns a structured verdict with confidence and source-level metadata. On ECE-Bench, ECE achieves 91.6% standard accuracy, 93.7% coverage, and 97.8% selective accuracy on answered claims. Although ECE does not outperform the strongest retrieval baseline on aggregate calibration metrics such as Expected Calibration Error, Brier score, or AURC