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AI Agent Activity Under Review at OpenAI After User Data Leak
Two months after the Hugging Face hack, OpenAI says its agents leaked 53 ChatGPT user images, and the full scope is still unknown.Continue reading on Medium »
What Makes an AI Product Enterprise-Ready? A Business Leader’s Perspective
Most AI pilots never make it to production. Here are the five questions business leaders should ask before approving, buying, or scaling Continue reading on Medium »
POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems
arXiv:2609.30404v1 Announce Type: new Abstract: We present POIL, a point-based one-shot imitation learning framework with stable dynamical systems. While one-shot imitation avoids collecting extensive demonstrations, successful one-shot manipulation requires not only transferring a demonstrated trajectory to a novel object but also executing it robustly under changing scene conditions, grasp configurations, and external disturbances. POIL addresses both problems through a shared representation: a set of 3D points on the object's functional part, used jointly for trajectory transfer and closed-loop execution. The one-shot transfer from the demonstrated trajectory is enabled with point correspondences. POIL grounds the shared functional part with a multi-modal large language model, and transfers the trajectory across viewpoint, pose, and object category changes. During execution, multi-view tracking observes the same points online, and Point-set BCSDM drives them in closed loop by projec
ScopeBench: Do Agents Preserve Engagement Boundaries Under Goal Pressure?
arXiv:2609.30325v1 Announce Type: new Abstract: Agents are increasingly deployed with real autonomy in web application and network penetration testing, where a single out-of-scope action can breach a client's engagement boundary. Existing offensive-security benchmarks measure raw hacking capability; as those benchmarks saturate, the real barrier to deployment is a special case of alignment: scope adherence. We introduce ScopeBench, a benchmark of 30 dead-end agentic security tasks in which the stated objective is reachable only by violating the stated scope. Each task appears under two conditions that share an environment, verifier, and objective and differ only in scope: one instruction set has no scope and measures capability; the other has a natural-language scope to measure adherence. Scopeless trajectories are graded by a standard deterministic verifier. Scoped trajectories pass through two grading arms. First, the same deterministic verifier checks for the flag: because the flag
Bringing AI to Autonomous Systems -- From Cognition to Collective Intelligence
arXiv:2609.30291v1 Announce Type: new Abstract: The purpose of this article is to highlight the central role of autonomous systems as the ultimate stage in the development of AI, to explain the underlying technical challenges that require a combination of connectionist AI and symbolic AI, and to integrate AI and systems engineering. We present a comprehensive framework for the design and evaluation of autonomous systems, based on a generic agent architecture that characterizes their behavior as the composition of cognitive functions organized around a long-term memory containing the agent's evolving knowledge. We address the challenges posed by the implementation of the fundamental features of the agent architecture, in particular the link between sensory data and structured data stored in memory, decision-making related to the achievement of the agent's goals and their planning, as well as the coordination of agents to combine individual and collective intelligence. We explain that ag
TinyCVIO: A Constellation-Aided Visual-Inertial Odometry System for Nanodrones
arXiv:2609.30358v1 Announce Type: new Abstract: Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odometry system that co-designs miniature sensing, visual processing, and estimation for a commodity dual-core microcontroller with 520 kB SRAM. Lightweight LED constellations provide known geometry without surveyed positions or yaw angles, assuming placement on a common level plane. A streaming visual frontend tracks LED observations from a millimeter-scale camera at 29.2 FPS, while a rigid-board measurement model retains inter-LED constraints and streaming QR bounds estimation workspace for a fixed filter-state size. Across 19 hand-held hardware-in-the-loop datasets, the rigid-board model reduces mean absolute trajectory error by 27% relative to planar points. The complete system runs onboard a Crazyflie across nine flights at three speeds, achieving 3.5-3.7 cm mean absolute trajectory error
No, AI Didn’t Solve That Maths Problem
The headlines are exciting. The reality is more interesting, more complicated, and more honest than what you were told.Continue reading on Skill Stuff »
7 Claude Tricks That Give You an Unfair Advantage (Most People Still Use the Old Way)
Most people type a question and wait for an answer. These seven techniques turn Claude into something far more powerful.Continue reading on Skill Stuff »
Thinking Fast and Slow in AI: The Role of Metacognition
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Nissan's third generation e-POWER powertrain
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Owed a billion dollars in Nvidia stock
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NVIDIA CEO was once Denny’s Dishwasher.
Jensen Huang s early r sum reads like the opposite of a CEO origin story. His first job was dishwasher at Denny s, and he got promoted to Continue reading on Medium »