Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
arXiv:2604.09574v1 Announce Type: new
Abstract: The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimension of anti-detection. We argue that for agents to survive in human-ce...
AHC: Meta-Learned Adaptive Compression for Continual Object Detection on Memory-Constrained Microcontrollers
arXiv:2604.09576v1 Announce Type: new
Abstract: Deploying continual object detection on microcontrollers (MCUs) with under 100KB memory requires efficient feature compression that can adapt to evolving task distributions. Existing approaches rely on fixed compression strategies (e.g., FiLM conditio...
Quantum‑Accelerated AI: The First Real Break From the Scaling Wall
Quantum optimization is emerging as the first real escape from AI’s scaling limits—accelerating training, reducing compute costs, and redefining frontier models.
Google AI Research Proposes Vantage: An LLM-Based Protocol for Measuring Collaboration, Creativity, and Critical Thinking
Standardized tests can tell you whether a student knows calculus or can parse a passage of text. What they cannot reliably tell you is whether that student can resolve a disagreement with a teammate, generate genuinely original ideas under pressure, or critically dismantle a flawed argument. These a...
Your Model Isn’t Done: Understanding and Fixing Model Drift
How production models fail over time, and how to catch and fix it before it breaks trust.
The post Your Model Isn’t Done: Understanding and Fixing Model Drift appeared first on Towards Data Science.
“Giant superatoms” could finally solve quantum computing’s biggest problem
In the pursuit of powerful and stable quantum computers, researchers at Chalmers University of Technology, Sweden, have developed the theory for an entirely new quantum system – based on the novel concept of ‘giant superatoms’. This breakthrough enables quantum information to be protected, controlle...
By compiling a simple program directly into transformer weights.
The post I Built a Tiny Computer Inside a Transformer appeared first on Towards Data Science.
Comprehension Debt: The Hidden Cost of AI-Generated Code
The following article originally appeared on Addy Osmani’s blog site and is being reposted here with the author’s permission. Comprehension debt is the hidden cost to human intelligence and memory resulting from excessive reliance on AI and automation. For engineers, it applies most to agentic engin...
Moovila Earns Multiple AI Industry Honors, Recognised in Second CRN AI 100
Moovila, the leading AI-driven project automation platform for managed service providers (MSPs), has been named to the 2026 CRN AI 100 list in the AI Software category. This marks the second consecutive year Moovila has been recognized on the prestigious list, which highlights companies driving inno...
Multi-agent orchestration with human-in-the-loop oversight compresses full-scope pentest engagements from weeks to under 48 hours Strobes, a leader in Exposure Management, today announced the launch of its proprietary AI Harness, a multi-agent orchestration engine that powers end-to-end AI Penetrati...
Enterprises power agentic workflows in Cloudflare Agent Cloud with OpenAI
Cloudflare brings OpenAI’s GPT-5.4 and Codex to Agent Cloud, enabling enterprises to build, deploy, and scale AI agents for real-world tasks with speed and security.
MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search
MiniMax, the AI research company behind the MiniMax omni-modal model stack, has released MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, both to human developers working in a terminal and to AI agents running in tools like ...
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
arXiv:2604.08553v1 Announce Type: new
Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where la...
QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation
arXiv:2604.08570v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework familiarity. We introduce QuanBenc...
arXiv:2604.08571v1 Announce Type: new
Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly overfit to standard textual formatting. We propose a perturbation pipeline consisting of 14 techniques ...