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The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
Muntasir Adnan, Carlos C. N. Kuhn
https://arxiv.org/abs/2506.18403 https://…

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strateg…

heise | macOS: Stable Diffusion in die eigene App integrieren
Ohne Training, aber mit dem vollen Potenzial von Apples Hardware: So binden Sie den Text-zu-Bild-Generator in Ihre eigene App ein.
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Ranking each division by quarterback heading into the 2025 NFL season https://www.nfl.com/news/ranking-each-division-by-quarterback-heading-into-the-2025-nfl-season

Did adding George Pickens make Dallas’ WRs the division’s best? https://insidethestar.com/did-adding-george-pickens-make-dallas-wrs-the-divisions-best

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
Yunyi Zhao, Wei Zhang, Cheng Xiang, Hongyang Du, Dusit Niyato, Shuhua Gao
https://arxiv.org/abs/2507.16867

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
This paper introduces DiffCarl, a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems. With the growing integration of renewables and increasing system complexity, microgrid communities face significant challenges in real-time energy scheduling and optimization under uncertainty. DiffCarl integrates a diffusion model into a deep reinforcement learning (DRL) framework to enable adaptive energy scheduling under uncertainty…

AMD and Stability AI launch the industry's first Stable Diffusion 3.0 Medium AI model optimized for AMD's XDNA 2 NPUs, designed to run locally on Ryzen laptops (Anton Shilov/Tom's Hardware)

AMD unveils industry-first Stable Diffusion 3.0 Medium AI model generator tailored for XDNA 2 NPUs — designed to run locally on Ryzen AI laptops
An offline image generator for XDNA 2 NPUs.

DefFusionNet: Learning Multimodal Goal Shapes for Deformable Object Manipulation via a Diffusion-based Probabilistic Model
Bao Thach, Siyeon Kim, Britton Jordan, Mohanraj Shanthi, Tanner Watts, Shing-Hei Ho, James M. Ferguson, Tucker Hermans, Alan Kuntz
https://arxiv.org/abs/2506.18779

DefFusionNet: Learning Multimodal Goal Shapes for Deformable Object Manipulation via a Diffusion-based Probabilistic Model
Deformable object manipulation is critical to many real-world robotic applications, ranging from surgical robotics and soft material handling in manufacturing to household tasks like laundry folding. At the core of this important robotic field is shape servoing, a task focused on controlling deformable objects into desired shapes. The shape servoing formulation requires the specification of a goal shape. However, most prior works in shape servoing rely on impractical goal shape acquisition meth…

heise | Vom Text zum Bild: So funktioniert Stable Diffusion
Das frei verfügbare Stable Diffusion generiert aus Texten realitätsnahe und detailreiche Bilder. Wir erklären, wie die KI arbeitet.
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Winning the NFC North: Reasons the Bears, Lions, Packers and Vikings can claim the division crown

https://www.cbssports.com/nfl/news/winning

NFC East 2025: One reason the Cowboys, Eagles, Commanders can win competitive division

https://www.cbssports.com/nfl/news/nfc-east-2025…