Following the release of GPT-5.5 last week, people noticed something funny about OpenAI's latest model. In its Codex coding app, the company left a system prompt instructing GPT 5.5 to avoid mention of goblins, gremlins and other creatures. Yes, you read that right. Never talk about goblins, gremlins, racoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query, the prompt reads. Apparently, enough people started talking about ChatGPT's creature obsession that OpenAI felt the need to provide an accounting of where the goblins came from .

PCWorld reports that Google is exploring adding advertisements to its Gemini AI app, following OpenAI's implementation of sponsored ads in ChatGPT's free and budget plans. Google's business chief Philipp Schindler views ads as potentially valuable commercial information if properly integrated, while the company has already tested ads in AI Mode and AI Overviews. This move could make AI services more accessible but raises important concerns about maintaining transparency and ensuring ads don't influence AI responses. Putting ads in AI replies is a controversial but lucrative practice, and it's one that OpenAI has already embraced with its free and budget-priced ChatGPT plans. But while Google hasn't gone there yet with Gemini, company execs admitted they're mulling the idea.

PCWorld reports that OpenAI's GPT models, including GPT-5.5, developed an unusual obsession with mentioning goblins and similar creatures in responses. This quirky behavior stemmed from a "Nerdy" personality instruction encouraging playful language use, which became reinforced through AI training processes. The goblin references became so prevalent that OpenAI implemented a direct ban in its Codex app, illustrating the unpredictable nature of large language model training. I've seen some odd AI system instructions in my day, but this one takes the cake: a prompt in OpenAI's Codex command-line app that demands models "never talk about goblins, gremlins, trolls, ogres, pigeons, or other animals or creatures."

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning. We reformulate the problem definitions of the fusion and rectangling stages and demonstrate that they can be effectively integrated into an inpainting task. Furthermore, we design the weighted masks to guide the reverse process in a pre-trained largescale diffusion model, implementing this integrated inpainting task in a single inference. Through extensive experimentation, we verify the interpretability and generalization capabilities of this unified model, demonstrating that SRStitcher outperforms state-of-the-art methods in both performance and stability.

Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network consisting of a sequence of first-order approximations telescoping out into a single empirically operational tool for practical analysis. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena in the literature -- including double descent, grokking, linear mode connectivity, and the challenges of applying deep learning on tabular data -- highlighting that this model allows us to construct and extract metrics that help predict and understand the a priori unexpected performance of neural networks. We also demonstrate that this model presents a pedagogical formalism allowing us to isolate components of the training process even in complex contemporary settings, providing a lens to reason about the effects of design choices such as architecture & optimization strategy, and reveals surprising parallels between neural network learning and gradient boosting.

Plus: Google, Microsoft, Amazon and Meta have all set AI spending records. In the past, getting to the North Pole involved a treacherous trip through ice many meters thick. But last year, a research vessel encountered open water and thin ice, which created an easy passage. It provided a reminder of how quickly the Arctic is changing. Now scientists are digging deep below the seabed to find out if the Arctic Ocean was ever ice-free--and what that could mean for the future of Earth's northernmost waters. Here's what they hope to discover .

PCWorld explains how vague prompts produce poor results from AI tools like ChatGPT and Gemini, emphasizing the need for specific, detailed requests. The article introduces prompt decomposition, a technique that breaks complex tasks into key variables to create more effective AI prompts. This method helps users guide AI tools more precisely, resulting in higher-quality, less biased outputs for complex tasks. It's never a good idea to hand ChatGPT, Claude, or Gemini big, vague tasks like "draw up a business plan for my new venture" or "act as my personal assistant." Fuzzy prompts like those are sure to yield equally fuzzy results, allowing the AI to make decisions based on its training data and inherent biases, potentially leading you down a path you never intended.

Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achieve translation between two images without training, and robustness to noisy data, can be used to improve security and natural robustness in image steganography tasks. For the choice of diffusion model, we selected Stable Diffusion, a type of conditional diffusion model, and fully utilized the latest tools from open-source communities, such as LoRAs and ControlNets, to improve the controllability and diversity of container images. In summary, we propose a novel image steganography framework, named Controllable, Robust and Secure Image Steganography (CRoSS), which has significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. These benefits are obtained without additional training. To our knowledge, this is the first work to introduce diffusion models to the field of image steganography. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed CRoSS framework in controllability, robustness, and security.