The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye. While offering new creative and commercial possibilities, the potential for misuse, such as in misinformation and fraud, highlights the need for effective detection methods. Current detection approaches often rely on access to model weights or require extensive collections of real image datasets, limiting their scalability and practical application in real-world scenarios. In this work, we introduce a novel black-box detection framework that requires only API access, sidestepping the need for model weights or large auxiliary datasets. Our approach leverages a corrupt-and-recover strategy: by masking part of an image and assessing the model's ability to reconstruct it, we measure the likelihood that the image was generated by the model itself. F or black-box models that do not support masked-image inputs, we incorporate a cost-efficient surrogate model trained to align with the target model's distribution, enhancing detection capability. Our framework demonstrates strong performance, outperforming baseline methods by 4.31% in mean average precision across eight diffusion model variant datasets. Code is publicly available at https://github.com/

Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models are constrained by rigid assumptions, limited contextual reasoning, and reduced generalizability. This study explores the potential of Large Language Models (LLMs) as a more flexible and context-aware approach to travel mode choice prediction, enhanced by Retrieval-Augmented Generation (RAG) to ground predictions in empirical data. We develop a modular framework for integrating RAG into LLM-based travel mode choice prediction and evaluate four retrieval strategies: basic RAG, RAG with balanced retrieval, RAG with a cross-encoder for re-ranking, and RAG with balanced retrieval and cross-encoder for re-ranking. These strategies are tested across three LLM architectures (OpenAI GPT-4o, o4-mini, and o3) to examine the interaction between model reasoning capabilities and retrieval methods. Using the 2023 Puget Sound Regional Household Travel Survey data, we conduct a series of experiments to evaluate model performance. The results demonstrate that RAG substantially enhances predictive accuracy across a range of models. Notably, the GPT-4o model combined with balanced retrieval and cross-encoder re-ranking achieves the highest accuracy of 80.8%, exceeding that of conventional statistical and machine learning baselines. Furthermore, LLM-based models exhibit superior generalization abilities relative to these baselines. Findings highlight the critical interplay between LLM reasoning capabilities and retrieval strategies, demonstrating the importance of aligning retrieval strategies with model capabilities to maximize the potential of LLM-based travel behavior modeling.

AI Alignment, primarily in the form of Reinforcement Learning from Human Feedback (RLHF), has been a cornerstone of the post-training phase in developing Large Language Models (LLMs). It has also been a popular research topic across various disciplines beyond Computer Science, including Philosophy and Law, among others, highlighting the socio-technical challenges involved. Nonetheless, except for the computational techniques related to alignment, there has been limited focus on the broader picture: the scope of these processes, which primarily rely on the selected objectives (values), and the data collected and used to imprint such objectives into the models. This work aims to reveal how alignment is understood and applied in practice from a value-setting and data-centric perspective. For this purpose, we investigate and survey (`audit') publicly available documentation released by 6 LLM development initiatives by 5 leading organizations shaping this technology, focusing on proprietary (OpenAI's GPT, Anthropic's Claude, Google's Gemini) and open-weight (Meta's Llama, Google's Gemma, and Alibaba's Qwen) initiatives, all published in the last 3 years. The findings are documented in detail per initiative, while there is also an overall summary concerning different aspects, mainly from a value-setting and data-centric perspective. On the basis of our findings, we discuss a series of broader related concerns.

This study investigates the impact of Generative AI on software development within the IT sector through a mixed-method approach, utilizing a survey developed based on expert interviews. The preliminary results of an ongoing survey offer early insights into how Generative AI reshapes personal productivity, organizational efficiency, adoption, business strategy and job insecurity. The findings reveal that 97% of IT workers use Generative AI tools, mainly ChatGPT. Participants report significant personal productivity gain and perceive organizational efficiency improvements that correlate positively with Generative AI adoption by their organizations (r = .470, p < .05). However, increased organizational adoption of AI strongly correlates with heightened employee job security concerns (r = .549, p < .001). Key adoption challenges include inaccurate outputs (64.2%), regulatory compliance issues (58.2%) and ethical concerns (52.2%). This research offers early empirical insights into Generative AI's economic and organizational implications.

--This paper introduces a system for generating interior design layouts based on user inputs, such as room type, style, and furniture preferences. CLIP extracts relevant furniture from a dataset, and a layout that contains furniture and a prompt are fed to the Stable Diffusion with ControlNet to generate a design that incorporates the selected furniture. The design is then evaluated by classifiers to ensure alignment with the user's inputs, offering an automated solution for realistic interior design. I. Introduction Interior design has become increasingly popular as people seek more comfort and personalization in their living spaces. While hiring professional designers is common for full-home projects, redesigning a single room--such as a bedroom--may not justify the cost or effort involved in hiring such services.Additionally, many individuals who prefer to furnish their rooms using items from specific stores like IKEA often feel uncertain about whether suggested furniture--based on their selected categories (e.g., sofa, table)--will suit the room's size, layout, and style.

From large language models to multi-modal agents, Generative Artificial Intelligence (AI) now underpins state-of-the-art systems. Despite their varied architectures, many share a common foundation in probabilistic latent variable models (PLVMs), where hidden variables explain observed data for density estimation, latent reasoning, and structured inference. This paper presents a unified perspective by framing both classical and modern generative methods within the PLVM paradigm. We trace the progression from classical flat models such as probabilistic PCA, Gaussian mixture models, latent class analysis, item response theory, and latent Dirichlet allocation, through their sequential extensions including Hidden Markov Models, Gaussian HMMs, and Linear Dynamical Systems, to contemporary deep architectures: Variational Autoencoders as Deep PLVMs, Normalizing Flows as Tractable PLVMs, Diffusion Models as Sequential PLVMs, Autoregressive Models as Explicit Generative Models, and Generative Adversarial Networks as Implicit PLVMs. Viewing these architectures under a common probabilistic taxonomy reveals shared principles, distinct inference strategies, and the representational trade-offs that shape their strengths. We offer a conceptual roadmap that consolidates generative AI's theoretical foundations, clarifies methodological lineages, and guides future innovation by grounding emerging architectures in their probabilistic heritage.

The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models are fundamentally constrained by \emph{catastrophic forgetting}, \ie~a persistent challenge where models experience performance degradation on previously learned tasks when adapting to new tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative AI in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative AI models, encompassing large language models, multimodal large language models, vision-language-action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, thereby providing deeper insights into the field. The project page of this paper is available at https://github.com/Ghy0501/Awesome-Continual-Learning-in-Generative-Models.

Elon Musk's artificial intelligence startup xAI has sued Apple and ChatGPT maker OpenAI, accusing them of illegally conspiring to thwart competition for artificial intelligence (AI). The lawsuit filed in a United States federal court in Texas on Monday says that Apple and OpenAI have "locked up markets to maintain their monopolies and prevent innovators like X and xAI from competing". The complaint filed by the billionaire said Apple and OpenAI conspired to suppress xAI's products, including on the Apple App Store. "If not for its exclusive deal with OpenAI, Apple would have no reason to refrain from more prominently featuring the X app and the Grok app in its App Store," xAI said. The lawsuit pointed out that in June 2024, Apple and OpenAI announced they would integrate ChatGPT into Apple's operating system under an exclusive arrangement.

Elon Musk's artificial intelligence startup xAI is suing OpenAI and Apple over allegations that they are engaging in anticompetitive conduct. The lawsuit, filed in a Texas court on Monday, accuses the companies of "a conspiracy to monopolize the markets for smartphones and generative AI chatbots". Musk had earlier this month threatened to sue Apple and OpenAI, which makes ChatGPT, after claiming that Apple was "making it impossible" for any other AI companies to reach the top spot on its app store. Musk's xAI makes the Grok chatbot, which has struggled to become as prominent as ChatGPT. Musk's lawsuit challenges a key partnership between Apple and OpenAI that was announced last year, in which the device maker integrated OpenAI's artificial intelligence capabilities into its operating systems.

Elon Musk's xAI filed a lawsuit against Apple and OpenAI on Monday, accusing the companies of behaving like monopolies and claiming Apple deprioritized ChatGPT rivals like Grok in the App Store. "This is a tale of two monopolists joining forces to ensure their continued dominance in a world rapidly driven by the most powerful technology humanity has ever created: artificial intelligence," the lawsuit alleges. "Working in tandem, Defendants Apple and OpenAI have locked up markets to maintain their monopolies and prevent innovators like X and xAI from competing." Grok is currently ranked third in the App Store for free productivity apps--behind only ChatGPT and Gmail. The'uncensored' chatbot is also integrated into Musk's social platform X, which is the number one free news app in the App Store.