Generative Artificial Intelligence (GAI) has rapidly emerged as a transformative force in aquaculture, enabling intelligent synthesis of multimodal data, including text, images, audio, and simulation outputs for smarter, more adaptive decision-making. As the aquaculture industry shifts toward data-driven, automation and digital integration operations under the Aquaculture 4.0 paradigm, GAI models offer novel opportunities across environmental monitoring, robotics, disease diagnostics, infrastructure planning, reporting, and market analysis. This review presents the first comprehensive synthesis of GAI applications in aquaculture, encompassing foundational architectures (e.g., diffusion models, transformers, and retrieval augmented generation), experimental systems, pilot deployments, and real-world use cases. We highlight GAI's growing role in enabling underwater perception, digital twin modeling, and autonomous planning for remotely operated vehicle (ROV) missions. We also provide an updated application taxonomy that spans sensing, control, optimization, communication, and regulatory compliance. Beyond technical capabilities, we analyze key limitations, including limited data availability, real-time performance constraints, trust and explainability, environmental costs, and regulatory uncertainty. This review positions GAI not merely as a tool but as a critical enabler of smart, resilient, and environmentally aligned aquaculture systems.

Ensuring synthesizability in generative small molecule design remains a major challenge. While recent developments in synthesizable molecule generation have demonstrated promising results, these efforts have been largely confined to 2D molecular graph representations, limiting the ability to perform geometry-based conditional generation. In this work, we present SynCoGen (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SynCoGen samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SynSpace, a dataset containing over 600K synthesis-aware building block graphs and 3.3M conformers. SynCoGen achieves state-of-the-art performance in unconditional small molecule graph and conformer generation, and the model delivers competitive performance in zero-shot molecular linker design for protein ligand generation in drug discovery. Overall, this multimodal formulation represents a foundation for future applications enabled by non-autoregressive molecular generation, including analog expansion, lead optimization, and direct structure conditioning.

Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and tasks in scientific research and discovery, while they have limitations in understanding the transformative potential and role differentiation of LLM. This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist. We distinguish between LLMs' contributions to structured scientific research processes and open-ended scientific discovery, thereby offering a unified taxonomy that clarifies capability boundaries, evaluation criteria, and human-AI interaction patterns at each level. Through an extensive analysis of current methodologies, benchmarks, systems, and evaluation metrics, this survey delivers an in-depth and systematic synthesis on LLM-driven scientific innovation. We present LLMs not only as tools for automating existing processes, but also as catalysts capable of reshaping the epistemological foundations of science itself. This survey offers conceptual clarity, practical guidance, and theoretical foundations for future research, while also highlighting open challenges and ethical considerations in the pursuit of increasingly autonomous AI-driven science. Resources related to this survey can be accessed on GitHub at: https://github.com/haoxuan-unt2024/llm4innovation.

Abstract visual reasoning (AVR) enables humans to quickly discover and generalize abstract rules to new scenarios. Designing intelligent systems with human-like AVR abilities has been a long-standing topic in the artificial intelligence community. Deep AVR solvers have recently achieved remarkable success in various AVR tasks. However, they usually use task-specific designs or parameters in different tasks. In such a paradigm, solving new tasks often means retraining the model, and sometimes retuning the model architectures, which increases the cost of solving AVR problems. In contrast to task-specific approaches, this paper proposes a novel Unified Conditional Generative Solver (UCGS), aiming to address multiple AVR tasks in a unified framework. First, we prove that some well-known AVR tasks can be reformulated as the problem of estimating the predictability of target images in problem panels. Then, we illustrate that, under the proposed framework, training one conditional generative model can solve various AVR tasks. The experiments show that with a single round of multi-task training, UCGS demonstrates abstract reasoning ability across various AVR tasks. Especially, UCGS exhibits the ability of zero-shot reasoning, enabling it to perform abstract reasoning on problems from unseen AVR tasks in the testing phase.

This study introduces Clarity and Reasoning Interface for Artificial Intelligence (ClarifAI), a novel approach designed to augment the transparency and interpretability of artificial intelligence (AI) in the realm of improved decision making. Leveraging the Case-Based Reasoning (CBR) methodology and integrating an ontology-driven approach, ClarifAI aims to meet the intricate explanatory demands of various stakeholders involved in AI-powered applications. The paper elaborates on ClarifAI's theoretical foundations, combining CBR and ontologies to furnish exhaustive explanation mechanisms. It further elaborates on the design principles and architectural blueprint, highlighting ClarifAI's potential to enhance AI interpretability across different sectors and its applicability in high-stake environments.

Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about the capabilities and limitations of NLP. These recommendations cover three themes: vague terminology as an obstacle to public understanding, unreasonable expectations as obstacles to sustainable growth, and ethical failures as obstacles to continued support. Published NLP research and popular news coverage are cited to illustrate these themes with examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.

At least fifteen people have been injured in the latest wave of Russian drone strikes, which this time hit four Ukranian cities. Video shows firefighters battling flames from a large blaze. This comes amid the 50-day deadline from the US for Russia to secure a peace deal.

For the past few weeks, the AI-powered ElliQ companion robot has perched on the end of my desk. Designed by Intuition Robotics for seniors living alone, this proactive animatronic chats to me throughout the day, checking how I'm feeling, suggesting "fun" activities, and prodding me to be more active and sociable. While it can be annoying, I've grown attached to ElliQ despite myself, and I can see the positive potential. According to the US Census Bureau, around 16 million elders (over 65) live alone in the country, and up to a third report feelings of loneliness. Multiple studies have shown that social isolation harms mental and physical health, increasing blood pressure, depression, weight gain, alcohol and drug use, and decreasing physical activity, cognition, heart health, and sleep.

Nvidia CEO Jensen Huang has called China's open-source artificial intelligence a "catalyst for global progress" and says it is "revolutionising" supply chains. In a speech during Wednesday's opening ceremony of the China International Supply Chain Expo in Beijing, Huang – whose firm last week became the first to touch 4 trillion in market value – hailed China's role in pioneering AI, describing Chinese AI startup DeepSeek as "giving every country and industry a chance to join the AI revolution". Huang made the comments a day after Nvidia announced it will resume sales of its H20 AI chips to China after the United States government pledged to remove licensing restrictions that had halted exports. "AI is transforming every industry from scientific research and healthcare to energy, transportation and logistics," said Huang, who also praised China's "super-fast" innovation, powered by its "researchers, developers and entrepreneurs". The California-based company produces some of the world's most advanced semiconductors but cannot ship its most cutting-edge chips to China due to Washington's concerns that Beijing could use them to enhance its military capabilities.

Before boarding Marine One on Tuesday afternoon, President Trump challenged two progressive Democrat congresswomen to an intelligence test. President Donald Trump lobbed a signature zinger on Tuesday as he paused to speak with reporters before boarding Marine One en route to an artificial intelligence summit. "[Alexandria Ocasio-Cortez], look, I think she's very nice, but she's very low IQ, and we really don't need low IQ," Trump said, smiling as cameras rolled. He added, "Between her and Crockett, we're going to give them both an IQ test to see who comes out best." TRUMP DARES AOC TO TRY TO IMPEACH HIM: 'MAKE MY DAY' President Donald Trump said AOC and Jasmine Crockett should take IQ tests.