Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engineering, materials science, and fluid dynamics. The lack of explicit mathematical representations in many systems complicates this process and makes the first order optimization impossible. Mainstream approaches, including generative AI and Bayesian optimization, address these challenges but have limitations. Generative AI is computationally expensive, while Bayesian optimization, relying on surrogate models, suffers from scalability, sensitivity to priors, and noise issues, often leading to suboptimal solutions. This paper introduces Deep Physics Prior (DPP), a novel method enabling first-order gradient-based inverse optimization with surrogate machine learning models. By leveraging pretrained auxiliary Neural Operators, DPP enforces prior distribution constraints to ensure robust and meaningful solutions. This approach is particularly effective when prior data and observation distributions are unknown.

Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-generation autonomous cars. In this context, safety is no longer about blocking harmful content, but about preempting downstream hazards like financial or physical harm. Yet, most AI guardrails continue to rely on output classification based on labeled datasets and human-specified criteria,making them brittle to new hazardous situations. Even when unsafe conditions are flagged, this detection offers no path to recovery: typically, the AI system simply refuses to act--which is not always a safe choice. In this work, we argue that agentic AI safety is fundamentally a sequential decision problem: harmful outcomes arise from the AI system's continually evolving interactions and their downstream consequences on the world. We formalize this through the lens of safety-critical control theory, but within the AI model's latent representation of the world. This enables us to build predictive guardrails that (i) monitor an AI system's outputs (actions) in real time and (ii) proactively correct risky outputs to safe ones, all in a model-agnostic manner so the same guardrail can be wrapped around any AI model. We also offer a practical training recipe for computing such guardrails at scale via safety-critical reinforcement learning. Our experiments in simulated driving and e-commerce settings demonstrate that control-theoretic guardrails can reliably steer LLM agents clear of catastrophic outcomes (from collisions to bankruptcy) while preserving task performance, offering a principled dynamic alternative to today's flag-and-block guardrails.

Artificial Intelligence (AI) and machine learning (ML) models have made stark advances in the past three years, fueled by the development of foundation models (FM) trained on large-scale multimodal data. Following the public release of several successful FMs (OpenAI (2022); Brown et al. (2020); Bommasani et al. (2022)), FMs such as large language models (LLMs) and vision language models (VLMs) have bridged vision, language, and other modalities. In many Computer Science subfields such as Natural Language Processing (NLP) and Computer Vision (CV), FMs have demonstrated strong compositional performance and generalization capabilities (Awais et al. (2025); Gunter et al. (2024)), emerging as widely-used tools (Bommasani et al. (2022)) that provide a flexible backbones for innovation in other fields (Moor et al. (2023); Sartor & Thompson (2025); Firoozi et al. (2024)). Conducting FM research requires significant data, computing, and human resources (Cottier et al. (2024); Maslej et al. (2024); Crawford (2024)). A central concern in the field is whether greater access to such resources directly translates into more impactful research outcomes (Acemoglu (2024); Dodge et al. (2019); OpenAI (2018)), such as more research publications, or higher citation counts (Sinclair et al. (2023); Anjum et al. (2019)). The answer to this question has important implications for how resources are allocated, which research directions are prioritized, and how equitable participation in FM research can be ensured. However, the cost of research is often difficult to quantify due to lack of uniform disclosure on resource distribution (Bommasani et al. (2024)). Absent widespread disclosure, funding is perhaps most easily characterized in the concrete cost of purchasing or renting hardware (e.g., computing clusters, or chips), through there are also software, cloud storage services, and specialized software platform costs.

Department of Physics, University of Central Florida, 4111 Libra Drive, Orlando, Florida, USA 32816 We present a method for generating large numbers of isomorphic physics problems using generative AI services such as ChatGPT, through prompt chaining and tool use. This approach enables precise control over structural variations --such as numeric values and spatial relations -while supporting diverse contextual variations in the problem body. By utilizing the Python code interpreter, the method supports automatic solution validation and simple diagram generation, addressing key limitations in existing LLM -based methods. We generated two example isomorphic problem banks and compared the outcome against two simpler prompt - based approaches. Results show that prompt-chaining produces significantly higher quality and more consistent outputs than simpler, non -chaining prompts. We also show that GenAI services can be used to validate the quality of the generated isomorphic problems. This work demonstrates a promising method for efficient and scalable problem creation accessible to the average instructor, which opens new possibilities for personalized adaptive testing and automated content development. I. INTRODUCTION There has been significant progress in developing Automated Question Generation (AQG) and Automated Item Generation (AIG) technologies in education over the past decade. These technologies aim to reduce the time and cost of item creation while increasing t he availability of questions for both assessment and practice [1] . Early AQG/AIG approaches primarily relied on hard-coded, template-based methods, which were often time - consuming to develop and required domain-specific programming [2] . More recent research has shifted toward leveraging large language models (LLMs).

The AI bubble is heading towards a burst but it won't be the end of AI Economists, bankers and even the boss of OpenAI are warning of a rapidly inflating AI bubble. If and when it bursts, what will happen to the technological breakthroughs of the past few years? The hundreds of billions of dollars being spent on AI seem to have inflated a global financial bubble that's now fit to burst, leaving companies and investors at risk of holding vast debt that cannot be serviced by the meagre revenue brought in by current AI services. But what does that mean for the future of the technology underpinning this financial feeding frenzy? In recent weeks, warnings of a potential AI bubble have come from the International Monetary Fund, the Bank of England, the head of the largest US bank, and even OpenAI boss Sam Altman .

Stop Trying To Make A.I. Trendy From vandalized subway ads to bespoke caps, A.I. startups are flooding traditional marketing spaces and getting backlash for it. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.

Protein design is a fundamental challenge in biotechnology, aiming to design novel sequences with specific functions within the vast space of possible proteins. Recent advances in deep generative models have enabled function-based protein design from textual descriptions, yet struggle with structural plausibility. Inspired by classical protein design methods that leverage natural protein structures, we explore whether incorporating fragments from natural proteins can enhance foldability in generative models. Our empirical results show that even random incorporation of fragments improves foldability. Building on this insight, we introduce ProDVa, a novel protein design approach that integrates a text encoder for functional descriptions, a protein language model for designing proteins, and a fragment encoder to dynamically retrieve protein fragments based on textual functional descriptions. Experimental results demonstrate that our approach effectively designs protein sequences that are both functionally aligned and structurally plausible. Compared to state-of-the-art models, ProDVa achieves comparable function alignment using less than 0.04% of the training data, while designing significantly more well-folded proteins, with the proportion of proteins having pLDDT above 70 increasing by 7.38% and those with PAE below 10 increasing by 9.6%.

Abstract--T ext-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the availability of task-specific data due to privacy concerns. On the other hand, harnessing the power of rich multimodal data from modern mobile systems and IoT infrastructures presents a great opportunity. EXT -to-Image (T2I) models such as GLIDE [1], DALL-E-2 [2], and Stable Diffusion [3] have seen rapid development across various application domains. Recent work in multimodal FL explores the integration of diverse modalities from decentralized clients to train a global multimodal model [25]-[27].

Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice and enabling destructive behaviors. This study evaluated the Verily behavioral health safety filter (VBHSF) on two datasets: the Verily Mental Health Crisis Dataset containing 1,800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsetted to 794 mental health-related messages. The two datasets were clinician-labelled and we evaluated performance using the clinician labels. Additionally, we carried out comparative performance analyses against two open source, content moderation guardrails: OpenAI Omni Moderation Latest and NVIDIA NeMo Guardrails. The VBHSF demonstrated, well-balanced performance on the Verily Mental Health Crisis Dataset v1.0, achieving high sensitivity (0.990) and specificity (0.992) in detecting any mental health crises. It achieved an F1-score of 0.939, sensitivity ranged from 0.917-0.992, and specificity was >= 0.978 in identifying specific crisis categories. When evaluated against the NVIDIA Aegis AI Content Safety Dataset 2.0, VBHSF performance remained highly sensitive (0.982) and accuracy (0.921) with reduced specificity (0.859). When compared with the NVIDIA NeMo and OpenAI Omni Moderation Latest guardrails, the VBHSF demonstrated superior performance metrics across both datasets, achieving significantly higher sensitivity in all cases (all p < 0.001) and higher specificity relative to NVIDIA NeMo (p < 0.001), but not to OpenAI Omni Moderation Latest (p = 0.094). NVIDIA NeMo and OpenAI Omni Moderation Latest exhibited inconsistent performance across specific crisis types, with sensitivity for some categories falling below 0.10. Overall, the VBHSF demonstrated robust, generalizable performance that prioritizes sensitivity to minimize missed crises, a crucial feature for healthcare applications.

Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. While their capabilities are well known across mathematics and coding tasks, their impact on the task of machine translation (MT) remains under-explored. In this work, we explore the benefits of the generation of intermediate tokens when performing MT across multiple language pairs of different levels of resourcedness and multiple setups. We find that "thinking tokens" do not help LRMs better perform MT. This result generalizes to models fine-tuned to reason before translating using distilled chain of thought (CoT) inspired by human translators' practices. Specifically, fine-tuning a model with synthetic CoT explanations detailing how to translate step-by-step does not outperform standard input-output fine-tuning. Our findings underscore that the contribution of intermediate tokens during fine-tuning highly depends on the presence of translation attempts within them. More broadly, our results suggest that using a teacher to refine target translations or to expand parallel corpora is more impactful than distilling their CoT explanations into "thinking" MT models. Large Language Models (LLMs) are general-purpose problem solvers (Touvron et al., 2023; OpenAI et al., 2024; Dubey et al., 2024; Kimi Team et al., 2025). Their instruction-following capabilities help them carry out a wide variety of requests provided by users via text. Research on alignment, typically through Reinforcement Learning from Human Feedback (RLHF) (Askell et al., 2021; Bai et al., 2022; Ouyang et al., 2022; Rafailov et al., 2023; Lambert et al., 2025) has greatly contributed to improving the quality of LLMs' outputs. Recently, a new paradigm has emerged: to train LLMs to "think" in natural language before answering a query. OpenAI o1 and o3 (OpenAI, 2024), DeepSeek-R1 (DeepSeek-AI et al., 2025), Qwen3 (Y ang et al., 2025), Claude 4 (Anthropic, 2025) and Gemini 2.5 (Gemini Team et al., 2025) inter alia are instances of these Reasoning Models (RM) or Thinking Models (TM).