Abstract
In the context of municipal heat planning, it is imperative to consider the numerous buildings, numbering in the hundreds or thousands, that are involved. This poses particular challenges for model-based energy system optimization, as the number of variables increases with the number of buildings under consideration. In the worst case, the computational complexity of the models experiences an exponential increase with the number of variables. Furthermore, within the context of heat transition, it is often necessary to map extended periods of time (i.e., the service life of systems) with high resolution (particularly in the case of load peaks that occur at the onset of the day). In response to these challenges, the aggregation of input data is a common practice. In general, building blocks or other geographical and urban formations, such as neighbourhoods, are combined. This article explores the potential of incorporating energy performance indicators into the grouping of buildings. The case study utilizes authentic data from the Neu-Schwachhausen district, grouped based on geographical location, building geometry, and energy performance indicators. The selection of energy indicators includes the annual heat consumption as well as the potential for solar energy generation. To this end, a methodology is hereby presented that considers not only the anticipated annual energy quantity, but also its progression over time. We present a full workflow from geodata to a set of techno-socio-economically Pareto-optimal heat supply options. Our findings suggest that it is beneficial to find a balance between geographical position and energy properties when grouping buildings for the use in energy system models.

Abstract
Traditional sea exploration faces significant challenges due to extreme conditions, limited visibility, and high costs, resulting in vast unexplored ocean regions. This paper presents an innovative AI-powered Autonomous Underwater Vehicle (AUV) system designed to overcome these limitations by automating underwater object detection, analysis, and reporting. The system integrates YOLOv12 Nano for real-time object detection, a Convolutional Neural Network (CNN) (ResNet50) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and K-Means++ clustering for grouping marine objects based on visual characteristics. Furthermore, a Large Language Model (LLM) (GPT-4o Mini) is employed to generate structured reports and summaries of underwater findings, enhancing data interpretation. The system was trained and evaluated on a combined dataset of over 55,000 images from the DeepFish and OzFish datasets, capturing diverse Australian marine environments. Experimental results demonstrate the system's capability to detect marine objects with a mAP@0.5 of 0.512, a precision of 0.535, and a recall of 0.438. The integration of PCA effectively reduced feature dimensionality while preserving 98% variance, facilitating K-Means clustering which successfully grouped detected objects based on visual similarities. The LLM integration proved effective in generating insightful summaries of detections and clusters, supported by location data. This integrated approach significantly reduces the risks associated with human diving, increases mission efficiency, and enhances the speed and depth of underwater data analysis, paving the way for more effective scientific research and discovery in challenging marine environments.

AI Summary

• The system maintained consistent inference speeds between 2.0ms and 5.5ms and processed external video data to validate its performance. [3]
• CNN: Convolutional Neural Network, a type of neural network designed to process data with grid-like topology, commonly used in image and signal processing. [3]
• PCA: Principal Component Analysis, a dimensionality reduction technique that transforms high-dimensional data into lower-dimensional space while retaining most of the information. [3]
• The system uses YOLOv12 Nano to detect and distinguish over 50% of annotated objects under challenging marine conditions. [2]

Abstract
This paper examines how international AI governance frameworks address gender issues and gender-based harms. The analysis covers binding regulations, such as the EU AI Act; soft law instruments, like the UNESCO Recommendations on AI Ethics; and global initiatives, such as the Global Partnership on AI (GPAI). These instruments reveal emerging trends, including the integration of gender concerns into broader human rights frameworks, a shift toward explicit gender-related provisions, and a growing emphasis on inclusivity and diversity. Yet, some critical gaps persist, including inconsistent treatment of gender across governance documents, limited engagement with intersectionality, and a lack of robust enforcement mechanisms. However, this paper argues that effective AI governance must be intersectional, enforceable, and inclusive. This is key to moving beyond tokenism toward meaningful equity and preventing reinforcement of existing inequalities. The study contributes to ethical AI debates by highlighting the importance of gender-sensitive governance in building a just technological future.

AI Summary

• Global AI governance is increasingly emphasizing the importance of gender equality and addressing gender-related AI harm. [2]

Abstract
This manuscript establishes information-theoretic limitations for robustness of AI security and alignment by extending Gödel's incompleteness theorem to AI. Knowing these limitations and preparing for the challenges they bring is critically important for the responsible adoption of the AI technology. Practical approaches to dealing with these challenges are provided as well. Broader implications for cognitive reasoning limitations of AI systems are also proven.

AI Summary

• The authors argue that Godel's theorem has significant implications for AI and ML, particularly in the context of large language models. [3]
• Godel's theorem: A mathematical proof that shows that any formal system powerful enough to describe basic arithmetic is either incomplete or inconsistent. [3]
• Large language models: Deep neural networks designed to process and generate human-like language. [3]
• The authors assume a high level of mathematical background, which may limit the accessibility of the paper to non-experts. [3]
• The paper discusses the limitations of formal systems and their implications for artificial intelligence (AI) and machine learning (ML). [2]

Abstract
Today, with the growing obsession with applying Artificial Intelligence (AI), particularly Machine Learning (ML), to software across various contexts, much of the focus has been on the effectiveness of AI models, often measured through common metrics such as F1- score, while fairness receives relatively little attention. This paper presents a review of existing gray literature, examining fairness requirements in AI context, with a focus on how they are defined across various application domains, managed throughout the Software Development Life Cycle (SDLC), and the causes, as well as the corresponding consequences of their violation by AI models. Our gray literature investigation shows various definitions of fairness requirements in AI systems, commonly emphasizing non-discrimination and equal treatment across different demographic and social attributes. Fairness requirement management practices vary across the SDLC, particularly in model training and bias mitigation, fairness monitoring and evaluation, and data handling practices. Fairness requirement violations are frequently linked, but not limited, to data representation bias, algorithmic and model design bias, human judgment, and evaluation and transparency gaps. The corresponding consequences include harm in a broad sense, encompassing specific professional and societal impacts as key examples, stereotype reinforcement, data and privacy risks, and loss of trust and legitimacy in AI-supported decisions. These findings emphasize the need for consistent frameworks and practices to integrate fairness into AI software, paying as much attention to fairness as to effectiveness.

AI Summary

• The study found that fairness requirements in AI are often context-dependent and tailored to specific applications rather than a universal metric. [2]

Abstract
While much research in artificial intelligence (AI) has focused on scaling capabilities, the accelerating pace of development makes countervailing work on producing harmless, "aligned" systems increasingly urgent. Yet research on alignment has diverged along two largely parallel tracks: safety--centered on scaled intelligence, deceptive or scheming behaviors, and existential risk--and ethics--focused on present harms, the reproduction of social bias, and flaws in production pipelines. Although both communities warn of insufficient investment in alignment, they disagree on what alignment means or ought to mean. As a result, their efforts have evolved in relative isolation, shaped by distinct methodologies, institutional homes, and disciplinary genealogies. We present a large-scale, quantitative study showing the structural split between AI safety and AI ethics. Using a bibliometric and co-authorship network analysis of 6,442 papers from twelve major ML and NLP conferences (2020-2025), we find that over 80% of collaborations occur within either the safety or ethics communities, and cross-field connectivity is highly concentrated: roughly 5% of papers account for more than 85% of bridging links. Removing a small number of these brokers sharply increases segregation, indicating that cross-disciplinary exchange depends on a handful of actors rather than broad, distributed collaboration. These results show that the safety-ethics divide is not only conceptual but institutional, with implications for research agendas, policy, and venues. We argue that integrating technical safety work with normative ethics--via shared benchmarks, cross-institutional venues, and mixed-method methodologies--is essential for building AI systems that are both robust and just.

AI Summary

• The dataset consists of 49,725 papers from various conferences related to artificial intelligence (AI) safety and ethics. [3]
• Abstract enrichment coverage: The percentage of papers with abstracts. [3]
• Keywords were generated by analyzing foundational surveys and texts in each field, with a hierarchical strategy spanning technical, theoretical, and applied domains. [2]
• The abstract enrichment coverage is 97.7%, indicating that most papers have abstracts. [1]

Abstract
We present Ethics Readiness Levels (ERLs), a four-level, iterative method to track how ethical reflection is implemented in the design of AI systems. ERLs bridge high-level ethical principles and everyday engineering by turning ethical values into concrete prompts, checks, and controls within real use cases. The evaluation is conducted using a dynamic, tree-like questionnaire built from context-specific indicators, ensuring relevance to the technology and application domain. Beyond being a managerial tool, ERLs help facilitate a structured dialogue between ethics experts and technical teams, while our scoring system helps track progress over time. We demonstrate the methodology through two case studies: an AI facial sketch generator for law enforcement and a collaborative industrial robot. The ERL tool effectively catalyzes concrete design changes and promotes a shift from narrow technological solutionism to a more reflective, ethics-by-design mindset.

AI Summary

• Technological solutionism: The tendency to view complex problems as solvable through technological fixes rather than addressing underlying issues. [3]
• Ethics-by-design is a powerful approach that can significantly improve the ethics readiness of AI systems. [2]

Abstract
Current embodied AI systems face severe engineering impediments, primarily characterized by poor cross-scenario adaptability, rigid inter-module coupling, and fragmented inference acceleration. To overcome these limitations, we propose RoboNeuron, a universal deployment framework for embodied intelligence. RoboNeuron is the first framework to deeply integrate the cognitive capabilities of Large Language Models (LLMs) and Vision-Language-Action (VLA) models with the real-time execution backbone of the Robot Operating System (ROS). We utilize the Model Context Protocol (MCP) as a semantic bridge, enabling the LLM to dynamically orchestrate underlying robotic tools. The framework establishes a highly modular architecture that strictly decouples sensing, reasoning, and control by leveraging ROS's unified communication interfaces. Crucially, we introduce an automated tool to translate ROS messages into callable MCP functions, significantly streamlining development. RoboNeuron significantly enhances cross-scenario adaptability and component flexibility, while establishing a systematic platform for horizontal performance benchmarking, laying a robust foundation for scalable real-world embodied applications.

AI Summary

• RoboNeuron provides a scalable foundation for embodied intelligence research, and future work will expand model coverage and integrate inference acceleration to further strengthen its universality. [3]
• RoboNeuron is a universal deployment framework that addresses the core engineering barriers of embodied AI by unifying LLM/VLA reasoning with ROS's real-time execution layer through the Model Context Protocol. [2]
• The framework enables seamless substitution of hardware, sensors, and VLA models with minimal reconfiguration. [1]

Abstract
This chapter explores human creativity in AI-assisted learning environments through the lens of student agency. We begin by examining four theoretical perspectives on agency, including instrumental, effortful, dynamically emergent, and authorial agency, and analyze how each frames the relationship between agency and creativity. Under each theoretical perspective, we discuss how the integration of generative AI (GenAI) tools reshapes these dynamics by altering students' roles in cognitive, social, and creative processes. In the second part, we introduce a theoretical framework for AI agentic engagement, contextualizing agency within specific cognitive, relational, and ethical dynamics introduced by GenAI tools. This framework is linked to the concept of Mini-c creativity, emphasizing personal relevance and self-directed learning. Together, these perspectives support a shift from viewing creativity as product-oriented to understanding it as a process of agentive participation and meaning-making. We conclude with two directions for future research focused on the creative process and performance in AI-assisted learning.

AI Summary

• Agentic engagement refers to the proactive, intentional, and reflective actions taken by students when interacting with AI tools. [3]
• Mini-c Creativity: A type of creative expression that is personally meaningful and novel, but not necessarily recognized externally. [3]
• Student agency is essential for effective use of AI tools in education. [2]
• The integration of AI in learning environments is changing how we understand student creativity. [1]

Abstract
We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover's quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.

AI Summary

• Previous research has shown that human-AI collaboration can improve performance in various tasks, including theorem discovery and proof verification. [3]
• The collaboration between human experts and an LLM is organized into three stages, starting from an informal conjecture and ending with a precise theorem and proof. [2]
• Human-AI collaboration can significantly improve mathematical proof and theorem discovery. [1]

Abstract
We present a proof-of-principle study demonstrating the use of large language model (LLM) agents to automate a representative high energy physics (HEP) analysis. Using the Higgs boson diphoton cross-section measurement as a case study with ATLAS Open Data, we design a hybrid system that combines an LLM-based supervisor-coder agent with the Snakemake workflow manager. In this architecture, the workflow manager enforces reproducibility and determinism, while the agent autonomously generates, executes, and iteratively corrects analysis code in response to user instructions. We define quantitative evaluation metrics including success rate, error distribution, costs per specific task, and average number of API calls, to assess agent performance across multi-stage workflows. To characterize variability across architectures, we benchmark a representative selection of state-of-the-art LLMs spanning the Gemini and GPT-5 series, the Claude family, and leading open-weight models. While the workflow manager ensures deterministic execution of all analysis steps, the final outputs still show stochastic variation. Although we set the temperature to zero, other sampling parameters (e.g., top-p, top-k) remained at their defaults, and some reasoning-oriented models internally adjust these settings. Consequently, the models do not produce fully deterministic results. This study establishes the first LLM-agent-driven automated data-analysis framework in HEP, enabling systematic benchmarking of model capabilities, stability, and limitations in real-world scientific computing environments. The baseline code used in this work is available at https://huggingface.co/HWresearch/LLM4HEP. This work was accepted as a poster at the Machine Learning and the Physical Sciences (ML4PS) workshop at NeurIPS 2025. The initial submission was made on August 30, 2025.

Abstract
Narratives about artificial intelligence (AI) entangle autonomy, the capacity to self-govern, with sentience, the capacity to sense and feel. AI agents that perform tasks autonomously and companions that recognize and express emotions may activate mental models of autonomy and sentience, respectively, provoking distinct reactions. To examine this possibility, we conducted three pilot studies (N = 374) and four preregistered vignette experiments describing an AI as autonomous, sentient, both, or neither (N = 2,702). Activating a mental model of sentience increased general mind perception (cognition and emotion) and moral consideration more than autonomy, but autonomy increased perceived threat more than sentience. Sentience also increased perceived autonomy more than vice versa. Based on a within-paper meta-analysis, sentience changed reactions more than autonomy on average. By disentangling different mental models of AI, we can study human-AI interaction with more precision to better navigate the detailed design of anthropomorphized AI and prompting interfaces.

AI Summary

• In all three experiments, participants were shown a description of an AI system called Corion, which was said to be working on various tasks. [3]
• The results show that when people are told that the AI is autonomous but not sentient, they tend to perceive it as having a mind and being capable of making decisions. [3]
• However, when people are told that the AI is both autonomous and sentient, they tend to have even more positive perceptions of its capabilities and decision-making abilities. [3]
• The findings have implications for the development of AI systems, as well as for the design of interfaces and interactions between humans and AI. [3]
• The study investigates how people's mental models of artificial intelligence (AI) are influenced by the concepts of autonomy and sentience. [2]
• The researchers conducted three experiments, each with a different manipulation: Experiment 1 manipulated autonomy, Experiment 2 manipulated sentience, and Experiment 3 fully crossed both autonomy and sentience. [1]

Abstract
Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain both eggs and noodles as separate entities. Multi-noun food categories are common in real-world datasets and account for a large portion of entries in benchmarks such as UEC-256. These compound names often cause generative models to misinterpret the semantics, producing unintended ingredients or objects. This is due to insufficient multi-noun category related knowledge in the text encoder and misinterpretation of multi-noun relationships, leading to incorrect spatial layouts. To overcome these challenges, we propose FoCULR (Food Category Understanding and Layout Refinement) which incorporates food domain knowledge and introduces core concepts early in the generation process. Experimental results demonstrate that the integration of these techniques improves image generation performance in the food domain.

AI Summary

• The model is trained on the LAION-5B dataset and achieves state-of-the-art results in various evaluation metrics. [3]
• The model is trained on a large dataset and achieves state-of-the-art results in various evaluation metrics. [3]
• Hierarchical text-conditional image generation: A method of generating images by first predicting the attributes of the image and then generating the image itself based on those attributes. [3]
• The model is trained on the LAION-5B dataset and achieves state-of-the-art results in various evaluation metrics. [3]
• The model is trained on a large dataset and achieves state-of-the-art results in various evaluation metrics. [3]
• The paper presents a novel approach to text-to-image synthesis using a hierarchical text-conditional image generation model with CLIP latents. [2]
• Diffusion-based image synthesis: A method of generating images by iteratively refining an initial noise signal until it converges to a realistic image. [1]

Abstract
Post-deployment monitoring of artificial intelligence (AI) systems in health care is essential to ensure their safety, quality, and sustained benefit-and to support governance decisions about which systems to update, modify, or decommission. Motivated by these needs, we developed a framework for monitoring deployed AI systems grounded in the mandate to take specific actions when they fail to behave as intended. This framework, which is now actively used at Stanford Health Care, is organized around three complementary principles: system integrity, performance, and impact. System integrity monitoring focuses on maximizing system uptime, detecting runtime errors, and identifying when changes to the surrounding IT ecosystem have unintended effects. Performance monitoring focuses on maintaining accurate system behavior in the face of changing health care practices (and thus input data) over time. Impact monitoring assesses whether a deployed system continues to have value in the form of benefit to clinicians and patients. Drawing on examples of deployed AI systems at our academic medical center, we provide practical guidance for creating monitoring plans based on these principles that specify which metrics to measure, when those metrics should be reviewed, who is responsible for acting when metrics change, and what concrete follow-up actions should be taken-for both traditional and generative AI. We also discuss challenges to implementing this framework, including the effort and cost of monitoring for health systems with limited resources and the difficulty of incorporating data-driven monitoring practices into complex organizations where conflicting priorities and definitions of success often coexist. This framework offers a practical template and starting point for health systems seeking to ensure that AI deployments remain safe and effective over time.

AI Summary

• Traditional and generative AI systems require unique monitoring considerations for deployment in clinical systems. [3]
• Performance monitoring: Evaluates the longitudinal accuracy and quality of AI system outputs to detect drift. [3]
• Impact monitoring: Verifies if the AI system produces sustained benefits to patients, health system staff, or health system finances over time. [3]
• The framework is applicable to both traditional and generative AI systems and can be tailored to specific use cases and deployments. [3]
• Post-deployment AI monitoring is crucial for ensuring the safety and effectiveness of AI systems in healthcare. [2]

Abstract
We document a fundamental paradox in AI transparency: explanations improve decisions when algorithms are correct but systematically worsen them when algorithms err. In an experiment with 257 medical students making 3,855 diagnostic decisions, we find explanations increase accuracy by 6.3 percentage points when AI is correct (73% of cases) but decrease it by 4.9 points when incorrect (27% of cases). This asymmetry arises because modern AI systems generate equally persuasive explanations regardless of recommendation quality-physicians cannot distinguish helpful from misleading guidance. We show physicians treat explained AI as 15.2 percentage points more accurate than reality, with over-reliance persisting even for erroneous recommendations. Competent physicians with appropriate uncertainty suffer most from the AI transparency paradox (-12.4pp when AI errs), while overconfident novices benefit most (+9.9pp net). Welfare analysis reveals that selective transparency generates \$2.59 billion in annual healthcare value, 43% more than the \$1.82 billion from mandated universal transparency.

AI Summary

• Explanations can improve diagnostic accuracy when AI advice is correct (+6.3 percentage points) but systematically impair accuracy when AI advice is incorrect (-4.9 percentage points). [3]
• AI Transparency Paradox: The phenomenon where explanations improve diagnostic accuracy when AI advice is correct but harm accuracy when AI advice is incorrect. [3]
• The AI Transparency Paradox highlights the need for careful consideration of how explanations are presented, as they can have both positive and negative effects on decision-making. [3]
• Explanations can create false certainty when AI advice is incorrect, which can lead to poor decisions. [3]
• The structure of the AI Transparency Paradox is criticaly moderated by the advice format, with probabilistic formats amplifying harm rather than mitigating it. [2]
• Explanations can create false certainty precisely when caution is most needed, as they mechanically increase the sum of squared probabilities (SSQ) by shifting probability mass toward the recommended option. [1]