Dynamic energy systems and controls require advanced modeling frameworks to design and test supervisory and fault tolerant strategies. Modelica is a widely used equation based language, but developing control modules is labor intensive and requires specialized expertise. This paper examines the use of large language models (LLMs) to automate the generation of Control Description Language modules in the Building Modelica Library as a case study. We developed a structured workflow that combines standardized prompt scaffolds, library aware grounding, automated compilation with OpenModelica, and human in the loop evaluation. Experiments were carried out on four basic logic tasks (And, Or, Not, and Switch) and five control modules (chiller enable/disable, bypass valve control, cooling tower fan speed, plant requests, and relief damper control). The results showed that GPT 4o failed to produce executable Modelica code in zero shot mode, while Claude Sonnet 4 achieved up to full success for basic logic blocks with carefully engineered prompts. For control modules, success rates reached 83 percent, and failed outputs required medium level human repair (estimated one to eight hours). Retrieval augmented generation often produced mismatches in module selection (for example, And retrieved as Or), while a deterministic hard rule search strategy avoided these errors. Human evaluation also outperformed AI evaluation, since current LLMs cannot assess simulation results or validate behavioral correctness. Despite these limitations, the LLM assisted workflow reduced the average development time from 10 to 20 hours down to 4 to 6 hours per module, corresponding to 40 to 60 percent time savings. These results highlight both the potential and current limitations of LLM assisted Modelica generation, and point to future research in pre simulation validation, stronger grounding, and closed loop evaluation.

Abstract-- This paper presents a novel framework for analyzing Incremental-Input-to-State Stability (δISS) based on the idea of using rewards as "test functions." Whereas control theory traditionally deals with Lyapunov functions that satisfy a time-decrease condition, reinforcement learning (RL) value functions are constructed by exponentially decaying a Lipschitz reward function that may be non-smooth and unbounded on both sides. Thus, these RL-style value functions cannot be directly understood as Lyapunov certificates. We develop a new equivalence between a variant of incremental input-to-state stability of a closed-loop system under given a policy, and the regularity of RL-style value functions under adversarial selection of a H older-continuous reward function. This result highlights that the regularity of value functions, and their connection to incremental stability, can be understood in a way that is distinct from the traditional Lyapunov-based approach to certifying stability in control theory.

The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memorizing) but also introduces new burdens (e.g., earning, compliance, psychological burdens). This research contributes to a more nuanced understanding of AI integration in organizational settings and highlights the efforts and burdens undertaken by CSRs to adapt to the updated system.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Ukraine has worsened fuel shortages across Russia in the past week as it has continued to hit Russia's refineries and energy infrastructure with long-range drones while Poland has called for more oil sanctions in the wake of Russia's first drone attack on NATO soil. In the meantime, Russia's creeping advance resulted in the capture of three villages over the past week, and perhaps for the first time, Ukraine's command reacted by dismissing the retreating officers.

US and UK sign major nuclear power deal: What does it include? British Prime Minister Keir Starmer and United States President Donald Trump have signed a multibillion-pound deal to expand nuclear power across both nations. Known as the Atlantic Partnership for Advanced Nuclear Energy, the agreement aims to speed up the construction of new reactors and provide reliable, low-carbon energy for high-demand sectors, including energy-intensive artificial intelligence data centres. Britain's largest energy supplier, Centrica, will pair up with the US firm X-energy to develop up to 12 advanced modular reactors in Hartlepool, a port town in northeast England, which could power 1.5 million homes and create up to 2,500 jobs. US nuclear technology company Holtec, France's state-backed energy giant EDF Energy, and United Kingdom real estate and investment firm Tritax will develop advanced data centres powered by small modular reactors (SMRs) in Nottinghamshire, East Midlands, valued at about 11 billion pounds ($15bn).

President Donald Trump and U.K. Prime Minister Sir Keir Starmer signed what the latter referred to as a "groundbreaking" new U.K.-U.S. Tech Prosperity Deal on Thursday. Praising the commitment, Starmer said "the deals and investment being announced today break all records." "What a day, 250 billion pounds [340 billion dollars] flowing both ways across the Atlantic," Starmer said. "It is the biggest investment package of its kind in British history by a country mile." The deal focuses heavily on AI investment, with Starmer announcing significant investments from companies including Nvidia, Nscale, OpenAI, Google, and Salesforce that would create "cutting-edge British jobs for years to come."

Artificial intelligence can draw cat pictures and write emails. A research team in California says it used AI to propose new genetic codes for viruses--and managed to get several of them to replicate and kill bacteria. The work, described in a preprint paper, has the potential to create new treatments and accelerate research into artificially engineered cells. But experts believe it is also an "impressive first step" toward AI-designed life forms. Hydrogen is sometimes held up as a master key for the energy transition. It can be made using several low-emissions methods and could play a role in cleaning up industries ranging from agriculture to aviation to shipping.

Anti-Trump Protesters Take Aim at'Naive' US-UK AI Deal Thousands marched in London to protest President Donald Trump's second state visit. Among them were many environmental activists unhappy with Britain's new AI deal with the US. They played extremely loud music. They let off foul-smelling smoke from a can. Thousands of people gathered on Wednesday in central London to protest against Trump's presence in the UK, accusing the UK government of kowtowing to him by hosting him for a state visit for the second time.

We use artificial intelligence (AI) and supervisory adaptive control systems to plan and optimize the mission of precise spacecraft formation. Machine learning and robust control enhance the efficiency of spacecraft precision formation of the Virtual Telescope for X-ray Observation (VTXO) space mission. VTXO is a precise formation of two separate spacecraft making a virtual telescope with a one-kilometer focal length. One spacecraft carries the lens and the other spacecraft holds the camera to observe high-energy space objects in the X-ray domain with 55 milli-arcsecond angular resolution accuracy. Timed automata for supervisory control, Monte Carlo simulations for stability and robustness evaluation, and integration of deep neural networks for optimal estimation of mission parameters, satisfy the high precision mission criteria. We integrate deep neural networks with a constrained, non-convex dynamic optimization pipeline to predict optimal mission parameters, ensuring precision mission criteria are met. AI framework provides explainability by predicting the resulting energy consumption and mission error for a given set of mission parameters. It allows for transparent, justifiable, and real-time trade-offs, a capability not present in traditional adaptive controllers. The results show reductions in energy consumption and improved mission accuracy, demonstrating the capability of the system to address dynamic uncertainties and disturbances.

In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promising democratic forum for developing effective policy outcomes on complex global issues. Despite theoretical attention, there remains limited empirical work that systematically traces how specific ideas evolve, are prioritized, or are discarded during deliberation to form policy recommendations. Addressing these gaps, this work poses two central questions: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To address these questions, we develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced in-person deliberative assembly. The framework identifies and visualizes the space of expressed suggestions. We also empirically reconstruct each delegate's evolving perspective throughout the assembly. Our methods contribute novel empirical insights into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs.