Mass conservation is a fundamental principle in chemistry, servicing as a critical constraint for accurately modeling chemical reactions. Postulated by Antoine Lavoisier in the eighteenth century, it asserts that the total mass of reactants equals the total mass of products, forming the basis for stoichiometry and chemical equation balancing. Despite its simplicity and essentiality, many machine learning models trained on chemical reaction data do not inherently enforce mass conservation. In this work, we introduce a new modeling formulation for reaction outcome prediction that achieves exact conservation by modeling chemical reactivity as a generative and probabilistic process of electron redistribution. The task of reaction outcome prediction has become a popular target for supervised machine learning [1, 2]. While chemists typically conceptualize, visualize, and communicate understanding of chemical reactions through mechanistic arrow-pushing diagrams, most data-driven models bypass this formalism and focus solely on predicting the major product in an end-to-end manner.

We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods relying on static extraction or single-pass learning, our approach couples a reasoning-native large language model with a continually updated graph representation. At each step, the system actively generates new concepts and relationships, merges them into a global graph, and formulates subsequent prompts based on its evolving structure. Through this feedback-driven loop, the model organizes information into a scale-free network characterized by hub formation, stable modularity, and bridging nodes that link disparate knowledge clusters. Over hundreds of iterations, new nodes and edges continue to appear without saturating, while centrality measures and shortest path distributions evolve to yield increasingly distributed connectivity. Our analysis reveals emergent patterns, such as the rise of highly connected 'hub' concepts and the shifting influence of 'bridge' nodes, indicating that agentic, self-reinforcing graph construction can yield open-ended, coherent knowledge structures. Applied to materials design problems, we present compositional reasoning experiments by extracting node-specific and synergy-level principles to foster genuinely novel knowledge synthesis, yielding cross-domain ideas that transcend rote summarization and strengthen the framework's potential for open-ended scientific discovery. We discuss other applications in scientific discovery and outline future directions for enhancing scalability and interpretability.

PCB (printed circuit board) substrates are often single-use, leading to material waste in electronics making. We introduce PCB Renewal, a novel technique that "erases" and "reconfigures" PCB traces by selectively depositing conductive epoxy onto outdated areas, transforming isolated paths into conductive planes that support new traces. We present the PCB Renewal workflow, evaluate its electrical performance and mechanical durability, and model its sustainability impact, including material usage, cost, energy consumption, and time savings. We develop a software plug-in that guides epoxy deposition, generates updated PCB profiles, and calculates resource usage. To demonstrate PCB Renewal's effectiveness and versatility, we repurpose a single PCB across four design iterations spanning three projects: a camera roller, a WiFi radio, and an ESPboy game console. We also show how an outsourced double-layer PCB can be reconfigured, transforming it from an LED watch to an interactive cat toy. The paper concludes with limitations and future directions.

March 28, 2025 Abstract After decades of evolution, the financial system has increasingly deviated from an idealized framework based on precise theorems. With the development of data science and machine intelligence, researchers are trying to digitalize and automate market prediction. However, existing methodologies struggle to represent the diversity of individuals and are regardless of the domino effects of interactions on market dynamics, leading to the poor performance facing abnormal market conditions where non-quantitative information dominates the market. To alleviate these disadvantages requires the introduction of knowledge about how non-quantitative information, like news and policy, affects market dynamics. This study investigates overcoming these challenges through rehearsing potential market trends based on the financial large language model agents whose behaviors are aligned with their cognition and analyses in markets. We propose a hierarchical knowledge architecture for financial large language model agents, integrating fine-tuned language models and specialized generators optimized for trading scenarios. For financial market, we develop an advanced interactive behavioral simulation system that enables users to configure agents and automate market simulations. In this work, we take commodity futures as an example to research the effectiveness of our methodologies. Our real-world case simulation succeeds in rehearsing abnormal market dynamics under geopolitical events and reaches an average accuracy of 3.4% across various points in time after the event on predicting futures price. Under normal market conditions, with corresponding news, our simulator also exhibits lower mean square error than series deep learning models and large language models in predicting three-day futures price of specific commodities. All experimental results demonstrate our method effectively leverages diverse information to simulate behaviors and their impact on market dynamics through systematic interaction. 1 Main The proliferation of financial derivatives in commodity markets, including forward contracts, futures, and options, has been primarily driven by the necessity for price risk mitigation. While these instruments enable investors to profit through finance, they have transformed commodity trading markets into complex human systems [1, 2, 3]. Due to its zero-sum properties, commodity futures represent a relatively straightforward segment within the financial system.

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. We present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.* These authors contributed equally to this work.

Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability and generalizability to novel optimization tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable out-of-domain generalizability to novel tasks. To demonstrate LLMs' potential for molecule optimization, we introduce $\mathtt{MoMUInstruct}$, the first high-quality instruction-tuning dataset specifically focused on complex multi-property molecule optimization tasks. Leveraging $\mathtt{MoMUInstruct}$, we develop $\mathtt{GeLLM^3O}$s, a series of instruction-tuned LLMs for molecule optimization. Extensive evaluations across 5 in-domain and 5 out-of-domain tasks demonstrate that $\mathtt{GeLLM^3O}$s consistently outperform state-of-the-art baselines. $\mathtt{GeLLM^3O}$s also exhibit outstanding zero-shot generalization to unseen tasks, significantly outperforming powerful closed-source LLMs. Such strong generalizability demonstrates the tremendous potential of $\mathtt{GeLLM^3O}$s as foundational models for molecule optimization, thereby tackling novel optimization tasks without resource-intensive retraining. $\mathtt{MoMUInstruct}$, models, and code are accessible through https://github.com/ninglab/GeLLMO.

The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.

A potential encounter this week between Chinese President Xi Jinping and e-commerce icon Jack Ma, coming after a blistering run by tech shares, could be the next catalyst to extend the rally in China's stocks. Prominent entrepreneurs including Ma have been invited to meet the nation's top leaders, people familiar with the matter said last week. The potential show of support for the private sector coincides with the recent surge in equities in Hong Kong, driven by growing capabilities in artificial intelligence. The Hang Seng China Enterprises Index jumped 4.1% on Friday to its highest since February 2022, exceeding an October peak spurred by a stimulus blitz. A tech gauge in Hong Kong entered a bull market earlier this month, fueled by Chinese startup DeepSeek's AI model that's hailed as a game-changer.

Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant training resources. This paper proposes a low-cost, tuning-free method using in-context learning (ICL) to enhance LLM alignment. Through an analysis of high-quality ICL demos, we identified style as a key factor influencing LLM alignment capabilities and explicitly restyled ICL exemplars based on this stylistic framework. Additionally, we combined the restyled demos to achieve a balance between the two conflicting aspects of LLM alignment--factuality and safety. We packaged the restyled examples as prompts to trigger few-shot learning, improving LLM alignment. Compared to the best baseline approach, with an average score of 5.00 as the maximum, our method achieves a maximum 0.10 increase on the Alpaca task (from 4.50 to 4.60), a 0.22 enhancement on the Just-eval benchmark (from 4.34 to 4.56), and a maximum improvement of 0.32 (from 3.53 to 3.85) on the MT-Bench dataset. We release the code and data at https://github.com/AnonymousCode-ComputerScience/RIDE.

Generative models are spearheading recent progress in deep learning, showing strong promise for trajectory sampling in dynamical systems as well. However, while latent space modeling paradigms have transformed image and video generation, similar approaches are more difficult for most dynamical systems. Such systems -- from chemical molecule structures to collective human behavior -- are described by interactions of entities, making them inherently linked to connectivity patterns and the traceability of entities over time. Our approach, LaM-SLidE (Latent Space Modeling of Spatial Dynamical Systems via Linked Entities), combines the advantages of graph neural networks, i.e., the traceability of entities across time-steps, with the efficiency and scalability of recent advances in image and video generation, where pre-trained encoder and decoder are frozen to enable generative modeling in the latent space. The core idea of LaM-SLidE is to introduce identifier representations (IDs) to allow for retrieval of entity properties, e.g., entity coordinates, from latent system representations and thus enables traceability. Experimentally, across different domains, we show that LaM-SLidE performs favorably in terms of speed, accuracy, and generalizability. (Code is available at https://github.com/ml-jku/LaM-SLidE)