OpenRaiser/Intern-Atlas huggingface dataset:
Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media
Venue: arXiv cs.LG 2025
Year: 2025

The ability to predict how shock waves traverse porous and architected materials is a key challenge in planetary defense and in the pursuit of inertial fusion energy. Yet capturing pore collapse, anomalous Hugoniot responses, and localized heating - phenomena that strongly influence asteroid deflection or fusion ignition - has remained a major challenge despite recent advances in single-field and reduced representations. We introduce a multi-field spatio-temporal model (MSTM) that unifies seven coupled fields - pressure, density, temperature, energy, material distribution, and two velocity components - into a single autoregressive surrogate. Trained on high-fidelity hydrocode data, MSTM captures nonlinear shock-driven dynamics across porous and architected configurations, achieving mean errors of 1.4% and 3.2% respectively, all while delivering over three orders of magnitude in speedup. MSTM reduces mean-squared error and structural dissimilarity by 94% relative torelative to single-field spatio-temporal models. This advance transforms problems once considered intractable into tractable design studies, establishing a practical framework for optimizing meso-structured materials in planetary impact mitigation and inertial fusion energy.
Strategic Analysis of Just-In-Time Liquidity Provision in Concentrated Liquidity Market Makers
Venue: arXiv cs.GT 2025
Year: 2025

Liquidity providers (LPs) are essential figures in the operation of automated market makers (AMMs); in exchange for transaction fees, LPs lend the liquidity that allows AMMs to operate. While many prior works have studied the incentive structures of LPs in general, we currently lack a principled understanding of a special class of LPs known as Just-In-Time (JIT) LPs. These are strategic agents who momentarily supply liquidity for a single swap, in an attempt to extract disproportionately high fees relative to the remaining passive LPs. This paper provides the first formal, transaction-level model of JIT liquidity provision for a widespread class of AMMs known as Concentrated Liquidity Market Makers (CLMMs), as seen in Uniswap V3, for instance. We characterize the landscape of price impact and fee allocation in these systems, formulate and analyze a non-linear optimization problem faced by JIT LPs, and prove the existence of an optimal strategy. By fitting our optimal solution for JIT LPs to real-world CLMMs, we observe that in liquidity pools (particularly those with risky assets), there is a significant gap between observed and optimal JIT behavior. Existing JIT LPs often fail to account for price impact; doing so, we estimate they could increase earnings by up to 69% on average over small time windows. We also show that JIT liquidity, when deployed strategically, can improve market efficiency by reducing slippage for traders, albeit at the cost of eroding average passive LP profits by up to 44% per trade.
Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
Venue: arXiv cs.LG 2025
Year: 2025

When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weakness of parametric machine learning systems is their failure to exhibit latent learning -- learning information that is not relevant to the task at hand, but that might be useful in a future task. We show how this perspective links failures ranging from the reversal curse in language modeling to new findings on agent-based navigation. We then highlight how cognitive science points to episodic memory as a potential part of the solution to these issues. Correspondingly, we show that a system with an oracle retrieval mechanism can use learning experiences more flexibly to generalize better across many of these challenges. We also identify some of the essential components for effectively using retrieval, including the importance of within-example in-context learning for acquiring the ability to use information across retrieved examples. In summary, our results illustrate one possible contributor to the relative data inefficiency of current machine learning systems compared to natural intelligence, and help to understand how retrieval methods can complement parametric learning to improve generalization. We close by discussing some of the links between these findings and prior results in cognitive science and neuroscience, and the broader implications.
Predictive Machine Learning to Increase the Throughput of Container Yards
Venue: arXiv cs.CE 2025
Year: 2025

This study seeks to improve the throughput rates for shipping container terminals. In the United States, shipping ports link the domestic economy to global markets and are vital to sustain supply chain flow and economic stability. Maritime shipping accounts for nearly half of the U.S.'s annual international trade, two thirds of which are represented by container shipping. Previous studies highlighted the capability of automation in enhancing container processing; however, unlike in European and East Asian ports, full automation is limited in U.S. ports due to legal protections for human labor. Consequently, there is a need for alternative methods that deliver automation level efficiencies while maintaining the terms of cooperative agreements. This paper proposes an Intelligent Planning System (IPS) that applies the concept of Pareto Optimization to container yards through a mixed integer linear programming (MILP) based recursive appointment system. The results show an improvement from baseline for both daily terminal throughput volumes and processing times. The generated IPS can be employed to provide recommendations for container positioning and truck pickup appointments to optimize container yard layout and flow resulting in reduced realtime congestion and predictively mitigated future congestion.
Scaling Digital Twin Models
Venue: arXiv cs.CE 2025
Year: 2025

In many industries, the scale and complexity of systems can present significant barriers to the development of accurate digital twin models. This paper introduces a novel methodology and a modular computational tool utilizing machine learning and dimensional analysis to establish a framework for scaling digital twin models. Scaling techniques have not yet been applied to digital twin technology, but they can eliminate the need for repetitive physical calibration of such models in industries where product lines include a variety of sizes of the same or similar products. In many cases, it may be easier or more cost-effective to perform physical calibration of the digital twin model on smaller units of a product line. Scaling techniques can then allow adapting the calibration data from the smaller units to other sizes of the product line without the need for additional data collection and experimentation for calibration. Conventional application of dimensional analysis for scaling in this context introduces several challenges due to distortion of scaling factors. This paper addresses these challenges and introduces a framework for proper scaling of digital twin models. The results are applied to scaling the models between an industrial-size wheel loader vehicle used in construction to a miniaturized system instrumented in a laboratory setting.
Strain localization in reduced order asymptotic homogenization
Venue: arXiv cs.CE 2025
Year: 2025

A reduced order asymptotic homogenization based multiscale technique which can capture damage and inelastic effects in composite materials is proposed. This technique is based on two scale homogenization procedure where eigen strain representation accounts for the inelastic response and the computational efforts are alleviated by reduction of order technique. Macroscale stress is derived by calculating the influence tensors from the analysis of representative volume element (RVE). At microscale, the damage in the material is modeled using continuum damage mechanics (CDM) based framework. To solve the problem of strain localization a method of the alteration of stress-strain relation of micro constituents based on the dissipated fracture energy in a crack band is implemented. The issue of spurious post failure artificial stiffness at macroscale is discussed and effect of increasing the order to alleviate this problem is checked. Verification studies demonstrated the proposed formulation predicts the macroscale response and also captures the damage and plasticity induced inelastic strains.
DarwinWafer: A Wafer-Scale Neuromorphic Chip
Venue: arXiv cs.ET 2025
Year: 2025

Neuromorphic computing promises brain-like efficiency, yet today's multi-chip systems scale over PCBs and incur orders-of-magnitude penalties in bandwidth, latency, and energy, undermining biological algorithms and system efficiency. We present DarwinWafer, a hyperscale system-on-wafer that replaces off-chip interconnects with wafer-scale, high-density integration of 64 Darwin3 chiplets on a 300 mm silicon interposer. A GALS NoC within each chiplet and an AER-based asynchronous wafer fabric with hierarchical time-step synchronization provide low-latency, coherent operation across the wafer. Each chiplet implements 2.35 M neurons and 0.1 B synapses, yielding 0.15 B neurons and 6.4 B synapses per wafer.At 333 MHz and 0.8 V, DarwinWafer consumes ~100 W and achieves 4.9 pJ/SOP, with 64 TSOPS peak throughput (0.64 TSOPS/W). Realization is enabled by a holistic chiplet-interposer co-design flow (including an in-house interposer-bump planner with early SI/PI and electro-thermal closure) and a warpage-tolerant assembly that fans out I/O via PCBlets and compliant pogo-pin connections, enabling robust, demountable wafer-to-board integration. Measurements confirm 10 mV supply droop and a uniform thermal profile (34-36 °C) under ~100 W. Application studies demonstrate whole-brain simulations: two zebrafish brains per chiplet with high connectivity fidelity (Spearman r = 0.896) and a mouse brain mapped across 32 chiplets (r = 0.645). To our knowledge, DarwinWafer represents a pioneering demonstration of wafer-scale neuromorphic computing, establishing a viable and scalable path toward large-scale, brain-like computation on silicon by replacing PCB-level interconnects with high-density, on-wafer integration.
On the Detection of Internal Defects in Structured Media
Venue: arXiv cs.CE 2025
Year: 2025

A critical issue that affects engineers trying to assess the structural integrity of various infrastructures, such as metal rods or acoustic ducts, is the challenge of detecting internal fractures (defects). Traditionally, engineers depend on audible and visual aids to identify these fractures, as they do not physically dissect the object in question into multiple pieces to check for inconsistencies. This research introduces ideas towards the development of a robust strategy to image such defects using only a small set of minimal, non-invasive measurements. Assuming a one dimensional model (e.g. longitudinal waves in long and thin rods/acoustic ducts or transverse vibrations of strings), we make use of the continuous one-dimensional wave equation to model these physical phenomena and then employ specialized mathematical analysis tools (the Laplace transform and optimization) to introduce our defect imaging ideas. In particular, we will focus on the case of a long bar which is homogeneous throughout except in a small area where a defect in its Young's modulus is present. We will first demonstrate how the problem is equivalent to a spring-mass vibrational system, and then show how our imaging strategy makes use of the Laplace domain analytic map between the characteristics of the respective defect and the measurement data. More explicitly, we will utilize MATLAB (a platform for numerical computations) to collect synthetic data (computational alternative to real world measurements) for several scenarios with one defect of arbitrary location and stiffness. Subsequently, we will use this data along with our analytically developed map (between defect characteristics and measurements) to construct a residual function which, once optimized, will reveal the location and magnitude of the stiffness defect.
Evaluation of Ensemble Learning Techniques for handwritten OCR Improvement
Venue: arXiv cs.CV 2025
Year: 2025

For the bachelor project 2021 of Professor Lippert's research group, handwritten entries of historical patient records needed to be digitized using Optical Character Recognition (OCR) methods. Since the data will be used in the future, a high degree of accuracy is naturally required. Especially in the medical field this has even more importance. Ensemble Learning is a method that combines several machine learning models and is claimed to be able to achieve an increased accuracy for existing methods. For this reason, Ensemble Learning in combination with OCR is investigated in this work in order to create added value for the digitization of the patient records. It was possible to discover that ensemble learning can lead to an increased accuracy for OCR, which methods were able to achieve this and that the size of the training data set did not play a role here.
An Open Dataset for Temperature Modelling in Machine Tools
Venue: arXiv cs.CE 2025
Year: 2025

This data set descriptor introduces a structured, high-resolution dataset of transient thermal simulations for a vertical axis of a machine tool test rig. The data set includes temperature and heat flux values recorded at 29 probe locations at 1800 time steps, sampled every second over a 30-minute range, across 17 simulation runs derived from a fractional factorial design. First, a computer-aided design model was de-featured, segmented, and optimized, followed by finite element (FE) modelling. Detailed information on material, mesh, and boundary conditions is included. To support research and model development, the dataset provides summary statistics, thermal evolution plots, correlation matrix analyses, and a reproducible Jupyter notebook. The data set is designed to support machine learning and deep learning applications in thermal modelling for prediction, correction, and compensation of thermally induced deviations in mechanical systems, and aims to support researchers without FE expertise by providing ready-to-use simulation data.
Increasing Inter-Fiber Contact in the Altendorf-Jeulin Model
Venue: arXiv cs.CE 2025
Year: 2025

In fields such as material design or biomedicine, fiber materials play an important role. Fiber simulations, also called digital twins, provide a basis for testing and optimizing the material's physical behavior digitally. Inter-fiber contacts can influence the thermal and mechanical behavior of a fiber system; to our knowledge, however, there exist no parametric fiber models allowing for explicit modeling of the number of inter-fiber contacts. Therefore, this paper proposes an extension of the iterative force-biased fiber packing by Altendorf \& Jeulin. In this extension, we model the inter-fiber contacts explicitly and add another force to the force-biased packing to increase the number of contacts. We successfully validate the packing with respect to its parameter accuracy. Moreover, we show that the extension indeed increases the number of contacts, even exceeding theoretical values. Hence, this packing scheme has the potential to achieve higher accuracy in physical simulations.
On LLM-Based Scientific Inductive Reasoning Beyond Equations
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.
Compositional Interface Refinement Through Subtyping in Probabilistic Session Types
Venue: arXiv cs.LO 2025
Year: 2025

Multiparty session types (MPST) are a robust typing framework that ensures safe and deadlock-free communication within distributed protocols. As these protocols grow in complexity, compositional modelling becomes increasingly important to scalably verify their behaviour. Therefore, we propose using a refinement-based subtyping approach to facilitate the modularity needed for compositional verification. Subtyping in classic MPST systems inherently represents a notion of refinement: A larger type may be safely substituted by a smaller, refined type. The aim of this thesis is to significantly extend this concept and discover just how flexible and expressive subtyping relations can be. We present a probabilistic extension for MPST, the probabilistic mixed choice multiparty session pi-calculus, with a novel, flexible subtyping system which allows one channel (the interface) to be substituted by several channels (the refinement). Our subtyping is remarkably expressive; any selection of well-typed channels as the refinement has a corresponding interface in a single channel type. To facilitate this generality, we base our system on a powerful variant of MPST, mixed choice multiparty session types (MCMP), which offers greater flexibility in communication choices. We establish soundness of the probabilistic mixed choice multiparty session system through several key results. In particular, we prove subject reduction, error-freedom and deadlock-freedom, ensuring that well-typed processes are well-behaved. This work demonstrates subtyping to possess great previously untapped potential for stepwise refinement and compositional verification. The presented framework enables highly expressive, compositional, and verifiable modelling of probabilistic distributed communication.
A Scalable and Interoperable Platform for Transforming Building Information with Brick Ontology
Venue: arXiv cs.CY 2025
Year: 2025

In the digital twin and building information era, many building automation companies searched for scalable methods to extract and analyze different building data, including Internet of Things (IoT) sensors, actuators, layout sections, zones, etc. The necessity for engineers to continuously manage the entire process for each new building creates scalability challenges. Furthermore, because construction information is sensitive, transferring data on vendor platforms via the cloud creates problems. This paper introduces a platform designed to address some of the common challenges in building automation. This is a smart platform designed for the transformation of building information into Brick ontology (Brick 2020) and graph formats. This technology makes it easy to retrieve historical data and converts the building point list into a Brick schema model for use in digital twin applications. The overarching goal of the proposed platform development is semi-automate the process while offering adaptability to various building configurations. This platform uses Brick schema and graph data structure techniques to minimize complexity, offering a semi-automated approach through its use of a tree-based graph structure. Moreover, the integration of Brick ontology creates a common language for interoperability and improves building information management. The seamless and offline integration of historical data within the developed platform minimizes data security risks when handling building information.
Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking
Venue: arXiv cs.CY 2025
Year: 2025

This study examines the impact of Socratic Mind, a Generative Artificial Intelligence (GenAI) powered formative assessment tool that employs Socratic questioning to support student learning in a large, fully online undergraduate-level computing course. Employing a quasi-experimental, mixed-methods design, we investigated participants' engagement patterns, the influence of user experience on engagement, and impacts on both perceived and actual learning outcomes. Data were collected from the system logs, surveys on user experience and perceived engagement and learning gains, student reflections, and course performance data. Results indicated that participants consistently reported high levels of affective, behavioral, and cognitive engagement, and these were strongly linked to positive user experiences and perceived learning outcomes. Quantitative analysis further revealed that students who engaged with the GenAI tool experienced significant gains in their quiz scores compared to those who did not, particularly benefiting students with lower baseline achievement. Additionally, thematic analysis of qualitative feedback revealed substantial perceived improvements in higher-order thinking skills, including problem solving, critical thinking, and self-reflection. Our findings highlight the promise of AI-mediated dialogue in fostering deeper engagement and higher-order cognitive skills. As higher education institutions expand GenAI integration in curriculum, this dialogic, GenAI powered assessment tool can offer a scalable strategy to promote students' meaningful learning outcomes.
Reconnecting Citizens to Politics via Blockchain - Starting the Debate
Venue: arXiv cs.CR 2025
Year: 2025

Elections are not the only but arguably one of the most important pillars for the proper functioning of liberal democracies. Recent evidence across the globe shows that it is not straightforward to conduct them in a free and fair manner. One constant concern is the role of money in politics, more specifically, election campaign financing. Frequent scandals are proof of the difficulties encountered with current approaches to tackle the issue. Suggestions on how to overcome the problem exist but seem difficult to implement. With the help of blockchain technology we might be able to make a step forward. A separate crypto currency specifically designed to pay for costs of political campaigning and advertising could be introduced. Admittedly, at this stage, there are many open questions. However, under the assumption that blockchain technology is here to stay, it is an idea that deserves further exploration.