Each environment state has a corresponding array and a scene graph representation.

Conventional Data Processing Workflow Conventional MD analysis, which has been used in previous data evaluation [2, 32, 33] and can serve labeling and validation purposes for ML model construction and preparation, employs a multi-tiered data distillation process to derive robust trends, see Figure 1. In the left column of this figure, we show representative examples of computational tomographs through the 3D MD model, with the atoms colored by (a) grain orientation in electron backscatter diffraction (EBSD) standard, (b) lattice type, grain boundaries, and defects, (c) advection (drift) velocity to visualize shearing, and (d) local stresses. As a first step in the data distillation process, these 3D data that are stored for each atom are averaged across the lateral system dimensions, revealing depth-resolved, time-dependent quantities of interest, as visualized in the heat map at the top of the middle column (e). Further elimination of the sample depth and time dimensions leads to time-resolved global quantities (f) and contact pressure dependent trends (g), which can be fitted with characteristic pressures that mark the transition between deformation patterns (h). As an outlook to the utility of such highly distilled data, we propose their incorporation into Ashby-style charts, as schematically shown in Figure 1 (i), which link material properties with tribological properties. This conventional approach 2 accommodates the complexities of polycrystalline materials under tribological loading conditions and is guided by the underlying physics, resulting in this time-consuming procedure. Thus, substituting this approach with a well-trained ML model is highly relevant. The conventional approach can serve as the ground truth for training this ML model or to refine and validate said model based on newly generated MD data.

Large language models (LLMs) exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced pattern recognition remains an open question. In this study, we investigate how LLMs process the temporal meaning of linguistic aspect in narratives that were previously used in human studies. Using an Expert-in-the-Loop probing pipeline, we conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner. Our findings show that LLMs over-rely on prototypicality, produce inconsistent aspectual judgments, and struggle with causal reasoning derived from aspect, raising concerns about their ability to fully comprehend narratives. These results suggest that LLMs process aspect fundamentally differently from humans and lack robust narrative understanding. Beyond these empirical findings, we develop a standardized experimental framework for the reliable assessment of LLMs' cognitive and linguistic capabilities.

In this paper, we propose ProRAC (Progression-based Reasoning about Actions and Change), a neuro-symbolic framework that leverages LLMs to tackle RAC problems. ProRAC extracts fundamental RAC elements including actions and questions from the problem, progressively executes each action to derive the final state, and then evaluates the query against the progressed state to arrive at an answer. We evaluate ProRAC on several RAC benchmarks, and the results demonstrate that our approach achieves strong performance across different benchmarks, domains, LLM backbones, and types of RAC tasks.

The Heun method was used for integration.

Geodesic problems involve computing trajectories between prescribed initial and final states to minimize a user-defined measure of distance, cost, or energy. They arise throughout physics and engineering -- for instance, in determining optimal paths through complex environments, modeling light propagation in refractive media, and the study of spacetime trajectories in control theory and general relativity. Despite their ubiquity, the scientific machine learning (SciML) community has given relatively little attention to investigating its methods in the context of these problems. In this work, we argue that given their simple geometry, variational structure, and natural nonlinearity, geodesic problems are particularly well-suited for the Deep Ritz method. We substantiate this claim with four numerical examples drawn from path planning, optics, solid mechanics, and generative modeling. Our goal is not to provide an exhaustive study of geodesic problems, but rather to identify a promising application of the Deep Ritz method and a fruitful direction for future SciML research.

The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure--activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the excellent catalytic performance of inverse catalysts observed in experiments.

State preparation is a cornerstone of quantum technologies, underpinning applications in computation, communication, and sensing. Its importance becomes even more pronounced in non-Markovian open quantum systems, where environmental memory and model uncertainties pose significant challenges to achieving high-fidelity control. Invariant-based inverse engineering provides a principled framework for synthesizing analytic control fields, yet existing parameterizations often lead to experimentally infeasible, singular pulses and are limited to simplified noise models such as those of Lindblad form. Here, we introduce a generalized invariant-based protocol for single-qubit state preparation under arbitrary noise conditions. The control proceeds in two-stages: first, we construct a family of bounded pulses that achieve perfect state preparation in a closed system; second, we identify the optimal member of this family that minimizes the effect of noise. The framework accommodates both (i) characterized noise, enabling noise-aware control synthesis, and (ii) uncharacterized noise, where a noise-agnostic variant preserves robustness without requiring a master-equation description. Numerical simulations demonstrate high-fidelity state preparation across diverse targets while producing smooth, hardware-feasible control fields. This singularity-free framework extends invariant-based control to realistic open-system regimes, providing a versatile route toward robust quantum state engineering on NISQ hardware and other platforms exhibiting non-Markovian dynamics.

In addition to matching the performance of Transformers (Vaswani et al. 2017) across many tasks, the recurrent mechanism brings two benefits: the ability to efficiently process long sequences thanks to its linear complexity, and the capacity to easily process tokens beyond their training context by simply rolling out the state. Nevertheless, in practice these benefits are often unrealized, given that their performance can drop considerably when the sequence length exceeds their training context (Ben-Kish et al. 2024; Waleffe et al. 2024; Ye et al. 2025; Yuan et al. 2024). This naturally leads to two questions: (1) why do these models fail to length generalize? and (2) how can we efficiently enable length generalization across several recurrent models? Recently, some works have studied the length generalization of Mamba (Dao and Gu 2024) and have proposed solutions such as forcing the model to forget previous context (Yingfa Chen et al. 2024) or skipping tokens in the state update to reduce the effective context of the processed sequence (Ben-Kish et al. 2024; Ye et al. 2025). However, these methods require changing the internal mechanism of Mamba and might not be easily transferable to other architectures. Other works have linked length generalization to state capacity and overfitting (Yingfa Chen et al. 2024; S. Wang 2024), proposing training on longer sequences and with Truncated Backpropagation Through Time (TBTT) (Sutskever 2013; Williams and J. Peng 1990) as a way to enable length generalization. In this work, we reason about the distribution of states that the model is trained on to introduce a precise hypothesis that explains why recurrent models fail to length generalize. Moreover, we perform comprehensive interventions that elucidate on what distributions recurrent models need to be trained to enable length generalization. 1

Each environment state has a corresponding array and a scene graph representation.