Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design.

As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system's information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of information processing. Class I systems are reactive and memoryless, mapping inputs directly to outputs. Class II systems incorporate internal states that provide memory but follow fixed transformation rules. Class III systems are adaptive; their transformation rules themselves change as a function of prior activity. While not sufficient on their own, these dynamics represent necessary informational conditions for genuine agency. This hierarchy offers a measurable, substrate-independent way to identify the informational precursors of agency. We illustrate the framework with neurophysiological and computational examples, including thermostats and receptor-like memristors, and discuss its implications for the ethical and functional evaluation of systems that may exhibit agency.

Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in multi-sensor systems or quantify uncertainty in HI reliability. This paper introduces a novel framework for HI construction, advancing three key contributions. First, we adapt Reconstruction along Projected Pathways (RaPP) as a health indicator (HI) for RUL prediction for the first time, showing that it outperforms traditional reconstruction error metrics. Second, we show that augmenting RaPP-derived HIs with aleatoric and epistemic uncertainty quantification (UQ)--via Monte Carlo dropout and probabilistic latent spaces-- significantly improves RUL-prediction robustness. Third, and most critically, we propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE which enables interpretable, mechanism-specific diagnostics. Evaluated on data sourced from aerospace and manufacturing systems, our approach achieves marked improvements in accuracy and generalizability compared to state-of-the-art HI methods while providing actionable insights into system failure pathways.

Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences across sectors. However, existing assessments often evaluate emerging energy pathways and their impacts in silos, overlooking critical interactions such as regional resource competition and cumulative impacts. We present an integrated modeling framework that couples agent-based modeling and Life Cycle Assessment (LCA) to simulate how energy transition pathways interact with regional resource competition, ecological constraints, and community-level burdens. We apply the model to a case study in Southern California. The results demonstrate how integrated and multiscale decision making can shape energy pathway deployment and reveal spatially explicit trade-offs under scenario-driven constraints. This modeling framework can further support more adaptive and resilient energy transition planning on spatial and institutional scales.

With rapid urbanization, predicting urban microclimates has become critical, as it affects building energy demand and public health risks. However, existing generative and homogeneous graph approaches fall short in capturing physical consistency, spatial dependencies, and temporal variability. To address this, we introduce UrbanGraph, a physics-informed framework integrating heterogeneous and dynamic spatio-temporal graphs. It encodes key physical processes -- vegetation evapotranspiration, shading, and convective diffusion -- while modeling complex spatial dependencies among diverse urban entities and their temporal evolution. We evaluate UrbanGraph on UMC4/12, a physics-based simulation dataset covering diverse urban configurations and climates. Results show that UrbanGraph improves $R^2$ by up to 10.8% and reduces FLOPs by 17.0% over all baselines, with heterogeneous and dynamic graphs contributing 3.5% and 7.1% gains. Our dataset provides the first high-resolution benchmark for spatio-temporal microclimate modeling, and our method extends to broader urban heterogeneous dynamic computing tasks.

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion rec ognition and video highlight detection, yet achieving this through manu al human annotations is challenging. Inspired by inter-subject corre lation (ISC) utilized in neuroscience, this study introduces a novel Electr oencephalog-raphy (EEG) based ISC methodology that leverages a single-e lectrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitabl e for classifying emotions--first-order difference (FD) an differential entrop y (DE). Secondly, through the use of overall correlation analysis, we d emonstrate the heterogeneous synchronized performance of electrodes. Th is performance aligns with neural emotion patterns established in prior st udies, thus validating the effectiveness of our approach. Thirdly, by emplo ying a sliding window correlation technique, we showcase the significant c onsistency of dynamic ISCs across various features or key electrodes in ea ch analyzed film clip. Our findings indicate the method's reliability in c apturing consistent, dynamic shared neural synchrony among individual s, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal . The implications of this research are significant for advancement s in affective computing and the broader neuroscience field, suggesting a s treamlined and effective tool for emotion analysis in real-world applic ations. 2 G. Pan et al.

Despite its significance, the underwater world remains largely overlooked as a result of the challenging conditions that hinder traditional research methods. Historically, the study of marine ecosystems relied on labor intensive research [1], which provided limited data and had a high error margin. In recent years, advances in autonomous and remotely operated vehicles (AUVs and ROVs) have revolutionized underwater exploration. These technologies, equipped with object detection systems, now allow real-time monitoring, which includes capturing images of marine organisms, environmental conditions, and even assessing biodiversity [2], [3]. However, the quality of images and videos captured underwater remains a significant obstacle. Light absorption, scattering, and water-related distortions, such as haze and color shifts [4], create noisy low-contrast images, further compounded by complex underwater backgrounds and camera motion. These challenges call for advanced detection techniques capable of accurately identifying and localizing objects despite underwater noise. Efficient underwater object detection (UOD) is crucial for a variety of marine applications, including biodiversity monitoring, conservation efforts, and resource management.

This work adapts a neuroscientific contrast localizer to pinpoint causally relevant units for Theory of Mind (ToM) and mathematical reasoning tasks in large language models (LLMs) and vision-language models (VLMs). Across 11 LLMs and 5 VLMs ranging in size from 3B to 90B parameters, we localize top-activated units using contrastive stimulus sets and assess their causal role via targeted ablations. We compare the effect of lesioning functionally selected units against low-activation and randomly selected units on downstream accuracy across established ToM and mathematical benchmarks. Contrary to expectations, low-activation units sometimes produced larger performance drops than the highly activated ones, and units derived from the mathematical localizer often impaired ToM performance more than those from the ToM localizer. These findings call into question the causal relevance of contrast-based localizers and highlight the need for broader stimulus sets and more accurately capture task-specific units.