We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using "floating" orbitals. Floating orbitals are a long-standing idea in the quantum chemistry community that promises more compact and accurate representations by placing orbitals freely in space, as opposed to centering all orbitals at the position of atoms. Finding ideal placements of these orbitals requires extensive domain knowledge though, which thus far has prevented widespread adoption. We solve this in a data-driven manner by training a Cartesian tensor network to predict orbital positions along with orbital coefficients. This is made possible through a symmetry-breaking mechanism that is used to learn position displacements with lower symmetry than the input molecule while preserving the rotation equivariance of the charge density itself. Inspired by recent successes of Gaussian Splatting in representing densities in space, we are using Gaussians as our orbitals and predict their weights and covariance matrices. Our method achieves a state-of-the-art balance between computational efficiency and predictive accuracy on established benchmarks.

The emergence of large language models (LLMs) opens new frontiers for unmanned aerial vehicle (UAVs), yet existing systems remain confined to predefined tasks due to hardware-software co-design challenges. This paper presents the first aerial intelligent agent capable of open-world task execution through tight integration of LLM-based reasoning and robotic autonomy. Our hardware-software co-designed system addresses two fundamental limitations: (1) Onboard LLM operation via an edge-optimized computing platform, achieving 5-6 tokens/sec inference for 14B-parameter models at 220W peak power; (2) A bidirectional cognitive architecture that synergizes slow deliberative planning (LLM task planning) with fast reactive control (state estimation, mapping, obstacle avoidance, and motion planning). Validated through preliminary results using our prototype, the system demonstrates reliable task planning and scene understanding in communication-constrained environments, such as sugarcane monitoring, power grid inspection, mine tunnel exploration, and biological observation applications. This work establishes a novel framework for embodied aerial artificial intelligence, bridging the gap between task planning and robotic autonomy in open environments.

Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive frameworks. To address these challenges, we proposed LangTime, a language-guided unified model for time series forecasting that incorporates cross-domain pre-training with reinforcement learning-based fine-tuning. Specifically, LangTime constructs Temporal Comprehension Prompts (TCPs), which include dataset-wise and channel-wise instructions, to facilitate domain adaptation and condense time series into a single token, enabling LLMs to understand better and align temporal data. To improve autoregressive forecasting, we introduce TimePPO, a reinforcement learning-based fine-tuning algorithm. TimePPO mitigates error accumulation by leveraging a multidimensional rewards function tailored for time series and a repeat-based value estimation strategy. Extensive experiments demonstrate that LangTime achieves state-of-the-art cross-domain forecasting performance, while TimePPO fine-tuning effectively enhances the stability and accuracy of autoregressive forecasting.

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Health evaluation for lithium-ion batteries (LIBs) typically relies on constant charging/discharging protocols, often neglecting scenarios involving dynamic current profiles prevalent in electric vehicles. Conventional health indicators for LIBs also depend on the uniformity of measured data, restricting their adaptability to non-uniform conditions. In this study, a novel training strategy for estimating LIB health based on the paradigm of self-supervised learning is proposed. A multiresolution analysis technique, empirical wavelet transform, is utilized to decompose non-stationary voltage signals in the frequency domain. This allows the removal of ineffective components for the health evaluation model. The transformer neural network serves as the model backbone, and a loss function is designed to describe the capacity degradation behavior with the assumption that the degradation in LIBs across most operating conditions is inevitable and irreversible. The results show that the model can learn the aging characteristics by analyzing sequences of voltage and current profiles obtained at various time intervals from the same LIB cell. The proposed method is successfully applied to the Stanford University LIB aging dataset, derived from electric vehicle real driving profiles. Notably, this approach achieves an average correlation coefficient of 0.9 between the evaluated health index and the degradation of actual capacity, demonstrating its efficacy in capturing LIB health degradation. This research highlights the feasibility of training deep neural networks using unlabeled LIB data, offering cost-efficient means and unleashing the potential of the measured information.

Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to comprehensively model behavioral multi-modality. To better capture multimodal behavior in interactive scenarios, we propose modeling interactions as dynamic graphs, replacing traditional aggregation-based techniques that rely on vessel states. By leveraging the natural multimodal capabilities of diffusion models, we frame the trajectory prediction task as an inverse process of motion uncertainty diffusion, wherein uncertainties across potential navigational areas are progressively eliminated until the desired trajectories is produced.

Rob Gramlich is founder and president of Grid Strategies and was economic advisor to the chairman of the Federal Energy Regulatory Commission during the George W. Bush administration. US electricity consumption is rising faster than it has in decades. Accommodating that growth will require building wind turbines, solar farms, and other power plants faster than we ever have before--and expanding the network of wires needed to connect those facilities to the grid. But one major problem is that it's expensive and slow to secure permits for new transmission lines and build them across the country. Fortunately, there are some shortcuts that could expand the capacity of the existing system without requiring completely new infrastructure: a suite of hardware and software tools known as advanced transmission technologies (ATTs), which can increase both the capacity and the efficiency of the power sector.

Reinforcement learning (RL) has demonstrated the ability to maintain the plasticity of the policy throughout short-term training in aerial robot control. However, these policies have been shown to loss of plasticity when extended to long-term learning in non-stationary environments. For example, the standard proximal policy optimization (PPO) policy is observed to collapse in long-term training settings and lead to significant control performance degradation. To address this problem, this work proposes a cost-aware framework that uses a retrospective cost mechanism (RECOM) to balance rewards and losses in RL training with a non-stationary environment. Using a cost gradient relation between rewards and losses, our framework dynamically updates the learning rate to actively train the control policy in a disturbed wind environment. Our experimental results show that our framework learned a policy for the hovering task without policy collapse in variable wind conditions and has a successful result of 11.29% less dormant units than L2 regularization with PPO.

This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulation faces critical bottlenecks, including significant challenges in insufficient data and inefficient data acquisition, long-horizon and complex task planning, and the multi-modality reasoning ability for robust policy learning performance across diverse environments. To tackle these challenges, this survey introduces several generative model paradigms, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, probabilistic flow models, and autoregressive models, highlighting their strengths and limitations. The applications of these models are categorized into three hierarchical layers: the Foundation Layer, focusing on data generation and reward generation; the Intermediate Layer, covering language, code, visual, and state generation; and the Policy Layer, emphasizing grasp generation and trajectory generation. Each layer is explored in detail, along with notable works that have advanced the state of the art. Finally, the survey outlines future research directions and challenges, emphasizing the need for improved efficiency in data utilization, better handling of long-horizon tasks, and enhanced generalization across diverse robotic scenarios. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/GAI4Manipulation/AwesomeGAIManipulation

This study introduces a unified control framework that addresses the challenge of precise quadruped locomotion with unknown payloads, named as online payload identification-based physics-informed neural network predictive control (OPI-PINNPC). By integrating online payload identification with physics-informed neural networks (PINNs), our approach embeds identified mass parameters directly into the neural network's loss function, ensuring physical consistency while adapting to changing load conditions. The physics-constrained neural representation serves as an efficient surrogate model within our nonlinear model predictive controller, enabling real-time optimization despite the complex dynamics of legged locomotion. Experimental validation on our quadruped robot platform demonstrates 35% improvement in position and orientation tracking accuracy across diverse payload conditions (25-100 kg), with substantially faster convergence compared to previous adaptive control methods. Our framework provides a adaptive solution for maintaining locomotion performance under variable payload conditions without sacrificing computational efficiency.