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Applications · Chemistry, Physics, and Earth Sciences

ZHIRAN HOU, TINGHUAI MA, Huan Rong, Li Jia, Anouar Imel, Heng Zhang, Ming Li

Molecular property prediction from 3D structures is fundamentally constrained by the scarcity of labeled data. To address this challenge, researchers have adapted various self-supervised pre-training methods from computer vision and natural language processing; however, these approaches often neglect the fundamental physical principles unique to molecular systems. When grounded in physical principles, denoising pre-training can be formally shown to be equivalent to learning molecular force fields.However, existing methods uniformly apply a uniform noise scheme across all molecules, which introduces systematic bias in molecular distribution modeling. To overcome this limitation, we propose MOES-Pred, a novel denoising pre-training framework that employs an energy sentinel mechanism to dynamically adjust molecule-specific noise perturbations. By incorporating chemical prior knowledge, we design molecule-specific noising strategies that expand conformational sampling coverage and improve the fidelity of molecular distribution modeling. Extensive experiments demonstrate that MOES-Pred consistently surpasses existing methods, achieving state-of-the-art performance on both force prediction tasks and downstream quantum chemical property predictions.

Yifei Li, Haixu Wu, Zeyi Xu, Tuur Stuyck, Wojciech Matusik

Learning-based methods have made significant progress in physics simulation, typically approximating dynamics with a monolithic end-to-end optimized neural network. Although these models offer an effective way to simulation, they may lose essential features compared to traditional numerical simulators, such as physical interpretability and reliability. Drawing inspiration from classical simulators that operate in a modular fashion, this paper presents Neural Modular Physics (NMP) for elastic simulation, which combines the approximation capacity of neural networks with the physical reliability of traditional simulators. Beyond the previous monolithic learning paradigm, NMP enables direct supervision of intermediate quantities and physical constraints by decomposing elastic dynamics into physically meaningful neural modules connected through intermediate physical quantities. With a specialized architecture and training strategy, our method transforms the numerical computation flow into a modular neural simulator, achieving improved physical consistency and generalizability. Experimentally, NMP demonstrates superior generalization to unseen initial conditions and resolutions, stable long-horizon simulation, better preservation of physical properties compared to other neural simulators, and greater feasibility in scenarios with unknown underlying dynamics than traditional simulators.

Yuqi Jiang, Yumeng Liu, Zimu Li, Jinyuan Deng, Qian Jin, Yucheng Cui, YU LI, Xunzhao Yin, Qi Sun, Cheng Zhuo

As semiconductor technology nodes continue to shrink, computational lithography has become critical to yield and performance. However, real-world lithography is a continuous, multi-stage physical process driven by implicit interventions, which cannot be captured by the existing static or stage-wise models. To address these issues, we present \textbf{LithoDreamer}, the first physics-informed World Model (WM) framework for computational lithography, designed to represent the ``Layout-Mask-Resist Image-After Development Image (ADI)'' pipeline as a decision-driven multi-stage physical evolution system, enabling multi-step latent state rollouts within stages and intervention-aware decision-making across stages. First, we learn the feature variations between adjacent states in latent spaces to capture the physical dynamics of each stage. Second, the model plans continuous process interventions through physical mappings in the spaces, which in turn drive subsequent state transitions. Furthermore, we propose a contrastive variational optimization paradigm that jointly explores the evolutions of the interventions and states without discrete action supervision, enabling stable and continuous process rollouts in the WM. Extensive experiments show that LithoDreamer achieves state-of-the-art accuracy and generalization performance.

Deep Learning · Robustness

Tianyi Xu, Cheng'an Wei, Yue Zhao, Kai Chen

Automatic Speech Recognition (ASR) systems, such as those in intelligent assistants, are vulnerable to adversarial examples (AEs). Benign audio clips like music, when embedded with small perturbations, can trick ASR models into recognizing attacker-specified commands. Prior studies focus on minimizing perturbation magnitude to craft AEs. However, they fails to achieve high attack stealthiness against black-box ASR systems in the physical world. In this paper, we introduce the first music carrier selection algorithm and an attention-aware stealthiness loss function to generate stealthy AEs. Extensive evaluations on five commercial ASR APIs and three widely-used voice assistants demonstrate that our method significantly outperforms state-of-the-art techniques in both effectiveness and stealthiness. Notably, in a user study involving 200 participants, 55.6\% of participants perceived our physical adversarial examples as benign audio, which is an improvement of over 20\% compared to existing methods.

Social Aspects · Safety

Jinjie Shen, Yaxiong Wang, Yujiao Wu, Lechao Cheng, Tianrui Hui, Nan Pu, Zhihui Li, Zhun Zhong

The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability. Existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to emerging manipulation types. We observed that the essence of manipulated misinformation lies in its intrinsic conflicts, i.e., semantic or physical inconsistencies either across modalities or with common world knowledge. Inspired by this observation, we propose Conflict-{Oriented REasoning (CORE) framework, an effective paradigm that learns to endows multimodal large language models (MLLMs) with explicit conflict-capturing capability. To this end, CORE first constructs the Conflict Attribution Corpus (CAC) with fine-grained annotations of conflict factors and sources, providing essential data support for subsequent conflict perception training. By performing conflict-oriented representation enhancement and reasoning based on CAC, CORE achieves robust and generalizable conflict detection, effectively and rapidly adapting to unseen manipulation types with a few samples or in even zero-shot settings. Extensive experiments demonstrate that CORE surpasses state-of-the-art models by 9.7\%, 14.1\%, and 11.8\% in accuracy on the DGM$^4$, MMFakeBench, and MDSM benchmarks, respectively.

Applications · Computer Vision

Yinyan Bu, Jiajie Yu, Xingyu Chen, Bo Wen, Xinyu Zhang, Piya Pal

Wireless channel modeling is essential for the design, analysis, and optimization of modern wireless sensing and communication systems. However, accurately modeling wireless channels in electrically large and complex environments remains a long-standing challenge, owing to the intricate interactions between radio-frequency (RF) signals and surrounding objects (e.g., reflection, diffraction, and scattering). Unlike conventional ray-tracing pipelines that rely on hand-engineer interaction rules, or black-box neural surrogates that do not explicitly model physical structure, we introduce SNRFT, a novel framework that integrates neural representations with physics-based RF propagation modeling. Our key idea is to view RF transport as a stochastic propagation process, from which a material-dependent attenuation coefficient emerges naturally as the rate parameter governing transport dynamics. This formulation inherently satisfies key physical constraints such as reciprocity and reversibility. Building on this foundation, we employ implicit neural representations to capture complex RF-object interactions while preserving the composability of traditional ray tracing. Extensive evaluations on real-world wireless communication and sensing testbeds demonstrate that SNRFT consistently outperforms existing methods, while requiring significantly fewer training samples.

Applications · Chemistry, Physics, and Earth Sciences

Weichen Qin, Yufan Xie, Peihao Wang, Chia-Jui Chou, Minghui Du, Peng Xu, Ziren Luo, Yi Yang, Jingyi Yu, Bo Liang 等

Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference. However, existing methods struggle with effective multimodal modeling. They often rely on brute-force fusion strategies that ignore the structural disparities between parameters and observations, thus limiting estimation fidelity. In this work, we introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation). Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction. Additionally, we propose an FK-steered sampling strategy that leverages intermediate observation likelihoods to guide the generative trajectories, effectively improving the sample quality during inference. Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC. Furthermore, in a real-world exoplanet orbital estimation task, FUSE successfully resolves complex parameter degeneracies that challenge existing methods, highlighting its potential to accelerate complex scientific discoveries in astrophysics and beyond.

Deep Learning · Algorithms

Kaiqi Lin, Jianping Luo

While LLM-driven Neural Architecture Search (NAS) leverages exceptional code generation and reasoning, it suffers from a critical "Semantic-Physical Misalignment": LLMs prioritize high-level semantic plausibility but are agnostic to intrinsic physical dynamics such as gradient flow, whereas Zero-Cost Proxies (ZCPs) capture these local sensitivities but lack global semantic planning. To bridge this gap, we propose SAGE-NAS, a closed-loop evolutionary framework that synergizes an LLM-Based Semantic Agent with a Graph-Based Evaluator. Specifically, SAGE-NAS coordinates an LLM-driven Semantic Agent to construct candidate architectures by dynamically scheduling complementary sub-policies that balance exploitation with exploration. Furthermore, the framework integrates a Dual-Modality Graph Evaluator that serves as a rapid performance predictor by fusing ZCP statistics with topological features, and a State-Aware Behavioral Atlas that guides sparsity-driven exploration to escape local optima. Experiments demonstrate that SAGE-NAS achieves state-of-the-art performance across multiple mainstream search spaces and downstream tasks, exhibiting a superior balance between search efficiency, model accuracy, and cross-task generalization capability.

Applications · Computer Vision

Chenxu Wang, Yuxuan Li, Yunheng Li, Xiang Li, Jingyuan Xia, Qibin Hou

Existing language-image pre-training for remote sensing object detection is constrained by Monolithic Label Learning, which relies on exhaustively enumerating open-set categories via black-box data to acquire fine-grained representations, creating a dependency incompatible with the domain's inherent data scarcity. To transcend this bottleneck, we propose SLIP-RS, establishing a Structured-Attribute Decoupling Paradigm that maps the open-ended category space into a finite, physically meaningful attribute space, unlocking fine-grained discriminability via explicit structural logic. This paradigm is realized via two technical pillars: (1) Structured-Attribute Contrastive Learning, which enforces the learning of decoupled intrinsic visual logic via combinatorial attribute augmentation; and (2) Conformal Attribute Reliability Engine, which leverages conformal prediction theory to rigorously distill high-fidelity supervision from noisy sources, yielding RS-Attribute-15M, the largest dataset with over 15 million attribute annotations. Extensive experiments demonstrate that SLIP-RS establishes unprecedented performance in fine-grained detection and cross-domain generalization, validating structured attributes as a vital foundation for scalable remote sensing models.

Applications · Chemistry, Physics, and Earth Sciences

Zeyu Wang, Xin Zheng, Yao Lu, Shanqing Yu, Qi Xuan, Shirui Pan

Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain. Despite the promising performance of existing methods, especially in the context-aware methods, they still face two-fold severe challenges with \textit{insufficient structural context modeling} \& \textit{redundant auxiliary context learning}, leading to inadequate context graph exploration and ineffective information utilization for effective molecule representation learning. To address these, in this paper, we propose a novel framework by learning on \textbf{\underline{Re}}lational and \textbf{\underline{C}}ompact c\textbf{\underline{o}}ntext \textbf{\underline{G}}raph, named \textbf{\method}, to comprehensively exploit the context graph for expressive molecular property prediction. Specifically, the proposed \method contains two core modules: a \textbf{(1) cross-property relational learning module} to better model the structural and relational context information, and a \textbf{(2) context graph information bottleneck module} to adaptively suppress irrelevant auxiliary signals for compact context information utilization, followed by a detailed theoretical demonstration regarding the importance of joint relational and compact knowledge extraction in context graphs. Extensive experiments across multiple datasets demonstrate that \method consistently outperforms state-of-the-art methods, validating its superiority. Code is available at~\url{https://anonymous.4open.science/r/ReCoG-main-40C7/}.

Applications · Chemistry, Physics, and Earth Sciences

Hongyu Wang, Weijian Liu, Mingzhen Li, Hongtao Xu, Yan Wang, Weile Jia, Guangming Tan

Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy MD simulations, and their models exhibit scaling-law trends similar to large language models. However, the lack of scalable and efficient distributed training systems for conservative MLIPs makes them difficult to scale. This is because conservative MLIPs inherently follow a double-backward execution pattern, which involves computing gradients during the forward pass. This pattern creates a mismatch with existing distributed training systems, especially for pipeline parallelism. Therefore, we present JanusPipe, an efficient 3D-parallel (PP/DP/GP) training system tailored for conservative MLIPs. It integrates SymFold to enable memory-efficient pipeline parallelism for conservative MLIPs, and WaveK to reduce pipeline bubbles by balancing the four-phase compute time. Experimental results on 32 GPUs show that JanusPipe improves throughput by $1.51\times$ and $1.45\times$ on average over 1F1B and Hanayo, respectively.

Applications · Chemistry, Physics, and Earth Sciences

Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt

Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP by 55% on average across phonon thermodynamic properties and achieves state-of-the-art accuracy among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.

Applications · Energy

Siqi Ding, Zitong Zhang, LiXingYu, Shi Guoyang, Xiang Gu, Y.N.Xu, Huasheng Xie, Hanyue Zhao, YUEJIANG SHI, tianyuan liu

Realizing the symbiotic potential of AI and fusion energy requires bridging a critical "sim-to-real" gap. Models trained on simulations must generalize reliably under distribution shifts in safety-critical workflows. Focusing on the strongly nonlinear Grad-Shafranov equation (GSE) for tokamak equilibria, we propose a physics-anchored operator learning framework. Through systematic benchmarking across neural operator instantiations, we identify the Transformer-KAN Neural Operator (TKNO) as the state-of-the-art performer. Crucially, we adopt a semi-supervised paradigm that synergizes sparse data supervision with physical loss constraints. This approach significantly mitigates the catastrophic collapse often observed in purely data-driven methods under boundary-shape distribution shifts, thereby ensuring robust extrapolation. Validated on experimental discharge data from the EXL-50U tokamak, the model achieves high-fidelity equilibrium prediction (RMSE < 1.3%) with millisecond-level inference. These results demonstrate that AI has the potential to significantly accelerate fusion research and development.

Reinforcement Learning · Deep RL

Shaojun Xu, Xiaoling Zhou, Yihan Lin, Yapeng Meng, Xinglong Ji, Luping Shi, Rong Zhao

Model-Based Reinforcement Learning (MBRL) leverages latent imagination for sample efficiency, yet remains constrained by **Historical Tethering**: imagination is typically initialized from observed states. This creates a learning asymmetry, where the world model’s manifold discovery outpaces the policy's sparse-reward optimization. We propose **Mind Dreamer (MD)**, a framework that operationalizes **Active Counterfactual Reasoning (ACR)** to transcend Markovian continuity. MD reformulates discovery as the minimization of a global Relay Manifold Expected Free Energy (R-EFE); by invoking a latent-space $do$-operator, MD utilizes an adversarial generator to synthesize non-continuous **latent jumps** to epistemic blind spots that are physically plausible yet cognitively challenging. To resolve the credit assignment paradox across these spatial ruptures, we derive the **Relay Value Function (RVF)** and **Relay Uncertainty Function (RUF)**. These potentials treat synthesized anchors as latent bridges, propagating pragmatic and epistemic value through a principled Bellman-style formulation. Notably, we prove that the Uncertainty propagation across discontinuities necessitates a quadratic discount $\gamma^2$, establishing a formal epistemic horizon. Theoretically, MD acts as an optimal importance sampler that expands the manifold's spectral gap, reducing the hitting time to critical bottleneck states. Empirically, MD achieves a **1.67$\times$ average speedup** over DreamerV3 on DeepMind Control Suite, reaching **8.8$\times$** in sparse-reward tasks.

Deep Learning · Theory

Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf, Samuel Tovey, Christian Holm

Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute cost, and performance. While these laws are applied to improve the performance of modern foundation models, the mechanisms underpinning them are less understood, in part due to the absence of scalable analysis tools. To this end, we introduce a framework for efficiently measuring the alignment between the empirical neural tangent kernel (eNTK) and loss residuals. Applying this framework to scaling experiments reveals a consistent pattern: larger and better-performing models exhibit *lower* kernel alignment throughout training. We interpret this *unalignment* through the lens of *spectral reach*: the capacity of a model to learn from progressively weaker spectral modes in its eNTK. This interpretation allows us to explain why larger models achieve lower losses: they sustain learning on weaker signals that smaller models cannot access. We further demonstrate that feature learning improves spectral reach and provide a mechanistic explanation of how this occurs, suggesting practical avenues for performance improvement.

Xiaoda Yang, Majun Zhang, Changhao Pan, Nick Huang, Yang Yuguang, fan zhuo, Pengfei Zhou, Jin Zhou, Sizhe Shan, Shan Yang 等

Unified audio--visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive media. However, when moving from general audio--video synthesis to music–dance co-generation, the task becomes substantially harder: musical rhythm, phrasing, and accents must drive choreographic motion at fine temporal resolution, and such rhythmic coupling is not captured by unimodal metrics or generic audiovisual consistency scores used in current evaluation practice. We introduce TMD-Bench, a benchmark for text-driven music–dance co-generation that assesses systems across unimodal generation quality, instruction adherence, and cross-modal rhythmic alignment. The benchmark integrates computable physical metrics with perceptual multimodal judgments, and is supported by a curated rhythm-aligned music–dance dataset and a fine-grained Music Captioner for structured music semantics. TMD-Bench further reveals that (i) modern commercial audio--visual models (e.g., Veo 3, Sora 2) produce high-quality music and video, while rhythmic coupling remains less consistently optimized and leaves room for improvement, and (ii) our unified baseline RhyJAM trained on rhythm-aligned data achieves competitive beat-level synchronization while maintaining competitive unimodal fidelity. This presents prospects for building next-generation music–dance models that explicitly optimize rhythmic and kinetic coherence.

Applications · Chemistry, Physics, and Earth Sciences

Hongfei Wu, Lijun Wu, Guoqing Liu, Zhirong Liu, Bin Shao, Zun Wang

In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an extension of traditional graphs; they are pivotal for modeling high-order relationships, a capability that conventional equivariant graph-based methods lack due to their inherent limitations in representing intricate many-body interactions. To achieve this, we first construct hypergraphs by proposing a new fragmentation method that considers both chemical and three-dimensional spatial information of the molecular system. We then design SE3Set, which incorporates equivariance into the hypergraph neural network. This ensures that the learned molecular representations are invariant to spatial transformations, thereby providing robustness essential for the accurate prediction of molecular properties. SE3Set has shown performance on par with state-of-the-art (SOTA) models for small molecule datasets like QM9 and MD17. It demonstrates outstanding performance on the MD22 dataset, achieving a remarkable ~20\% improvement in accuracy across all molecules. Furthermore, on the OE62 dataset, SE3Set outperforms all short-range models. We also conducted a detailed analysis of OE62, highlighting the prevalence of complex many-body interactions in large molecules. This exceptional performance of SE3Set across diverse molecular structures underscores its transformative potential in computational chemistry, offering a route to more accurate and physically nuanced modeling. The code of this work is available at https://github.com/Navantock/SE3Set.

Deep Learning · Everything Else

Antonios Tragoudaras, Daniil Cherniavskii, Chenyu Zhang, Antonis Vozikis, Thijmen Nijdam, Derck Prinzhorn, Márk Bodrácska, Nicu Sebe, Andrii Zadaianchuk, Efstratios Gavves

Recent advances in image and video generation raise hopes that these models possess world modeling capabilities—the ability to generate realistic, physically plausible videos. This could revolutionize applications in robotics, autonomous driving, and scientific simulation. However, before treating these models as world models, we must ask: Do they adhere to physical laws? Current evaluation methods rely on subjective judgments or trajectory matching, limiting their usage for physical reasoning estimation, where many generations could be physically plausible. Thus, we introduce **Morpheus**, one of the first physics-informed evaluation frameworks for measuring the ability of video generation models to comprehend Newtonian dynamics. **Morpheus** features 130 real-world videos capturing physical phenomena, guided by conservation laws. Using those as conditioning for video generation, we assess physical plausibility leveraging interpretable metrics evaluated with respect to infallible conservation laws known per physical setting, leveraging advances in physics-informed neural networks and vision-language foundation models. Importantly, **Morpheus** targets controlled Newtonian rigid-body settings to enable quantitative checks. Our findings reveal that even with advanced prompting and video conditioning, contemporary models struggle to encode physical principles despite generating aesthetically pleasing videos.

General Machine Learning · Supervised Learning

Zihan Yu, Guanren Wang, Jingtao Ding, Huandong Wang, Yong Li

Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is insufficient: existing algorithms often exploit this metric to discover accurate and compact but structurally irrational formulas that are numerically ill-conditioned and physically inexplicable. Inspired by the structural stability of real physical laws, we propose the Effective Information Criterion (EIC) to quantify formula rationality. EIC models formulas as information channels and measures the amplification of inherent rounding noise during recursive calculation, effectively distinguishing physically plausible structures from pathological ones without relying on ground truth. Our analysis reveals a stark structural stability gap between human-derived equations and SR-discovered results. By integrating EIC into SR workflows, we provide explicit structural guidance: for heuristic search, EIC steers algorithms toward stable regions to yield superior Pareto frontiers; for generative models, EIC-based filtering improves pre-training sample efficiency by 2–4 times and boosts generalization $R^2$ by 22.4\%. Finally, an extensive study with 108 human experts shows that EIC aligns with human preferences in 70\% of cases, validating structural stability as a critical prerequisite for human-perceived interpretability. We release our code at https://anonymous.4open.science/r/EIC-91B2.

Applications · Chemistry, Physics, and Earth Sciences

Ruikun Li, Huandong Wang, Jingtao Ding, Yuan Yuan, Qingmin Liao, Yong Li

Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce applications. We propose DynaDiff, a generative meta-learning framework that transitions the paradigm from gradient-based tuning or modulation to direct weight-space generation. Specifically, we first abstract expert weights as novel weight graphs, utilizing multi-head attention to explicitly capture topological coupling within weights. Subsequently, we design a functional loss to ensure that the generated models achieve consistency with expert models in physical behavior. Finally, we develop a dynamics-informed prompter that extracts cross-domain physical and spectral features from observation sequences to condition the diffusion model. Experiments demonstrate that DynaDiff boosts average prediction accuracy by 10.78\% over competitive baselines. Furthermore, by pre-constructing a model zoo of expert predictors, we amortize the fine-tuning overhead into a one-time offline cost, significantly boosting deployment efficiency in new environments.