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Applications · Computer Vision

Yanqing Liu, Yingcheng Liu, Fanghong Dong, Budianto Budianto, Cihang Xie, Yan jiao

As video content creation shifts towards long-form narratives, retrieving and composing short clips into coherent storylines becomes a critical challenge. Standard retrieval formulations, however, perform context-agnostic retrieval, prioritizing local semantic alignment while neglecting procedural state and identity consistency across time. To address this, we introduce the task of Consistent Video Retrieval (CVR) and establish a rigorous benchmark across YouCook2, COIN, and CrossTask, designed to explicitly evaluate temporal and identity consistency. We propose CAST (Context-Aware State Transition), a lightweight, embedding-agnostic adapter that models procedural progression by predicting a state-conditioned residual update ($\Delta$) from visual history, decoupling procedural progression from static identity. Extensive experiments demonstrate that CAST yields significant and consistent gains across diverse datasets over standard baselines. Furthermore, we showcase its potential as a plug-and-play consistency verifier, guiding black-box generation models (e.g., Sora, Veo) toward physically plausible continuations.

Sandeep Suresh Cranganore, Andrei Bodnar, Gianluca Galletti, Fabian Paischer, Johannes Brandstetter

The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial differential equations (PDEs) have reached the petabyte-to-exabyte scale. Transient simulations modeling Navier-Stokes equations, magnetohydrodynamics, plasma physics, or binary black hole mergers generate data volumes that are prohibitive for modern high-performance computing (HPC) infrastructures. To address this bottleneck, we introduce ANTIC (Adaptive Neural Temporal in situ Compressor), an end-to-end in situ compression pipeline. ANTIC consists of an adaptive temporal selector tailored to high-dimensional physics that identifies and filters informative snapshots at simulation time, combined with a spatial neural compression module based on continual fine-tuning that learns residual updates between adjacent snapshots using neural fields. By operating in a single streaming pass, ANTIC enables a combined compression of temporal and spatial components and effectively alleviates the need for explicit on-disk storage of entire time-evolved trajectories. Experimental results demonstrate that ANTIC achieves storage reductions of approximately $\sim 400\times$ for 2D Kolmogorov flow simulations and $\sim 7000\times$ for large-scale physics simulations such as binary black hole mergers.

Applications · Chemistry, Physics, and Earth Sciences

Jiyeon Kim, Youngjoon Hong, Won-Yong Shin

Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super-resolution techniques aim to mitigate this cost by reconstructing high-resolution (HR), high-fidelity solutions from low-cost, low-resolution (LR) counterparts. However, training neural networks for super-resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data-efficient super-resolution framework for mesh-based simulations aided by message passing neural networks (MPNNs). As its core, SuperMeshNet introduces complementary learning, a semi-supervised approach that effectively leverages both 1) a small amount of paired LR-HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN-based models. Additionally, our model is enriched by inductive biases, which is empirically shown to further improve super-resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://anonymous.4open.science/r/SuperMeshNet/README.md.

Perry Dong, Kuo-Han Hung, Alexander Swerdlow, Dorsa Sadigh, Chelsea Finn

Despite scale driving substantial recent advancements in machine learning, reinforcement learning (RL) methods still primarily use small value functions. Naively scaling value functions -- including with a transformer architecture, which is known to be highly scalable -- often results in learning instability and worse performance. In this work, we ask what prevents transformers from scaling effectively for value functions? Through empirical analysis, we identify the critical failure mode in this scaling: attention scores collapse as capacity increases. Our key insight is that we can effectively prevent this collapse and stabilize training by controlling the entropy of the attention scores, thereby enabling the use of larger models. To this end, we propose Transformer Q-Learning (TQL), a method that unlocks the scaling potential of transformers in learning value functions in RL. Our approach yields up to a 43\% improvement in performance when scaling from the smallest to the largest network sizes, while prior methods suffer from performance degradation.

Applications · Chemistry, Physics, and Earth Sciences

Jiyeon Kim, Byungju Lee, Won-Yong Shin

Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning-based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non-autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non-autoregressive learning framework based on modality reduction, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables a predictor to learn dynamics without requiring sequential inference and to benefit from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non-autoregressive benchmarks across both types of datasets. Our code is available at https://anonymous.4open.science/r/2026.

Deep Learning · Generative Models and Autoencoders

Elio Moreau, Florentin Coeurdoux, Grégoire Ferré, Eric Vanden-Eijnden

Understanding the geometry of learned distributions is fundamental to improving and interpreting diffusion models, yet systematic tools for exploring their landscape remain limited. Standard latent-space interpolations fail to respect the structure of the learned distribution, often traversing low-density regions. We introduce a framework based on the string method that computes continuous paths between samples by evolving curves under the learned score function. Operating on pretrained models without retraining, our approach interpolates between three regimes: pure generative transport, which yields continuous sample paths; gradient-dominated dynamics, which recover minimum energy paths (MEPs); and finite-temperature string dynamics, which compute principal curves---self-consistent paths that balance energy and entropy. We demonstrate that the choice of regime matters in practice. For image diffusion models, MEPs contain high-likelihood but unrealistic ``cartoon'' images, confirming prior observations that likelihood maxima appear unrealistic; principal curves instead yield realistic morphing sequences despite lower likelihood. For protein structure prediction, our method computes transition pathways between metastable conformers directly from models trained on static structures, yielding paths with physically plausible intermediates. Together, these results establish the string method as a principled tool for probing the modal structure of diffusion models---identifying modes, characterizing barriers, and mapping connectivity in complex learned distributions.

Applications · Chemistry, Physics, and Earth Sciences

Dingling Yao, Andrea Polesello, Adeel Pervez, Caroline Muller, Francesco Locatello

While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making certain perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models may look correct without reasoning correctly—a discrepancy we term the *Perception–Physics Paradox*. To address this gap, we introduce *Scientific Alignment* as an implicit objective for representation learning in scientific domains. We study a principled, testable aspect of scientific alignment through *Structural Isomorphism*, which requires latent representations to uniquely identify physical systems up to a linear reparameterization. This perspective induces a hierarchy of necessary conditions and yields a systematic probing protocol for physical and causal interpretability. To operationalize this framework, we release *TC-Atlas*, a foundational global dataset and automated construction pipeline for tropical cyclone research, and show that current VFMs rely on visual shortcuts that collapse in extreme regimes, indicating that scientific alignment does not arise as a natural byproduct of visual scaling alone.

Applications · Computer Vision

Yuyang Yin, Hao-Xiang Guo, Fangfu Liu, Mengyu Wang, Hanwen Liang, Eric Li, Yikai Wang, Xiaojie Jin, Yao Zhao, Yunchao Wei

Achieving a complete and explorable 360-degree visual world is a cornerstone of immersive content creation. While recent advances in video generation have achieved impressive results, they follow a 2D paradigm that treats content generation as transitions of 2D pixels, lacking an intrinsic understanding of the physical 3D world, resulting in frequent geometric inconsistencies. To achieve an explorable and physical-consistent visual world, the generation process should shift to a 3D paradigm: the visual content is governed by the physical relationships of the entire 3D environment together with 3D motion signals. However, under this setting, the conventional modeling methods and control signals, such as spatial attention computation in a 2D space, become unsuitable and ineffective. To address this, we propose PanoWorld-X for explorable 3D scene video generation. Our framework is built on the panoramic representation, which naturally maps a 3D scene into a standard format and provides an ideal basis for consistency. Specifically, we first develop a data curation pipeline to produce high-quality and large-motion 3D scene evolution with movement trajectories. To achieve precise control, we design the Exploration Panoramic Plücker Embedding (PPE), a guidance signal tailored for 3D motion. Furthermore, leveraging the spherical geometric properties of panoramic data, we propose a sphere-aware attention mechanism, which can capture true geometric adjacency by reprojecting features onto a spherical surface. Extensive experiments demonstrate that PanoWorld-X achieves superior performance in motion range, control precision, and visual quality, underscoring its potential for real-world applications.

Applications · Neuroscience, Cognitive Science

Hyun-Jong Lee, Jae-Han Lim

Spiking Neural Networks (SNNs) are regarded as the third generation of neural networks, offering energy-efficient computing for neuromorphic devices. Despite this benefit, hardware-implemented SNNs are vulnerable to hardware faults, which severely degrade their performance. Previous approaches have required direct access to internal SNN circuits to modify weights or monitor internal states, limiting their practicality. Improving robustness to hardware faults without such access remains challenging. To overcome this challenge, we propose a fault-tolerant mechanism that operates only through input data control. The hardware faults reduce the usable learning capacity of SNNs, leading to a mismatch between the instantaneous input load and the degraded network dynamics. Our mechanism mitigates this mismatch by dividing each input sample into multiple fragments, redistributing the input load via a learnable fragmentation strategy. The strategy learns two key fragmentation components: 1) division boundaries and 2) the number of fragments. To our knowledge, this is the first to improve the fault tolerance of SNNs without accessing the internal SNN circuit. Experimental results demonstrate that our mechanism consistently outperforms previous methods in various SNN models, achieving these gains without direct access to internal circuits. Furthermore, we validate its effectiveness on SNNs implemented with a physical FPGA platform, confirming its practicality.

Deep Learning · Generative Models and Autoencoders

Bowen Xue, Giuseppe Guarnera, Shuang Zhao, Zahra Montazeri

Current video diffusion models generate visually compelling content but often violate basic laws of physics, producing subtle artifacts like rubber-sheet deformations and inconsistent object motion. We introduce a frequency-domain physics prior that improves motion plausibility without modifying model architectures. Our method decomposes common rigid motions (translation, rotation, scaling) into lightweight spectral losses computed on a low-frequency subset. Applied to Open-Sora, MVDIT, and Hunyuan, our approach improves both motion accuracy and action recognition by ~11\% on average on OpenVID-1M (relative), while maintaining visual quality. User studies show 74--83\% preference for our physics-enhanced videos. It also reduces warping error by 22--37\% (depending on the backbone) and improves temporal consistency scores. These results indicate that simple, global spectral cues are an effective drop-in regularizer for physically plausible motion in video diffusion.

Deep Learning · Generative Models and Autoencoders

Yingyan Hou, Xianchi Dong, Chao Ren, Wanxuan Lu, Zihan Wei, Hongfeng Yu, Yixiao Wang, Yaning Zhou

Object insertion has emerged as a promising augmentation paradigm to solve the label scarcity and long-tail distributions in remote sensing. It aims to generate training samples by synthesizing target instances onto real backgrounds. However, existing methods have three critical issues: (i) Semantic placement inconsistency, (ii) Radiometric inconsistency with illumination and atmospheric conditions, and (iii) Textural discontinuity. To cope with these issues, we propose a physics-aware method, called "Plan, Decouple, Assimilate" (PDA), for generating high-fidelity training samples. In the planning stage, the Planning (P) module automatically generates geometrically bounding boxes. In the generation stage, we design a dual-module model to generate the target instance: the Decoupling (D) module employs Asymmetric Spectral Adaptation Decoupling to disentangle structural identity from environmental illumination, while the Assimilation (A) module utilizes Neighborhood-Aware Texture Assimilation to harmonize the local manifold. By strategically integrating these modules, PDA enforces multi-level consistency spanning global geometry to local micro-textures. Extensive experiments verify that PDA consistently outperforms existing state-of-the-art methods in generative quality, reducing whole-image FID by 15.7%, and substantially improves downstream detection performance, boosting average mAP50 by 15.9% over the real-data baseline.

Applications · Chemistry, Physics, and Earth Sciences

Michael Aich, Andreas Fürst, Florian Sestak, Carlos Ruiz-Gonzalez, Niklas Boers, Johannes Brandstetter

Deep learning has revolutionized weather and climate modeling, yet the current landscape remains fragmented: highly specialized models are typically trained individually for distinct tasks. To unify this landscape, we introduce WIND, a single pre-trained foundation model capable of replacing specialized baselines across a vast array of tasks. Crucially, in contrast to previous atmospheric foundation models, we achieve this without any task-specific fine-tuning. To learn a robust, task-agnostic prior of the atmosphere, we pre-train WIND with a self-supervised video reconstruction objective, utilizing an unconditional video diffusion model to iteratively reconstruct atmospheric dynamics from a noisy state. At inference, we frame diverse domain-specific problems strictly as inverse problems and solve them via posterior sampling. This unified approach allows us to tackle highly relevant weather and climate problems, including probabilistic forecasting, spatial and temporal downscaling, sparse reconstruction and enforcing conservation laws purely with our pre-trained model. We further demonstrate the model's capacity to generate physically consistent counterfactual storylines of extreme weather events under global warming scenarios. By combining generative video modeling with inverse problem solving, WIND offers a computationally efficient paradigm shift in AI-based atmospheric modeling.

Applications · Chemistry, Physics, and Earth Sciences

Dongzhe Zheng, Tao Zhong, Christine Allen-Blanchette

In this paper, we study solution operators of physical field equations on geometric meshes from a function-space perspective. We reveal that Hodge orthogonality fundamentally resolves spectral interference by isolating unlearnable topological degrees of freedom from learnable geometric dynamics, enabling an additive approximation confined to structure-preserving subspaces. Building on Hodge theory and operator splitting, we derive a principled operator-level decomposition. The result is a Hybrid Eulerian-Lagrangian architecture with an algebraic-level inductive bias we call Hodge Spectral Duality (HSD). In our framework, we use discrete differential forms to capture topology-dominated components and an orthogonal auxiliary ambient space to represent complex local dynamics. Our method achieves superior accuracy and efficiency on geometric graphs with enhanced fidelity to physical invariants.

Haotian Chi, Zeyu Feng, Xingrui Yu, Linbo Luo, Yew Soon ONG, Ivor Tsang, Hechang Chen, Yi Chang, Haiyan Yin

Planning collaboration strategies for multi-agent embodied systems remains a core challenge for LLM-based planners, which often fail to capture the physical and coordination constraints of realworld environments. To address this, we present EvoCF, an agentic memory-driven evolutionary counterfactual planning framework for discovering improved multi-agent collaboration strategies through counterfactual plan generation and evaluation. First, we propose a symbolic constraint inductor that induces reusable symbolic constraints from failures, forming an evolving rule library. Then, we propose an evolutionary counterfactual plan generator that systematically explores semantically consistent plan variants through rule-conditioned mutations, enabling robust collaboration strategies beyond short-sighted one-shot LLM plans. Finally, we design an agentic memory-grounded evaluator that ranks candidate plans using retrieval-augmented evidence, producing interpretable, constraint-aware selections. Across multi-agent embodied simulation benchmarks, EvoCF consistently discovers more robust and executable plans compared to baseline approaches. Our results demonstrate that grounding multi-agent planning in agentic memory and counterfactual reasoning significantly enhances both effectiveness and robustness.

Applications · Everything Else

Yanfang Liu, Mingjun Wang, Peng XU, Rongliang Fu, Bei Yu, Tsung-Yi Ho

Analog circuits constitute the indispensable interface between physical reality and digital computation, underpinning safety-critical systems from autonomous driving to medical implants. Consequently, verification correctness is paramount; yet, it remains the critical bottleneck in hardware design, consuming over 50\% of engineering cycles due to a heavy reliance on the manual interpretation of unstructured, heterogeneous specifications. While Large Language Models (LLMs) offer automation potential, their probabilistic, autoregressive nature is structurally misaligned with the strict determinism required for analog verification tasks. Specifically, generic LLMs struggle to resolve semantic dispersion, latent causal dependencies, and numerical precision. To bridge this gap, we introduce AnalogVerifier, a neuro-symbolic framework that automates end-to-end testbench generation by decoupling semantic translation from logical enforcement. We propose a four-stage architecture: (1) Context-Aware Task Serialization transforms complex specifications into atomic tasks via an agentic workflow; (2) Graph-Symbolic Scheduling satisfies analog design constraints through Port Dependency Graphs (PDG) for correct-by-construction sequencing; (3) Numerical-Symbolic Grounding mitigates numerical hallucination by delegating threshold derivation to a deterministic symbolic oracle; (4) Closed-Loop Repair enables correctness and completeness of the generated testbenches by simulation feedback. Evaluation on five industrial analog circuits demonstrates that AnalogVerifier achieves 82.3\%--100\% functional pass rate, establishing a new paradigm for reliable, automated analog verification. The code and data are publicly available at \url{https://anonymous.4open.science/r/ICML26--AnalogVerifier-72EE/}.

Deep Learning · Sequential Models, Time series

Zinuo You, Jin Zheng, John Cartlidge

Irregular multivariate time series pose a fundamental trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often rely on sequential numerical solvers that are prone to drift. To bridge this gap, we present the Latent Laplace Diffusion (LLapDiff), a generative framework that models the target as a low-dimensional latent trajectory, enabling horizon-wide generation without step-by-step integration over physical time. We guide the reverse process using a stable modal parameterization motivated by stochastic port-Hamiltonian dynamics, and parameterize its mean evolution in the Laplace domain via learnable complex-conjugate poles, allowing for direct evaluation over irregular timestamps. Moreover, we link continuous dynamics to irregular observations through renewal-averaging analysis, which maps sampling gaps to effective event-domain poles and theoretically motivates a gap-aware history summarizer for conditioning. Extensive experiments demonstrate that LLapDiff consistently outperforms baselines in long-horizon forecasting, and its continuous-time generative nature also supports missing-value imputation by querying the same model at historical timestamps.

Applications · Chemistry, Physics, and Earth Sciences

Daria Ledneva, Mikhail Nuridinov, Denis Kuznetsov

Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.

Applications · Chemistry, Physics, and Earth Sciences

Daria Ledneva, Denis Kuznetsov

Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as $k$-mers or BPE, which impose arbitrary sequence boundaries and may obscure biologically relevant structure. Although recent work has demonstrated the feasibility of adaptive hierarchical tokenization in autoregressive settings, its extension to masked language modeling and systematic downstream evaluation remain underexplored. We present \textbf{LDARNet}, a 120M-parameter hierarchical genomic foundation model that integrates learnable sequence compression into the masked language modeling paradigm by combining BiMamba-2 state-space layers with selective attention and ratio-based regularization to induce stable token boundaries without supervision. We evaluate LDARNet by fine-tuning on 27 tasks from the Genomics Benchmarks and Nucleotide Transformer suites, covering regulatory, epigenetic, and sequence-level prediction problems, and compare it against state-of-the-art models ranging from 8M to 2.5B parameters. LDARNet achieves 11 out of 18 wins among models under 300M parameters and attains state-of-the-art performance on 5 histone modification tasks, outperforming even substantially larger models on several long-range epigenetic benchmarks. These results indicate that adaptive hierarchical tokenization under masked language modeling can capture long-range genomic dependencies relevant to regulatory biology and highlight learnable compression as a promising direction for efficient and scalable genomic foundation models.

Applications · Health / Medicine

Shaoqing Duan, Haofei Song, Xintian Mao, Qingli Li, Yan Wang

Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induced by a position-dependent integral imaging process. Existing deep learning methods, constrained by shift-invariance assumptions and limited interpretability, are not well suited to such heterogeneous blur patterns. Neural operators provide a principled alternative by modeling defocus formation directly as an integral operator, offering a new perspective on defocus deblurring. However, most existing neural operator architectures for low-level vision rely on globally parameterized kernels that assume smoothness and stationarity, limiting their ability to model heterogeneous and locally discontinuous blur patterns. To address this limitation, we propose the Discontinuous Galerkin Neural Operator (DGNO), which parameterizes the integral kernel using a discontinuous Galerkin formulation with element-local volume operators and interface numerical fluxes. DGNO provides a principled combination of locality, heterogeneity modeling, and global coherence while preserving the underlying physics of optical image formation. Extensive experiments demonstrate that DGNO surpasses state-of-the-art methods, delivering sharper reconstructions, robust handling of spatially varying blur, and scalable high-resolution performance.

Applications · Computer Vision

Yuan Zeng, Yujia Shi, Tiao Tan, Xingting Li, Yaqi Qin, Zongqing Lu, Wenming Yang, Jing-Hao Xue, Qingmin Liao

Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively hallucinates plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is at https://egotactile.github.io/.