Crossmodal matching in single-cell omics is essential for explaining biological regulatory mechanisms and enhancing downstream analyses. However, current single-cell crossmodal models often suffer from three limitations: sparse modality signals, underutilization of biological attributes, and insufficient modeling of regulatory interactions. These challenges hinder generalization in data-scarce settings and restrict the ability to uncover fine-grained biologically meaningful crossmodal relationships. Here, we present a novel framework which reformulates crossmodal matching as a graph classification task on Attributed Bipartite Graphs (ABGs). It models single-cell ATAC-RNA data as an ABG, where each expressed ATAC and RNA is treated as a distinct node with unique IDs and biological features. To model crossmodal interaction patterns on the constructed ABG, we propose Bi2Former, a biologically-driven bipartite graph transformer that learns interpretable attention over ATAC-RNA pairs. This design enables the model to effectively learn and explain biological regulatory relationships between ATAC and RNA modalities. Extensive experiments demonstrate that Bi2Former achieves state-of-the-art performance in crossmodal matching across diverse datasets, remains robust under sparse training data, generalizes to unseen cell types and datasets, and reveals biologically meaningful regulatory patterns.

Image quality assessment (IQA) aims to quantify the visual quality of digital images consistent with human perceptual judgments. Commonly, IQA models are classified into full-reference (FR) and noreference (NR) approaches [47], depending on the availability of pristine-quality reference images. In this paper, we focus on NR-IQA due to its practical relevance in real-world scenarios where reference images are unavailable. Over the decades, NR-IQA has evolved from knowledge-driven [33, 12] to data-driven approaches [30, 19, 54], and shifted from regression-based to ranking-based [58, 59] techniques. Nevertheless, achieving strong model generalization (e.g., generalization to unseen image distortions) remains a significant, unresolved challenge, driving recent research toward multi-dataset training [6], active fine-tuning [44], and continual model adaptation [57]. The rapid advancement of vision-language models (VLMs) offers promising avenues for enhancing NR-IQA generalization by contextualizing it into broader vision tasks [51]. VLMs can effectively integrate multi-modal information, enabling understanding of both low-level image distortions (e.g., noise and blur) and high-level perceptual attributes (e.g., aesthetics and content semantics). This multi-modal semantic contextualization allows VLMs to articulate nuanced quality descriptions with stronger generalization. However, current NR-IQA methods mainly leverage VLMs through supervised fine-tuning (SFT), which face several critical limitations [49, 56].

The AI era needs ever faster chips. ASML has a monopoly on the expensive contraptions needed to pattern them. Jos Benschop is climbing a ladder to get to the top of his newest machine. The contraption is the size of a double-decker bus--more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a futuristic V8 engine. When I reach the top with Benschop we're looking down from about 15 feet in the air, with bunny-suited technicians scurrying around below. It's more than 200 cubic meters of tech--"mechatronic devices that hold a few mirrors in a position with atomic precision," he says, gesturing at the gargantuan apparatus. Benschop, a tall and grizzled 66-year-old, has spent over a decade working with his engineers to design this thing, but even so, he'll sometimes look at it and go: Benschop is the executive vice president of technology for ASML, a Dutch company that is the linchpin of the microchip industry. If you want to make powerful chips to power phones or AI, a lithography machine like the one we're standing on is what you need to create increasingly tiny circuitry. Lithography is the art and science of shining light on a silicon wafer to pattern out the transistors, wiring, and other components of the microchips that will be cut from it. The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant. Nine years ago, ASML began selling machines that use a daring new way of patterning chip features.

Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained visual encoders are connected with pre-trained LLMs through continuous multimodal pre-training. However, the multimodal scaling property of this paradigm remains difficult to explore due to the separated training. In this paper, we focus on the native training of MLLMs in an end-to-end manner and systematically study its design space and scaling property under a practical setting, i.e., data constraint. Through careful study of various choices in MLLM, we obtain the optimal meta-architecture that best balances performance and training cost. After that, we further explore the scaling properties of the native MLLM and indicate the positively correlated scaling relationship between visual encoders and LLMs. Based on these findings, we propose a native MLLM called NaViL, combined with a simple and cost-effective recipe. Experimental results on 14 multimodal benchmarks confirm the competitive performance of NaViL against existing MLLMs. Besides that, our findings and results provide in-depth insights for the future study of native MLLMs.

We study the problem of entropy calibration, which asks whether a language model's entropy over generations matches its log loss on human text. Past work found that models are miscalibrated, with entropy per step increasing as generations grow longer, due to error accumulation. To calibrate the model and improve text quality, it has become standard practice to truncate the distribution, but this approach reduces output diversity, which we would like to avoid. Therefore, in this paper, we ask: does miscalibration improve automatically with scale, and if not, is it theoretically possible to calibrate without tradeoffs? To build intuition, we first study a simplified theoretical setting to characterize the scaling behavior of miscalibration with respect to dataset size. We find that the rate of scaling depends on the power law exponent of the data distribution -- in particular, for a power law exponent close to 1, the scaling exponent is close to 0, meaning that miscalibration improves very slowly with scale.

Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) from year 2022 to 2024: our TraffiDent dataset includes traffic, i.e., time-series indexes on traffic flow, lane occupancy, and average vehicle speed, and incident, whose records are spatiotemporally aligned with traffic data, with seven different incident classes. Additionally, each node includes detailed physical and policylevel meta-attributes of lanes. Previous datasets typically contain only traffic or incident data in isolation, limiting research to general forecasting tasks.

Multimodal Large Language Models (MLLMs) have achieved considerable accuracy in Optical Character Recognition (OCR) from static images. However, their efficacy in video OCR is significantly diminished due to factors such as motion blur, temporal variations, and visual effects inherent in video content. To provide clearer guidance for training practical MLLMs, we introduce MMEVideoOCR benchmark, which encompasses a comprehensive range of video OCR application scenarios.

Discrete Flow-based Models (DFMs) are powerful generative models for highquality discrete data but typically suffer from slow sampling speeds due to their reliance on iterative decoding processes. This reliance on a multi-step process originates from the factorization approximation of DFMs, which is necessary for handling high-dimensional data. In this paper, we analyze the factorization approximation error using Conditional Total Correlation (TC), and reveal its dependence on the coupling. To address the challenge of efficient few-step generation, we propose Rectified Discrete Flow (ReDi), a novel iterative method that reduces the underlying factorization error (measured as Conditional TC) by rectifying the coupling between source and target distributions. We theoretically prove that each ReDi step guarantees a monotonic decreasing Conditional TC, ensuring its convergence. Empirically, ReDi significantly reduces Conditional TC and enables few-step generation. Moreover, we demonstrate that the rectified couplings are well-suited for training efficient one-step models on image generation. ReDi offers a simple and theoretically grounded approach for tackling the few-step challenge, providing a new perspective on efficient discrete data synthesis.

The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1.97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome.

Uncertainty quantification (UQ) in scientific machine learning is increasingly critical as neural networks are widely adopted to tackle complex problems across diverse scientific disciplines. For physics-informed neural networks (PINNs), a prominent model in scientific machine learning, uncertainty is typically quantified using Bayesian or dropout methods. However, both approaches suffer from a fundamental limitation: the prior distribution or dropout rate required to construct honest confidence sets cannot be determined without additional information. In this paper, we propose a novel method within the framework of extended fiducial inference (EFI) to provide rigorous uncertainty quantification for PINNs. The proposed method leverages a narrow-neck hyper-network to learn the parameters of the PINN and quantify their uncertainty based on imputed random errors in the observations. This approach overcomes the limitations of Bayesian and dropout methods, enabling the construction of honest confidence sets based solely on observed data. This advancement represents a significant breakthrough for PINNs, greatly enhancing their reliability, interpretability, and applicability to real-world scientific and engineering challenges. Moreover, it establishes a new theoretical framework for EFI, extending its application to large-scale models, eliminating the need for sparse hyper-networks, and significantly improving the automaticity and robustness of statistical inference.