Vision-Language Models (VLMs) have achieved remarkable success in video understanding tasks. Yet, a key question remains: do they comprehend visual information, or merely learn superficial mappings between visual and textual patterns? Understanding visual cues, particularly those related to physics and common sense, is crucial for AI systems interacting with the physical world. However, existing VLM evaluations primarily rely on positivecontrol tests using real-world videos that resemble training distributions. While VLMs perform well on such benchmarks, it is unclear whether they grasp underlying visual and contextual signals or simply exploit visual-language correlations. To fill this gap, we propose incorporating negative-control tests, i.e., videos depicting physically impossible or logically inconsistent scenarios, and evaluating whether models can recognize these violations.

Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual descriptions, LLMs can still effectively answer related questions. This raises a fundamental question: How can a decoder-only Transformer architecture understand underlying graph structures? To address this, we start with the substructure extraction task, interpreting the inner mechanisms inside the transformers and analyzing the impact of the input queries.

We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the AstroDSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics.

Our evaluation and analysis are conducted mainly on the group of models listed in Table 2 in the13 main paper. We have chosen models such that they cover most of the popular and best-performing14 methods used by recent multimodal understanding work. In this part, we discuss all the models we15 have used in our experiments and explain their evaluation details, the public checkpoints we have16 chosen, and display the prompts we used to adapt the model to our datasets.17 During evaluation, we chose to follow the standard prompt provided by the authors whenever possi-18 ble for multiple-choice and short-answer questions. When the prompt is not provided for the model,19 we select a custom prompt that is created through several iterations of prompt engineering to select20 the one that produces the most effective results. The images are always included as the prefix.21 We used three proprietary models in our evaluation: GPT-o4-mini [1], Gem-22 ini 1.5 Pro [9], and Claude 3 Haiku [10]. Below we note the model API version used for evaluation.23 GPT-o4-mini: May 13-15, 2025.24 Cambrian-1 is a recent state-of-the-art model that excels at visual-centric tasks.27 This model explores combinations of vision encoders, text and image integration techniques, and28 instruction tuning strategies. We use the official implementation and checkpoint1 with a LLaMA3-29 8B-Instruct LLM backbone model in our evaluation.30 InternVL scales up the vision foundation model while aligning it with the back-31 bone LLM, and is trained on web-scale image-text data to achieve strong performance across a vari-32 ety of vision-centric tasks. We use the official implementation and checkpoint2 with the InternViT-33 300M-448px vision backbone and Internlm2.5-7B-chat LLaMA-3.2 is the first collection of multimodal large language model from the35 LLaMA family that was previously text-only. The integration of vision involves utilizing cross-36 attention layers and a pre-trained vision encoder that feeds directly into the text-processor. The37 model follows a commonly used training recipe that includes pretraining on noisy image-text pairs38 and then high-quality knowledge enhanced pairs. Notably, the language-model parameters were39 frozen during the training of alignment of image and text to retain strong text-only capabilities. We40 use the official implementation and checkpoint3 that uses a LLaMA-3.1 text-only language backbone41 in our evaluation. When evaluating the model, we choose to use a custom prompt since no standard42 prompt is provided.43

Unlike prior datasets that rely on crowdsourced prompts, AGMMU is distilled from 116,231 authentic dialogues between everyday growers and USDAauthorized Cooperative Extension experts. Through a three-stage pipeline: automated knowledge extraction, QA generation, and human verification, we construct (i) AGMMU, an evaluation set of 746 multiple-choice questions (MCQs) and 746 open-ended questions (OEQs), and (ii) AGBASE, a development corpus of 57,079 multimodal facts covering five high-stakes agricultural topics: insect identification, species identification, disease categorization, symptom description, and management instruction. AGMMU has three key advantages: Authentic & Expert-Verified: All facts, images, and answers originate from real farmer and gardener inquiries answered by credentialed specialists, ensuring high-fidelity agricultural knowledge. Complete Development Suite: AGMMU uniquely couples a dual-format evaluation benchmark (MCQ and OEQ) with AGBASE, a large-scale training set, enabling both rigorous assessment and targeted improvement of VLMs. Knowledge-intensive Challenge: Our tasks demand the synergy of nuanced visual perception and domain expertise, exposing fundamental limitations of current general-purpose models and charting a path toward robust, application-ready agricultural AI. Benchmarking 12 leading VLMs reveals pronounced gaps in fine-grained perception and factual grounding. Open-sourced models trail after proprietary ones by a wide margin. Simple fine-tuning on AGBASE boosts open-sourced model performance on challenging OEQs for up to 11.6% on average, narrowing this gap and also motivating future research to propose better strategies in knowledge extraction and distillation from AGBASE. We hope AGMMU stimulates research on domain-specific knowledge integration and trustworthy decision support in agriculture AI development.

Many computational tasks benefit from being formulated as the composition of neural networks followed by a discrete symbolic program. The goal of neurosymbolic learning is to train the neural networks using end-to-end input-output labels of the composite. We introduce CTSketch, a novel, scalable neurosymbolic learning algorithm. CTSketch uses two techniques to improve the scalability of neurosymbolic inference: decompose the symbolic program into sub-programs and summarize each sub-program with a sketched tensor. This strategy allows us to approximate the output distribution of the program with simple tensor operations over the input distributions and the sketches. We provide theoretical insight into the maximum approximation error. Furthermore, we evaluate CTSketch on benchmarks from the neurosymbolic learning literature, including some designed for evaluating scalability. Our results show that CTSketch pushes neurosymbolic learning to new scales that were previously unattainable, with neural predictors obtaining high accuracy on tasks with one thousand inputs, despite supervision only on the final output. 2

Cryo-EM is a transformational paradigm in molecular biology where computa-1 tional methods are used to infer 3D molecular structure at atomic resolution from2 extremely noisy 2D electron microscope images. At the forefront of research is3 how to model the structure when the imaged particles exhibit non-rigid conforma-4 tional flexibility and compositional variation where parts are sometimes missing.5 We introduce a novel 3D reconstruction framework with a hierarchical Gaussian6 mixture model, inspired in part by Gaussian Splatting for 4D scene reconstruction.7 In particular, the structure of the model is grounded in an initial process that infers8 a part-based segmentation of the particle, providing essential inductive bias in9 order to handle both conformational and compositional variability. The framework,10 called CryoSPIRE, is shown to reveal biologically meaningful structures on com-11 plex experimental datasets, and establishes a new state-of-the-art on CryoBench, a12 benchmark for cryo-EM heterogeneity methods.

The matrix recovery (completion) problem, a central problem in data science, involves recovering a matrix Afrom a relatively small random set of entries. While such a task is generally impossible, it has been shown that one can recover A exactly in polynomial time, with high probability, under three basic and necessary assumptions: (1) the rank of A is very small compared to its dimensions (low rank), (2) A has delocalized singular vectors (incoherence), and (3) the sample size is sufficiently large. Various algorithms address this task, including convex optimization by Candes, Recht, and Tao (2009), alternating projection by Hardt and Wooters (2014), and low-rank approximation with gradient descent by Keshavan, Montanari, and Oh (2009, 2010). In applications, Candes and Plan (2009) noted that it is more realistic to assume noisy observations. In such cases, the above approaches provide approximate recovery with small root mean square error, which is difficult to convert into exact recovery.

Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on computationally expensive sampling procedures and classifier-free guidance (CFG), resulting in slow inference.

We study the problem of infrequent exploration in linear bandits, addressing a significant yet overlooked gap between fully adaptive exploratory methods (e.g., UCB and Thompson Sampling), which explore potentially at every time step, and purely greedy approaches, which require stringent diversity assumptions to succeed. Continuous exploration can be impractical or unethical in safety-critical or costly domains, while purely greedy strategies typically fail without adequate contextual diversity. To bridge these extremes, we introduce a simple and practical framework, INFEX, explicitly designed for infrequent exploration. INFEX executes a base exploratory policy according to a given schedule while predominantly choosing greedy actions in between. Despite its simplicity, our theoretical analysis demonstrates that INFEX achieves instance-dependent regret matching standard provably efficient algorithms, provided the exploration frequency exceeds a logarithmic threshold. Additionally, INFEXis a general, modular framework that allows seamless integration of any fully adaptive exploration method, enabling wide applicability and ease of adoption. By restricting intensive exploratory computations to infrequent intervals, our approach can also enhance computational efficiency. Empirical evaluations confirm our theoretical findings, showing state-of-the-art regret performance and runtime improvements over existing methods.