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.

The UK Will Scan Asylum-Seekers' Faces for Age Checks--Despite Knowing the Tech Is Flawed Age verification is consuming the internet . From social media bans in Australia to porn restrictions in half of US states, for many having to prove their age to access websites is becoming an everyday requirement . But one of the key technologies underpinning many of these age checks is about to seep into the offline world--with potentially life-changing consequences for people having their age predicted by AI. Starting next year, the British government is planning to introduce facial age estimation--where AI scans your face and suggests how old you are --to help determine the age of asylum seekers arriving at the United Kingdom's border. The move is believed to be the first time that a so-called facial age estimation (FAE) system has been used in this way.

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.

Recent advances in large language models (LLMs) have enabled the automatic generation of executable code for task planning and control in embodied agents such as robots, demonstrating the potential of LLM-based embodied intelligence. However, these LLM-based code-as-policies approaches often suffer from limited environmental grounding, particularly in dynamic or partially observable settings, leading to suboptimal task success rates due to incorrect or incomplete code generation. In this work, we propose a neuro-symbolic embodied task planning framework that incorporates explicit symbolic verification and interactive validation processes during code generation. In the validation phase, the framework generates exploratory code that actively interacts with the environment to acquire missing observations while preserving task-relevant states. This integrated process enhances the grounding of generated code, resulting in improved task reliability and success rates in complex environments. We evaluate our framework on RLBench and in realworld settings across dynamic, partially observable scenarios. Experimental results demonstrate that our framework improves task success rates by 46.2% over Code as Policies baselines and attains over 86.8% executability of task-relevant actions, thereby enhancing the reliability of task planning in dynamic environments.