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Deep Learning · Large Language Models

Minghao Chen, Xinyi Hu, Zhou Yu, Yufei Yin

Large Language Model (LLM) based agents have demonstrated proficiency in multi-step interactions with graphical user interfaces (GUIs). While most research focuses on improving single-task performance, practical scenarios often involve repetitive GUI tasks for which invoking LLM reasoning repeatedly, i.e., the ReAct paradigm, is inefficient. Prior to LLMs, traditional Robotic Process Automation (RPA) offers runtime efficiency but demands significant manual effort to develop and maintain. To bridge this gap, we propose \textbf{AutoRPA}, a framework that automatically distills the decision logic of ReAct-style agents into robust RPA functions. AutoRPA introduces two core innovations: (1) A \textit{translator-builder pipeline} where a translator agent converts hard-coded ReAct actions into soft-coded procedures, and a builder agent synthesizes robust RPA functions via retrieval-augmented generation over multiple trajectories; (2) A \textit{hybrid repair strategy} during code verification, combining RPA execution with ReAct-based fallback for iterative refinement. Experiments across multiple GUI environments demonstrate that RPA functions generated by AutoRPA successfully solve similar tasks while reducing token usage by 82\%\textasciitilde96\%, significantly improving runtime efficiency and reusability.

Yakun Wang, Leyang Wang, Song Liu, Taiji Suzuki

Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works have exploited this unique capability to resolve fine-grained structural details beyond generation tasks. This paper presents a flow-inspired framework for representation learning. First, we demonstrate that a rectified flow trained using independent coupling is zero everywhere at $t=0.5$ if and only if the source and target distributions are identical. We term this property the \emph{zero-flow criterion}. Second, we show that this criterion can certify conditional independence, thereby extracting \emph{sufficient information} from the data. Third, we translate this criterion into a tractable, simulation-free loss function that enables learning amortized Markov blankets in graphical models and latent representations in self-supervised learning tasks. Experiments on both simulated and real-world datasets demonstrate the effectiveness of our approach.

Applications · Language, Speech and Dialog

Chenyang Shao, Jiahe Liu, Fengli Xu, Yong Li

Scientific illustration figures are essential for depicting research works' conceptual designs, methodology, and experimental workflows, playing a pivotal role in communicating complex academic insights. However, creating high-quality scientific illustrations remains a labor-intensive task for human scientists. While recent generative image models have advanced prompt-based editing, the synthesis of fully **editable** figures remains a fundamental challenge. Valid editability involves structured transformations of graphical elements, scales, attributes, and text, rather than simple pixel-level changes. Existing models generate raster outputs that do not support manual correction or layout adjustment, limiting their utility in scientific publishing, where editable vector figures are typically required for submission. To address this challenge, we introduce **LiveFigure**, an agentic framework driven by VLM agents that imitates the multi-step drawing workflow of human researchers. It first plans figure blueprints by drawing inspiration from high-quality references in previous works, then generates executable scripts that produce figures via the PowerPoint interface based on skills and experience, and finally refines the outputs with targeted visual diagnostics, producing fully vectorized, editable figures that meet publication standards. Extensive experiments demonstrate that LiveFigure generates inherently editable figures that are both visually clear and aesthetically appealing, achieving 80% publication-readiness within just 17 manual edits—far surpassing the 24% rate of the strongest baseline, NanoBanna. Human preference studies further validate this advantage, with LiveFigure securing a 60% win rate against NanoBanna. Our code is available at [https://anonymous.4open.science/r/LiveFigure](https://anonymous.4open.science/r/LiveFigure).

Deep Learning · Large Language Models

Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda, Reda Boumasmoud, Brian DuSell, Ryan Cotterell

Large language models (LLMs) trained on natural language data are capable of translating between languages, predict chess moves, and write poetry. Performance on a given task depends on directly relevant training data, yet confounders abound: data in related languages has been shown to help low-resource languages, and training on code has been shown to improve reasoning capabilities in natural language generation. Formal languages have become a common tool for understanding the learnability of language model architectures and their limitations---we argue that they should also be treated as multi-task learners when studying the learnability of a given \emph{task}. This means that to understand the learnability of a given property of a formal language, confounders from other tasks need to be considered. We propose a causal graphical model and an efficient sampling mechanism for probabilistic finite-state automata that gives full control over the occurrences of a given task while maintaining other language properties. To enable targeted evaluation, we derive task-specific decomposed KL-divergences. These tools allow us to know the \emph{causal} relationship between how often a task appears and its true learnability. Our experiments confirm that the correlation between task occurrences and learnability does not recover the accurate relationship---for this, the causal analysis and machinery is necessary.

Computer Vision · Image and Video Generation

Li Li, Ryan Rossi, Sungchul Kim, Sunav Choudhary, Franck Dernoncourt, Puneet Mathur, Zhengzhong Tu, Yue Zhao

Generative models, such as diffusion and autoregressive approaches, have demonstrated impressive capabilities in editing natural images. However, applying these tools to scientific charts rests on a flawed assumption: a chart is not merely an arrangement of pixels but a visual representation of structured data governed by a graphical grammar. Consequently, chart editing is not a pixel-manipulation task but a structured transformation problem. To address this fundamental mismatch, we introduce \textit{FigEdit}, a large-scale benchmark for scientific figure editing comprising over 30,000 samples. Grounded in real-world data, our benchmark is distinguished by its diversity, covering 10 distinct chart types and a rich vocabulary of complex editing instructions. The benchmark is organized into five distinct and progressively challenging tasks: single edits, multi edits, conversational edits, visual-guidance-based edits, and style transfer. Our evaluation of a range of state-of-the-art models on this benchmark reveals their poor performance on scientific figures, as they consistently fail to handle the underlying structured transformations required for valid edits. Furthermore, our analysis indicates that traditional evaluation metrics (e.g., SSIM, PSNR) have limitations in capturing the semantic correctness of chart edits. Our benchmark demonstrates the profound limitations of pixel-level manipulation and provides a robust foundation for developing and evaluating future structure-aware models. By releasing \textit{FigEdit}, we aim to enable systematic progress in structure-aware figure editing, provide a common ground for fair comparison, and encourage future research on models that understand both the visual and semantic layers of scientific charts.

Yan Yang, Dongxu Li, Yutong Dai, Yuhao Yang, Ziyang Luo, Zirui Zhao, Zhiyuan Hu, Junzhe Huang, Amrita Saha, Zeyuan Chen 等

Graphical user interface (GUI) agents autonomously complete tasks across platforms (\eg, Linux) by sequentially decomposing user instructions into action proposals that iteratively interact with visual elements in the evolving environment. However, two main challenges arise: i) planning (\ie, the action proposal sequence) under expansive action space, where selecting an appropriate plan is non-trivial, as many valid ones may exist; ii) accurately grounding actions in complex and high-resolution interfaces, \ie, precisely interacting with visual targets. This paper investigates the aforementioned challenges with our \textbf{G}UI \textbf{T}est-time Scaling \textbf{A}gent, namely \name. First, we conduct test-time scaling to select the most appropriate action proposal: at each step, multiple candidate proposals are sampled and evaluated and selected by a judge model. It trades off computation for better decision quality by concurrent sampling. Second, we propose a model that improves grounding of the selected action proposals to its corresponding visual elements. Our key insight is that reinforcement learning (RL) facilitates grounding through inherent objective alignments, rewarding successful clicks on interface elements. Experimentally, \name achieves state-of-the-art performance on both grounding and agent task execution benchmarks.

Optimization · Sampling and Optimization

Yaomin Wang, Xiaodong Luo, Tianshu Yu

This study introduces a novel method for performing Maximum A Posteriori (MAP) estimation on Markov Random Fields (MRFs) that are defined on locally and sparsely connected graphs, broadly existing in real-world applications. We address this long-standing challenge by sampling uniform random spanning trees(SPT) from the associated graph. Such a sampling procedure effectively breaks the cycles and decomposes the original MAP inference problem into overlapping sub-problems on trees, which can be solved exactly and efficiently. We demonstrate the effectiveness of our approach on various types of graphical models, including grids, cellular/cell networks, and Erdős–Rényi graphs. Our algorithm outperforms various baselines on synthetic, UAI inference competition, and real-world PCI problems, specifically in cases involving locally and sparsely connected graphs. Furthermore, our method achieves comparable results to these methods in other scenarios. The code of our model can be accessed at \url{https://github.com/LOGO-CUHKSZ/From-fields-to-random-trees.git}.

Deep Learning · Generative Models and Autoencoders

Yidan Xu, Yixin Wang, XuanLong Nguyen

Flow matching is a powerful approach for high-fidelity density estimation, but it often fails to capture the latent structure of complex data. Probabilistic models like variational autoencoders (VAEs), on the other hand, learn structured representations but underperform in sample quality. We propose Structured Flow Autoencoders (SFA), a family of probabilistic models that augments graphical models with conditional continuous normalizing flow (CNF) likelihoods, enabling flow-matching-based structured representation learning. At the core of SFA is a novel flow matching objective that explicitly accounts for latent variables, allowing joint learning of the CNF likelihood and posterior. SFA applies broadly to graphical models with continuous and mixture latents, as well as latent dynamical systems. Empirical studies across image, video, and RNA-seq data show that SFA consistently outperforms VAEs and their structured extensions in generation quality, representation utility, and scalability to large datasets. Compared to generative models like latent flow matching (LatentFM), SFA also produces more diverse samples, suggesting better coverage of the data distribution.

Applications · Time Series

Xuechun Li, Shan Gao, Susu Xu

Complex causal systems with interdependent variables require inference from heterogeneous observations that vary in spatial resolution, temporal frequency, and noise characteristics due to data acquisition constraints. Existing multi-modal fusion approaches assume uniform data quality or complete observability -- assumptions often violated in real-world applications. Current methods face three limitations: they treat causally-related variables independently, failing to exploit causal relationships; they cannot integrate multi-resolution observations effectively; and they lack theoretical frameworks for cascaded approximation errors. We introduce the Score-based Variational Graphical Diffusion Model (SVGDM), which integrates score-based diffusion within causal graphical structures for inference under heterogeneous incomplete observations. SVGDM introduces causal score decomposition enabling information propagation across causally-connected variables while preserving original observation characteristics. Diffusion provides a natural way to model scale-dependent sensing noise, which is common in remote-sensing, climate, and physical measurement systems, while the causal graph encodes well-established mechanistic dependencies between latent processes. We provide theoretical analysis and demonstrate superior performance on both synthetic and real-world datasets compared to relevant baselines.

Computer Vision · Image and Video Generation

Hui Zhang, Dexiang Hong, Maoke Yang, Yutao Cheng, Zhao Zhang, Weidong Chen, Jie Shao, Xinglong Wu, Zuxuan Wu, Yu-Gang Jiang

Graphic design plays a vital role in visual communication across advertising, marketing, and multimedia entertainment. Prior work has explored automated graphic design generation using diffusion models, aiming to streamline creative workflows and democratize design capabilities. However, complex graphic design scenarios require accurately adhering to design intent specified by multiple heterogeneous user-provided elements (\eg images, layouts, and texts), which pose multi-condition control challenges for existing methods. Specifically, previous single-condition control models demonstrate effectiveness only within their specialized domains but fail to generalize to other conditions, while existing multi-condition methods often lack fine-grained control over each sub-condition and compromise overall compositional harmony. To address these limitations, we introduce CreatiDesign, a systematic solution for automated graphic design covering both model architecture and dataset construction. First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements with minimal architectural modifications to the base diffusion model. Furthermore, to ensure that each condition precisely controls its designated image region and to avoid interference between conditions, we propose a multimodal attention mask mechanism. Additionally, we develop a fully automated pipeline for constructing graphic design datasets, and introduce a new dataset with 400K samples featuring multi-condition annotations, along with a comprehensive benchmark. Experimental results show that CreatiDesign outperforms existing models by a clear margin in faithfully adhering to user intent.

Computer Vision · Vision Models & Multimodal

Haomin Wang, Jinhui Yin, Qi Wei, Wenguang Zeng, Lixin Gu, Shenglong Ye, Zhangwei Gao, Yaohui Wang, Yanting Zhang, Yuanqi Li 等

General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data–benchmark–model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on \benchset and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.

Computer Vision · Vision Models & Multimodal

Qinghongbing Xie, Zhaoyuan Xia, Feng Zhu, Lijun GONG, Ziyue Li, Rui Zhao, Long ZENG

Recently spatial-temporal intelligence of Visual-Language Models (VLMs) has attracted much attention due to its importance for autonomous driving, embodied AI and general AI. Existing spatial-temporal benchmarks mainly focus on egocentric (first-person) perspective reasoning using images/video contexts, or geographic reasoning with graphical context (e.g., maps), thus fail to assess VLMs' geographic spatial-temporal intelligence that requires integrating both images/video and graphical context, which is crucial for real-world scenarios such as traffic management and emergency response. To address the gaps, we introduce Geo-Temporal Reasoning benchmark (GTR-Bench), a novel challenge for geographic temporal reasoning of moving targets in a large-scale camera network. GTR-Bench is more challenging as it requires multiple perspective switches between maps and videos, joint reasoning across multiple videos with non-overlapping fields of view, and inference over spatial-temporal regions that are unobserved by any video context. Evaluations of more than 10 popular VLMs on GTR-Bench show that even the best proprietary model, Gemini-2.5-Pro (34.9\%), significantly lags behind human performance (78.61\%) on geo-temporal reasoning. Moreover, our comprehensive analysis on GTR-Bench reveals three major deficiencies of current models for geo-temporal reasoning. (1) VLMs exhibit imbalanced utilization of spatial and temporal context during reasoning. (2) they show weak temporal forecasting ability, leading to poorer performance on temporally focused tasks. (3) they lack the capability to effectively align and integrate map data with multi-view video inputs. We believe GTR-Bench offers valuable insights and opens up new opportunities for research and applications in spatial-temporal intelligence. Benchmark and code will be released at https://github.com/X-Luffy/GTR-Bench.

Theory · Interpretability and Visualization

Tianxiang Dai, Jonathan Fan

Multi-Resolution Hash Encoding (MHE), the foundational technique behind Instant Neural Graphics Primitives, provides a powerful parameterization for neural fields. However, its spatial behavior lacks rigorous understanding from a physical systems perspective, leading to reliance on heuristics for hyperparameter selection. This work introduces a novel analytical approach that characterizes MHE by examining its Point Spread Function (PSF), which is analogous to the Green's function of the system. This methodology enables a quantification of the encoding's spatial resolution and fidelity. We derive a closed-form approximation for the collision-free PSF, uncovering inherent grid-induced anisotropy and a logarithmic spatial profile. We establish that the idealized spatial bandwidth, specifically the Full Width at Half Maximum (FWHM), is determined by the average resolution, $N_{\text{avg}}$. This leads to a counterintuitive finding: the effective resolution of the model is governed by the broadened empirical FWHM (and therefore $N_{\text{avg}}$), rather than the finest resolution $N_{\max}$, a broadening effect we demonstrate arises from optimization dynamics. Furthermore, we analyze the impact of finite hash capacity, demonstrating how collisions introduce speckle noise and degrade the Signal-to-Noise Ratio (SNR). Leveraging these theoretical insights, we propose Rotated MHE (R-MHE), an architecture that applies distinct rotations to the input coordinates at each resolution level. R-MHE mitigates anisotropy while maintaining the efficiency and parameter count of the original MHE. This study establishes a methodology based on physical principles that moves beyond heuristics to characterize and optimize MHE.

Ruichuan An, Shizhao Sun, Danqing Huang, Mingxi Cheng, Yan Gao, Ji Li, YU QIAO, Jiang Bian

Assessing the aesthetic quality of graphic design is central to visual communication, yet remains underexplored in vision–language models (VLMs). We investigate whether VLMs can evaluate design aesthetics in ways comparable to humans. Prior work faces three key limitations: benchmarks restricted to narrow principles and coarse evaluation protocols, a lack of systematic VLM comparisons, and limited training data for model improvement. In this work, we introduce AesEval-Bench, a comprehensive benchmark spanning four dimensions, twelve indicators, and three fully quantifiable tasks: aesthetic judgment, region selection, and precise localization. Then, we systematically evaluate proprietary, open-source, and reasoning-augmented VLMs, revealing clear performance gaps against the nuanced demands of aesthetic assessment. Moreover, we construct a training dataset to fine-tune VLMs for this domain, leveraging human-guided VLM labeling to produce task labels at scale and indicator-grounded reasoning to tie abstract indicators to concrete design regions.Together, our work establishes the first systematic framework for aesthetic quality assessment in graphic design.

Deep Learning · Generative Models and Autoencoders

Yidan Xu, Yixin Wang, XuanLong Nguyen

Flow matching is a powerful approach for high-fidelity density estimation, but it often fails to capture the latent structure of complex data. Probabilistic models like variational autoencoders (VAEs), on the other hand, learn structured representations but underperform in sample quality. We propose Structured Flow Autoencoders (SFA), a family of probabilistic models that augments graphical models with conditional continuous normalizing flow (CNF) likelihoods, enabling flow-matching-based structured representation learning. At the core of SFA is a novel flow matching objective that explicitly accounts for latent variables, allowing joint learning of the CNF likelihood and posterior. SFA applies broadly to graphical models with continuous and mixture latents, as well as latent dynamical systems. Empirical studies across image, video, and RNA-seq data show that SFA consistently outperforms VAEs and their structured extensions in generation quality, representation utility, and scalability to large datasets. Compared to generative models like latent flow matching (LatentFM), SFA also produces more diverse samples, suggesting better coverage of the data distribution.

Reinforcement Learning · Online

Yifan Xu, Xiao Liu, Xinghan Liu, Jiaqi Fu, Jiayu Huang, Hanchen Zhang, Bohao Jing, Shudan Zhang, Yuting Wang, Zhao wenyi 等

Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile GUI agents with reinforcement learning (RL) remains challenging due to the heavy-tailed distribution of task difficulty and the inefficiency of large-scale environment sampling. We present an online agentic reinforcement learning framework MOBILERL to enhance GUI agents in mobile environments. Its core component is the Difficulty-Adaptive GRPO (ADAGRPO) algorithm. In ADAGRPO, we design difficulty-adaptive positive replay and failure curriculum filtering to adapt the model to different task difficulties. We introduce the shortest-path reward adjustment strategy to reshape rewards concerning the task length in multi-turn agentic tasks. Those strategies jointly stabilize RL training, improve sample efficiency, and generate strong performance across diverse mobile apps and tasks. We apply MOBILERL to two open models (Qwen2.5-VL-7B-Instruct and GLM-4.1V-9B-Base). The resultant MOBILERL-9B model achieves state-of-the-art results in terms of success rates on both AndroidWorld (80.2%) and Android-Lab (53.6%). The MOBILERL is open-sourced at https://github.com/THUDM/MobileRL.

Computer Vision · Vision Models & Multimodal

Rui LYU, Juncheng Mo, Tianyi Chu, Chen Rao, Hongyi Jing, Jiajie Teng, Jiafu Chen, Shiqi Zhang, Liangzi Ding, Shuo Fang 等

Graphical User Interface (GUI) agent is pivotal to advancing intelligent human-computer interaction paradigms. Constructing powerful GUI agents necessitates the large-scale annotation of high-quality user-behavior trajectory data (i.e., intent–trajectory pairs) for training. However, manual annotation methods and current GUI agent data mining approaches typically face three critical challenges: high construction cost, poor data quality, and low data richness. To address these issues, we propose M$^2$-Miner, the first low-cost and automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search (MCTS). For better data mining efficiency and quality, we present a collaborative multi-agent framework, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, acceleration, and evaluation. To further enhance the efficiency of mining and enrich intent diversity, we design an intent recycling strategy to extract extra valuable interaction trajectories. Additionally, a progressive model-in-the-loop training strategy is introduced to improve the success rate of data mining. Extensive experiments have demonstrated that the GUI agent fine-tuned using our mined data achieves state-of-the-art performance on several commonly used mobile GUI benchmarks. Our work will be released to facilitate the community research.

Computer Vision · 3D Rendering & Reconstruction

Zhigang Cheng, Mingchao Sun, Liu Yu, zengye ge, Luyang Tang, Mu Xu, Yangyan Li, Peng Pan

Level of Detail (LoD) is a fundamental technique in real-time computer graphics for managing the rendering costs of complex scenes while preserving visual fidelity. Traditionally, LoD is implemented using discrete levels (DLoD), where multiple, distinct versions of a model are swapped out at different distances. However, this long-standing paradigm suffers from two major drawbacks: it requires significant storage for multiple model copies and causes jarring visual "popping" artifacts during transitions, degrading the user experience. We argue that the explicit, primitive-based nature of the emerging 3D Gaussian Splatting (3DGS) technique enables a more ideal paradigm: Continuous LoD (CLoD). A CLoD approach facilitates smooth and seamless quality scaling within a single unified model, thereby circumventing the core problems of DLOD. To this end, we introduce CLoD-GS, a framework that integrates a continuous LoD mechanism directly into a 3DGS representation. Our method introduces a learnable distance-dependent decay parameter for each Gaussian primitive that dynamically adjusts its opacity based on viewpoint proximity. This allows for the progressive and smooth filtering of less significant primitives, effectively creating a continuous spectrum of detail within one model. To train this model to be robust across all distances, we introduce a virtual distance scaling mechanism with point count regularization. Our approach not only eliminates the storage overhead and visual artifacts of discrete methods but also reduces the primitive count and memory footprint of the final model. Extensive experiments demonstrate that CLoD-GS achieves smooth, quality-scalable rendering from a single model, delivering high-fidelity results across a wide range of performance targets.

Applications · Everything Else

Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang, Xiaoning Du, Jiaojiao Jiang, Zhenchang Xing, Jiamou Sun, Terry Yue Zhuo

Web applications are prime targets for cyberattacks due to their role as entry points to vital services and sensitive data repositories. Traditional penetration testing is expensive and requires specialized expertise, creating scalability challenges for securing the expanding web ecosystem. While language model agents have shown promise in certain cybersecurity tasks, modern web applications require visual understanding of complex user interfaces, dynamic content rendering, and multi-step interactive workflows that only computer-use agents (CUAs) can handle. Despite CUAs' demonstrated capabilities in web browsing and visual task automation, their potential to discover and exploit web application vulnerabilities through graphical interfaces remains unknown. We introduce HackWorld, the first evaluation framework for systematically assessing CUAs' capabilities in exploiting web application vulnerabilities through visual interaction. Unlike existing benchmarks using sanitized environments, HackWorld exposes CUAs to 36 curated applications spanning 11 frameworks and 7 languages, containing realistic vulnerabilities including injection flaws, authentication bypasses, and unsafe input handling. Our framework directly evaluates CUAs' ability to discover and exploit these vulnerabilities using Capture-the-Flag (CTF) methodology while navigating complex web interfaces. Evaluation of state-of-the-art CUAs reveals exploitation rates below 12%, struggling to plan multi-step attacks and use security tools effectively. Our results expose CUAs' limited cybersecurity skills when operating on vulnerable web applications, opening future research directions on developing security-aware CUAs for vulnerability detection and exploitation.

Yunzhi Zhang, Carson Murtuza-Lanier, Zizhang Li, Yilun Du, Jiajun Wu

Modern neural models capture rich priors and have complementary knowledge over shared data domains, e.g., images and videos. Integrating diverse knowledge from multiple sources—including visual generative models, visual language models, and sources with human-crafted knowledge such as graphics engines and physics simulators remains under-explored. We propose a probabilistic framework that combines information from these heterogeneous models, where expert models jointly shape a product distribution over outputs. To sample from this product distribution for controllable image/video synthesis tasks, we introduce an annealed MCMC sampler in combination with SMC-style resampling to enable efficient inference-time model composition. Our framework empirically yields better controllability than monolithic methods and additionally provides flexible user interfaces for specifying visual generation goals.