NeurIPS 2026 Papers

Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Quality Assessment (IQA). However, existing RL-based IQA models typically rely on coarse-grained global views, failing to capture subtle local degradations in high-resolution scenarios. While emerging "Thinking with Images" paradigms enable multi-scale visual perception via zoom-in mechanisms, their direct adaptation to IQA induces spurious "cropping-implies-degradation'' biases and misinterprets natural depth-of-field as artifacts. To address these challenges, we propose Q-Probe, the first agentic IQA framework designed to scale IQA to high resolution via context-aware probing. First, we construct Vista-Bench, a pioneering benchmark tailored for fine-grained local degradation analysis in high-resolution IQA settings. Furthermore, we propose a three-stage training paradigm that progressively aligns the model with human preferences, while simultaneously eliminating causal bias through a novel context-aware cropping strategy. Extensive experiments demonstrate that Q-Probe achieves state-of-the-art performance in high-resolution settings while maintaining superior efficacy across resolution scales.

Recent advances in sparse voxel-based VAEs have demonstrated remarkable capabilities in high-fidelity 3D autoencoding, yet their generative counterparts consistently lag behind their reconstruction performance. A fundamental cause of this gap lies in the \emph{brittle output representation} in sparse voxel decoders, which parameterize geometry through discrete topological decisions, such as intersection flags or occupancy signs. Small perturbations in these parameters, which are unavoidable under diffusion sampling, can be amplified into abrupt topological ruptures and grid-like artifacts. We argue that a generation-friendly representation should ensure that such perturbations induce only smooth geometric transitions. To this end, we propose Pygmalion, a generation-friendly autoencoding framework that reformulates sparse voxel decoding as a coupled SDF parameterization. To retain explicit mesh-level supervision within this parameterization, which is non-trivial as the SDF jointly governs both topology and geometry, we introduce hinge-based sign correction, case-aware geometry supervision, and rendering-based refinement, achieving high-fidelity reconstruction while preserving generative robustness. Built upon this generation-friendly representation, Pygmalion scales consistently across DiT model sizes and voxel resolutions. Extensive experiments demonstrate state-of-the-art reconstruction fidelity and high-quality 3D generation with complete surfaces, sharp features, and fine geometric details.

Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically “sculpts” reflective objects into clay-like forms through image-to-clay translation. We then introduce a dual-branch design in which a BRDF-based reflective branch and a clay-guided branch share the same Gaussian geometry but operate through independent rendering paths. The clay-guided branch, supervised by the synthesized clay images, provides reflection-free geometric guidance that complements the photometric supervision of the BRDF branch. Experiments on both synthetic and real datasets show consistent improvements in both geometry and photometric metrics. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.

Modern video generative models produce visually impressive results, yet frequently violate basic physical principles. We propose \textbf{Proprio}, a training-free framework that enables a frozen video generator to assess and improve the physical plausibility of its own outputs. Inspired by \textit{proprioception}, the biological sense of one’s own movement, Proprio treats the model's flow residual under controlled latent perturbations as a self-scoring signal. Samples that are better explained by the generator's learned dynamics induce smaller and more stable residuals. We aggregate this signal across timesteps and perturbations, focus it on motion-relevant regions with a dynamic spatiotemporal mask, and use it for best-of-N search, gradient-based self-refinement, or both. Across text-to-video and image-to-video benchmarks, Proprio consistently improves physical plausibility, outperforming VLM-based scoring, and external world-model baselines in several settings. On TurboWan2.2, Proprio improves Physics-IQ from 32.2 to 37.5 (+16.5%) and VideoPhy2-hard physical commonsense from 45.6 to 55.0 (+20.6%). Human evaluation further shows that raters prefer Proprio-selected or refined videos for physical plausibility in roughly two-thirds of comparisons. These results suggest that frozen video generators contain actionable internal signals for evaluating and improving the physical plausibility of their own outputs.

Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal-centric lens. While recent research demonstrates the promise of fully visual approaches, they suffer from significant computational inefficiency due to the step-by-step planning-by-generation paradigm. In this work, we present EAR, an editing-as-reasoning paradigm that reformulates visual planning as a single-step image transformation. To isolate intrinsic reasoning from visual recognition, we employ abstract puzzles as probing tasks and introduce AMAZE, a procedurally generated dataset that features the classical Maze and Queen problems, covering distinct, complementary forms of visual planning. The abstract nature of AMAZE also facilitates automatic evaluation of autoregressive and diffusion-based models in terms of both pixel-wise fidelity and logical validity. We assess leading proprietary and open-source editing models. The results show that they all struggle in the zero-shot setting, finetuning on basic scales enables remarkable generalization to larger in-domain scales and out-of-domain scales and geometries. However, our best model that runs on high-end hardware fails to match the zero-shot efficiency of human solvers, highlighting a persistent gap in neural visual reasoning.

Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. However, Classifier-Free Guidance (CFG) and extrapolation-based methods are heuristic linear combinations of velocities/scores that ignore the generative manifold geometry, breaking probability conservation and driving samples off the learned manifold under strong guidance. We analyse guidance through the continuity equation and show its effect decomposes into a divergence term and a score-parallel term defined invariantly across parameterisations. We prove the divergence term blows up structurally as sampling approaches the data manifold, motivating a time-dependent schedule alongside score-parallel attenuation. The resulting plug-and-play rule, Adaptive Manifold Guidance (AdaMaG), bounds both terms at no additional inference cost. Finally, we show that most empirical heuristics for reducing saturation or improving generation quality correspond directly to the two terms in our decomposition. Across image generation benchmarks, AdaMaG improves realism, reduces hallucinations, and induces controlled desaturation in high-guidance regimes.

Structured visual artifacts such as geometry diagrams, circuit schematics, and flowcharts are increasingly important targets for image generation, yet visual plausibility alone is insufficient---a single missing object, wrong relation, broken connection, or extra element can invalidate the output. Existing image generators remain brittle on such tasks, and generic step-by-step or agentic methods often fail because intermediate edits may corrupt previously correct structures and ignore generator-specific failure modes. To address this, we present ProGraf, a profile-guided framework for structured non-natural image generation. We first build a benchmark of 304 tasks across geometry, circuit, flowchart, and general diagrams, each annotated with fine-grained verification points. From repeated generator outputs, we derive model-specific capability profiles capturing category-level reliability, component-level weaknesses, and recurrent failure patterns. ProGraf uses these profiles to guide closed-loop decomposition, verifies each intermediate result, commits only accepted states, and replans after failures. On generator-specific hardest subsets, ProGraf recovers 43.7--77.8\% of previously failed tasks across generators and categories, outperforming fixed-step and adapted agent baselines; ablation studies further show that benchmark-derived profiles are a key source of the gain.

Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, including oscillations and high-frequency variations. Flow Matching (FM) offers an efficient alternative to diffusion models, but practical implementations typically rely on a single finite-capacity global vector-field estimator. In such heterogeneous temporal distributions, distinct regimes may pass through nearby flow states while requiring incompatible conditional velocities. A monolithic estimator trained with the standard $\ell_2$ velocity-matching objective may therefore learn an overly smoothed approximation of the local transport field. This estimator-level smoothing can attenuate branch-specific dynamics, leading to spectral distortion and poor mode coverage. To address this, we propose PrismFlow, a new FM method with Koopman-inspired dynamical experts. Each expert learns residual corrections in a latent space where local nonlinear temporal evolution can be approximated by linear transitions. We further propose a confidence-aware Winner-Take-All (WTA) objective that updates only the expert best aligned with each sample while masking gradients to the others, encouraging mode-specific specialization. During sampling, the selected expert adds a residual dynamical correction to the global transport field, preserving FM stability while recovering fine-grained and high-frequency temporal structures. Across various benchmarks, PrismFlow effectively mitigates the spectral contraction in standard FM and achieves state-of-the-art performance, with a 15.6\% gain in Context-FID and a 38.6\% improvement in Discriminative Score, while remaining robust in low-data settings and effective for forecasting and imputation.

We consider the problem of learning to generate data while satisfying a prescribed sample-wise constraint with a user-specified probability of constraint satisfaction. Starting with a population-level constrained negative log-likelihood minimization and using a flow-matching (FM) parameterization, under regularity assumptions and sufficient expressivity of the velocity field family, we derive guarantees on the probability of constraint satisfaction for the corresponding primal-dual optimization algorithm. We provide a practical implementation of this approach, called Primal-Dual Flow Matching (PDFM), which has direct control over constraint satisfaction rate and requires only binary membership-oracle feedback for constraint satisfaction, avoiding the need for differentiable distances, projections, or special structure of the constraint set, such as convexity. We show that compared to existing methods, PDFM achieves higher constraint satisfaction rates while maintaining competitive distribution fidelity.

Code generation is typically trained in the \emph{primal} space of programs: a model produces a candidate solution and receives sparse execution feedback, often a single pass/fail bit. Test-time scaling enriches the inference procedure by sampling multiple candidates and judging among them, but the comparative information this process reveals is discarded after inference. We argue that this information defines a \emph{dual} judgment space that provides a far richer training signal: the model learns not from an isolated success or failure, but from the relative correctness structure across its own plausible attempts, identifying which succeed, which fail, and what distinguishes them. We introduce \textbf{DuST} (\textbf{Du}al \textbf{S}elf-\textbf{T}raining), a framework for self-training from the dual judgment space. DuST samples candidate programs from the model's own distribution, labels them through sandbox execution, retains groups containing both successes and failures, and trains the model to rank candidates by execution correctness using GRPO. The objective is purely discriminative: the model is never directly rewarded for generating correct programs. Dual self-training improves both judgment and generation. Across five models spanning two families and three scales (4B to 30B), DuST consistently improves Best-of-4 test-time scaling on LiveCodeBench. For Qwen3-30B-Thinking on LiveCodeBench v6, judgment quality improves by +6.2 NDCG, single-sample pass@1 improves by +3.1, and Best-of-4 accuracy improves by +4.1. The trained model's single rollout matches the base model's Best-of-4 performance. SFT on the same ranking data improves judgment without improving generation, confirming that on-policy RL is the mechanism that transfers dual-space learning back into primal generation.

Token-level maximum likelihood and mean negative log-likelihood dominate the training and evaluation of large language models (LLMs), yet long-horizon generation often suffers from late-stage degradation despite improved mean loss. We identify two coupled causes of this mismatch: mean token loss is not scale-free with respect to sequence length, so small per-token divergences can amplify into large sequence-level distribution shifts, and long-horizon failures are spike-dominated, where rare but abrupt prefix deviations trigger errors that are largely invisible to mean objectives. To address these issues, we propose \textbf{Prefix Likelihood-Ratio Control} (PLRC), which centers training on the prefix log-likelihood ratio process and its increments to directly capture sequence-level deviation and spikes. As the data likelihood is unknown, PLRC learns a prefix ratio critic via noise-contrastive prefix discrimination and introduces tail-sensitive regularizers on the terminal ratio and its increments, yielding guarantees that bound long-horizon event distortion and the probability of the first spike. PLRC preserves standard teacher-forcing training without architectural changes while explicitly targeting failure modes that mean token loss cannot capture.

Vector polygon generation converts visual inputs, e.g., remote sensing (RS) images, into vectorized polygonal geometries, supporting applications such as autonomous driving, vector map construction, and remote sensing. Early pipelines predict raster masks and post-process them into polygons, which prevents end-to-end optimization and may miss small objects or introduce inaccurate vertices. Recent methods directly generate vector polygons, but most focus on simple exterior contours, while they either cannot represent complex polygons with holes or fail to preserve their topology. In this paper, we propose PolyTopoBench, a unified evaluation framework for vector polygon generation from RS images with explicit emphasis on complex polygons. PolyTopoBench evaluates both exterior and interior rings, and benchmarks representative methods, including segmentation-based polygonization pipelines, vision foundation model baselines, and specialized vector polygon generators, on two RS-image datasets covering buildings, roads, vegetation, and unvegetated regions. Experiments show that existing methods often recover simple exterior boundaries but degrade substantially on polygons with holes or multiple rings. These results reveal complex polygon generation as an unresolved challenge and motivate topology-aware benchmarks and model designs.

Many projection-based imaging modalities reconstruct unknown objects from collections of noisy projections related by unknown in-plane rotations, making effective estimation dependent on rotationally consistent alignment and aggregation. In cryogenic electron microscopy (cryo-EM), the extremely low signal-to-noise ratio (SNR) makes such multi-image integration essential. We introduce the *polar transformer*, a permutation-invariant architecture for *sets* of images built around an $\mathrm{SO}(2)^K$-equivariant *angular attention* mechanism that jointly aligns and aggregates information across images. This architecture leverages a stable polar representation in which we define polar CNN feature extractors and evaluate attention weights over all relative angular shifts efficiently via FFT-based correlations, guaranteeing equivariance to independent in-plane rotations of each image in the set. We evaluate the polar transformer on simulated cryo-EM images using denoising as a quantitative proxy. Across different regimes, polar transformers outperform strong single-image and image-set baselines, achieving a reduction of up to 40% in relative MSE at an SNR of 0.02 and improving downstream ab initio reconstruction resolution by 20%.

Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle with compositional visual conditioning - simultaneously preserving semantic fidelity across multiple het erogeneous control signals while maintaining high visual quality, where they employ separate control branches that often introduce redundant guidance during the denoising process, leading to structural distortions and artifacts in generated images. To address this issue, we present PixelPonder, a novel unified control framework, which allows for effective control of multiple visual conditions under a single control structure. Specifically, we design a patch-level adaptive condition selection mechanism that dynamically prioritizes spatially relevant control signals at the subregion level, enabling precise local guidance without global interference. Additionally, a time-aware control injection 017 scheme is deployed to modulate condition influence according to denoising timesteps, progressively transitioning from structural preservation to texture refinement and utilizing the control information from different categories to promote finer image generation. Extensive experiments demonstrate that PixelPonder surpasses previous methods across different benchmark datasets, showing superior improvement in spatial alignment accuracy while maintaining high textual semantic consistency.

Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sidestep this physical model and predict only the sharp signal itself: most single-image deblurring methods recover a single frame at the exposure center, while blur-to-video methods predict a fixed set of frames. We introduce PickMoment, a continuous-time reformulation that directly learns the interval-mean blur over arbitrary sub-intervals of the exposure with a single deterministic model. Drawing an analogy to MeanFlow's average-velocity formulation, we train the model with three supervisions derived from the blur integral: an empirical reconstruction loss from available subframes, an additivity loss that enforces self-consistency across overlapping sub-intervals, and a sharp-frame loss anchored at the zero-interval limit. A single trained model unifies single-image deblurring, blur-to-video generation, and continuous-time pick-a-moment recovery as different queries to the same network, with no separate training for each task. Our PickMoment achieves state-of-the-art performance among generative-based deblurring methods on GoPro and HIDE while competitive against restoration-based methods on RealBlur, and the highest per-frame fidelity on GoPro-7 blur-to-video, all in a single forward pass without iterative sampling.

Most practical high-resolution text-to-image systems rely on latent diffusion models, where generation is performed in a compact latent space and a decoder maps latents back to pixels. Yet the latent-to-pixel decoder in this pipeline remains primarily reconstruction-oriented: as the sole pathway from latents to pixels, it is optimized to invert an encoder rather than to synthesize high-resolution details, and becomes increasingly costly in megapixel pipelines. This gap calls for a more expressive and efficient decoding paradigm. Motivated by recent progress in scalable pixel-space diffusion, we introduce **PiD**, a **Pi**xel diffusion **D**ecoder that reformulates latent decoding as conditional pixel diffusion, unifying decoding and upsampling into one generative module. By denoising directly in high-resolution pixel space, PiD synthesizes $4\times$and even $8\times$ upscaled images with low latency. For latent conditioning, a lightweight sigma-aware adapter injects noise-corrupted latents into the backbone, enabling PiD to handle partially denoised states and terminate the base diffusion process early. To further improve efficiency, we distill the decoder using DMD2, reducing inference to just $4$ steps. PiD applies to both conventional VAE latents and semantic latents (e.g., SigLIP, DINOv2) used in recent RAE-based models. Across multiple latent spaces and base generators, PiD improves visual fidelity over conventional decode-then-upsample cascades while reducing memory and latency, decoding latents of $512{\times}512$ images into $2048{\times}2048$ pixels in under $1$ second with $13$ GB peak memory on a consumer RTX 5090, and as fast as $210$ ms on a data-center GPU, about $6{\times}$ faster than cascaded diffusion-based super-resolution pipelines.

Realistic physical interaction is a cornerstone of embodied intelligence, yet obtaining high-fidelity tactile data remains significantly more expensive than visual data. This scarcity necessitates Visual-to-Tactile synthesis to bridge the Sim-to-Real gap. However, existing data-driven approaches often treat this as a naive image-to-image translation task, neglecting fundamental contact mechanics. Moreover, the inherent spatial misalignment in real-world visual-tactile datasets frequently forces generative models to average out spatial uncertainties, resulting in blurry and textureless outputs. To address these limitations, we introduce PhysTacGen, a physics-aware generation framework that enforces consistency between visual appearance and tactile mechanics. Our system makes three technical contributions. Firstly, we propose Group Tactile Policy Optimization (GTPO), a reinforcement learning alignment strategy that refines Large Multimodal Models to infer latent physical properties by rewarding physics-consistent reasoning chains. Secondly, we introduce a Semantic-Driven Data Curation and Visual Prior pipeline, leveraging DINOv2 to filter spatially misaligned data and extracting pure depth maps to decouple macro-geometry from surface texture. Finally, we present a physics-driven generative architecture based on Stable Diffusion XL (SDXL). It synthesizes tactile images via a ControlNet conditioned on GTPO-generated physical descriptions and depth-augmented visual priors. Extensive experiments demonstrate that PhysTacGen achieves state-of-the-art perceptual quality and significantly enhances grasp force prediction for zero-shot Sim-to-Real transfer.

Multimodal large language models (MLLMs) achieve strong performance in translating WebUI screenshots into HTML/CSS. However, when they faced multiple frontend frameworks (React/Vue/Angular), they often suffer from negative transfer that leads to compilation failures. These failures commonly stem from mixing framework specific syntax and violating framework constraints. This paper studies supervised fine tuning for multi-framework WebUI code generation and proposes a phase-wise MLLM tuning method driven by compatibility and heterogeneity signals across frameworks. We first apply a normalization mapping that preserves each framework's native syntax while suppressing noise from variable naming and literal content. We then estimate cross framework heterogeneity and compatibility offline, and automatically derive training groups and their ordering by maximizing a phased objective. Finally, we perform phase-wise adapter tuning to reduce cross framework interference. Experiments on multiple benchmarks show that the proposed method improves both generation quality and compilation success rate compared with baselines.

Image--text alignment models such as CLIP are typically trained with contrastive learning, where unpaired examples are treated as strict negatives. While this assumption introduces noise by penalising semantically related pairs, existing solutions often rely on heuristic similarity thresholds that lack statistical grounding and sensitivity to dataset-specific noise distributions. In this paper, we propose **Permute-then-Adapt (PTA)**, a framework that leverages efficient *weak models* to identify missed positives, while enforcing *statistical control* to reject random associations. Unlike standard distillation or arbitrary thresholding, PTA estimates the null distribution of the weak teacher's similarities via permutation testing, ensuring selected pairs are *statistically distinguishable from the distribution of random pairings* at level $\alpha$. The calibrated positive set is then trained against by a single multi-positive contrastive objective: each anchor maximises the total probability mass it assigns to its positive set. We further demonstrate that the calibration can be pre-computed offline using the weak model, so training overhead is negligible compared to standard baselines. Extensive experiments show that PTA consistently outperforms heuristic soft-label approaches on object recognition and cross-modal retrieval, while exhibiting superior data efficiency.