NeurIPS 2026 Papers

Inversion-free methods for text-driven image editing construct direct ODE paths between source and target distributions using pre-trained flow models, but operate on single trajectories vulnerable to noise-induced drift with no mechanism to exploit the geometry of plausible edits. We propose Transport-Guided Flow Editing, which recasts the editing problem in distributional terms: we construct source and target particle clouds in latent space, solve entropic optimal transport between them with an edit-direction-aware cost, and use the resulting barycentric map to define a time-varying correction field for the editing ODE. The transport plan adapts online at each integration step, and the correction strength is governed by a variational co-state derived from the OT anchor, responding to accumulated trajectory deviation rather than following a fixed schedule. The output is a single edited image, but one whose trajectory has been steered by distributional information inaccessible to single-trajectory methods. Extensive experiments demonstrate state-of-the-art structure preservation with superior semantic alignment and consistent human preference over existing baselines.

Text-to-image diffusion models fail predictably on compositional prompts: attributes bind to the wrong objects, spatial relations invert, and multi-object scenes lose count. Recent architectures already augment CLIP with a T5 encoder precisely because CLIP's contrastive embedding loses compositional structure, yet these failures persist. We argue the binding problem is therefore not one of missing information but of misaligned information: a text encoder preserves compositional structure, but in a representation space shaped by language modelling rather than vision, and the denoising objective does not directly reward aligning the two. We show this correspondence can be supplied as an explicit training signal, that the relevant cross-modal information is concentrated in a low-rank subspace of self-supervised visual features, and that supplying it can be folded into diffusion training as a single auxiliary loss. Our method, ORCA, Orthogonal Residual Compositional Alignment, aligns the latent of a diffusion transformer with a low-rank target derived from a frozen visual encoder, through a predictor whose orthogonal basis is parameterised by a learned residual between T5 and CLIP embeddings, which provides a prompt-dependent signal for selecting the visual readout subspace. We prove that the cross-modal information recoverable at a given rank is bounded by the spectral mass of the visual encoder's covariance in the top components. Across three diffusion-transformer backbones, DiT-B/2, DiT-L/2, and U-ViT-L, ORCA improves FID and GenEval over both vanilla and REPA baselines at zero inference-time cost; on DiT-L/2 it reaches FID 16.65 and GenEval 0.291 at 200K steps, exceeding the strongest 400K baseline at half the training cost, with the largest gains concentrated on attribute binding, spatial relations, and multi-object prompts.

Recent advances in Video Foundation Models (VFMs) have revolutionized human-centric video synthesis, yet fine-grained and independent control of subjects and scenes remains a critical challenge. Recent attempts to achieve joint human-environment control often rely on explicit human-scene 3D alignment, improving geometric control but sacrificing generative flexibility and long-horizon consistency while requiring heavy 3D pre-processing. We present ONE-SHOT, a parameter-efficient framework for compositional human-environment video generation. Our key insight is to factorize the generative process into disentangled signals, separating human dynamics from scene cues. Specifically, our canonical-space motion injection and Dynamic-Grounded-RoPE map canonical human motion to the target video region, enabling accurate motion and placement control without heuristic human-scene 3D alignment. Hybrid Context Integration further maintains subject and scene consistency for minute-level synthesis. Experiments show that ONE-SHOT consistently outperforms state-of-the-art methods in structural control, flexible composition, text-guided semantic control, and long-horizon consistency.

Unified models for visual understanding and generation have made rapid progress, yet they still lack the ability to understand and manipulate the spatial states of object instances. Existing models can describe objects in natural language, but they struggle to precisely represent continuous object poses and generate geometrically consistent images under target viewpoints. To mitigate this, we propose \emph{Object-Uni}, a unified model for object-centric spatial understanding and controllable generation. Specifically, we formulate object-centric spatial intelligence as a unified problem connecting pose perception, spatial reasoning, pose-conditioned generation, and object-centric novel view synthesis. We treat object pose as an explicit geometric variable shared by understanding and generation, rather than merely a prediction label or control signal. To make pose usable by multimodal large language models, we propose a viewpoint-based orientation abstraction that maps orientation into structured viewpoint descriptions while preserving continuous geometric supervision. We further construct an object-centric spatial benchmark (UniSpatial-80K) and train a unified model with an object-token-grounded pose anchor to associate each instance with its pose state. Experiments show that our model improves object-level pose understanding and pose-controllable generation, moving unified models from describing objects toward manipulating spatial states.

Continual tuning is essential for adapting multimodal large language models to evolving tasks and domains, yet existing methods often assume that every image-present example provides valid supervision for the visual pathway. We address the resulting gap: how to learn from all incoming multimodal instructions while preventing visually unnecessary supervision from drifting the visual interface. We propose Visual-Necessity-Gated Continual Tuning (VNG-CT), a path-aware framework that estimates sample-level dependence on visual evidence by comparing target likelihoods under the original image and a counterfactual null image. The resulting gate routes gradients to different parameter paths: all examples update the language path, visually necessary examples update the visual path, and low-necessity examples are absorbed by a lightweight calibration path that promotes invariance to irrelevant visual evidence. Across CoIN, MLLM-CL, and UCIT-style continual instruction streams, VNG-CT improves final and average performance while reducing visual forgetting. These results suggest that visual necessity offers a practical principle for stabilizing continual multimodal adaptation without discarding useful language-side supervision.

Parameter-efficient image-to-video transfer adapts frozen image foundation models to video via lightweight trainable adapters. Existing methods insert the same adapter at every transformer layer, implicitly assuming all layers require equal temporal modeling. A layer-wise sensitivity analysis across four video-native model families and two pretraining paradigms reveals that this assumption is wrong: temporal sensitivity increases with depth, and the pretraining paradigm determines its growth profile. Vision-only models exhibit a gradual rise, while vision-language models show early suppression followed by late recovery. This finding motivates STRAP, a training-free adapter placement rule that derives a spatial-to-temporal transition boundary from a paradigm-matched video model. Spatial adapters are assigned before this boundary and temporal adapters after it, without modifying the adapter architecture itself. On four fine-grained action recognition benchmarks, STRAP improves accuracy by up to 11.8\% over uniform placement, reduces trainable parameters by 29 to 48\%, and lowers inference time by up to 10\%. On standard benchmarks, it matches accuracy with fewer parameters.

Histological whole-slide images (WSIs) are central to computational pathology but pose severe computational challenges due to their extremely high resolution, often spanning several gigabytes per slide. To enable scalable learning, existing methods apply self-supervised data condensation to reduce computational cost, but typically rely on heuristic prototype learning and do not explicitly preserve learning-relevant feature distributions for downstream tasks. In response, we introduce a principled reformulation of WSI condensation as a distribution-matching problem under a fixed representational lens, and develop NICER, a tractable approximation framework based on a nonparametric prior with slide-adaptive capacity. Experiments on five histopathology datasets, together with clinical evaluation from a board-certified pathologist, show that NICER consistently outperforms prior methods, achieving an average accuracy improvement of 7.44% while offering improved efficiency–accuracy trade-offs, highlighting the benefits of principled, distribution-aware condensation for scalable histological representation learning.

Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-SR), owing to their noise-started generation process that enables realistic texture synthesis and captures the one-to-many nature of super-resolution. However, diffusion-based Real-SR methods still face a fundamental efficiency-quality trade-off. Multi-step methods generate high-quality results by iteratively denoising random Gaussian noise under LR conditioning, but suffer from slow sampling. Recent one-step methods greatly improve efficiency, yet they typically replace noise-started generation with direct LR-to-HR restoration, which weakens stochasticity and limits realistic detail synthesis. To address this issue, we propose SMFSR, a noise-started one-step Real-SR framework via LR-conditioned SplitMeanFlow and GAN refinement. SMFSR preserves the random-noise starting point of diffusion models and learns a direct noise-to-HR mapping conditioned on the LR image. To this end, Interval Splitting Consistency distills the multi-step generative trajectory into a single average-velocity prediction, enabling efficient one-step generation. To compensate for the reduced opportunity for progressive refinement, we further introduce a GAN refinement stage, where a DINOv3-based discriminator enhances realistic texture synthesis and variational score distillation aligns the generated outputs with the natural image distribution. Extensive experiments demonstrate that SMFSR achieves superior perceptual quality while retaining the high efficiency of one-step diffusion models.

Reactive synthesis from temporal logic modulo theories (LTLt) automatically con- structs correct-by-construction controllers for systems with arithmetic constraints— elevators, thermostats, robotic patrols—but the state-of-the-art CEGRES algorithm times out on 77% of existing benchmarks due to a fundamental bottleneck: it discovers the universally valid theory tautologies needed to refine its Boolean abstraction one at a time, through expensive counterexample analysis. We present NEUROSYNTHEOS, a neural augmentation of the CEGRES loop that predicts batches of tautologies at each refinement step. We formalize template invariance for parametric specification families, showing that the prediction problem reduces to constant re-instantiation when specifications share algebraic structure. We train a GNN over formula abstract syntax trees and an autoregressive transformer on tautology traces from 104 solved benchmarks. NEUROSYNTHEOS establishes a new state of the art for LTLt synthesis: on 83 standard benchmarks, it solves 60 ±1.2 specifications (vs. 19 for the previous best), resolving 41 that no prior method could handle within a 5-minute budget, while reducing the median itera- tion count by 5.2×with under 3 seconds of neural overhead. All predictions are SMT-verified before injection, preserving full soundness guarantees.

Docking ultimately determines effective ligand binding, yet bridging global functional semantics and local physical constraints remains a central challenge for protein generation for ligand-binding design. We therefore use docking-oriented language describing docking processes, binding patterns, and local interaction constraints as generation conditions, rather than relying only on coarse-grained functional text. However, existing methods still insufficiently model docking constraints and show limited coordination among heterogeneous modalities. To address these limitations, we propose DockWizard, an encoder-router-decoder framework for multimodal protein generation. Concretely, (1) a multimodal encoder aligns function text, docking text, ligand descriptions, ligand SMILES, and ligand 3D information into shared conditional representations, improving semantic consistency across heterogeneous inputs; (2) a router module organizes heterogeneous conditions into role-specific memories by their scopes of action, preserving global functional guidance while strengthening local binding constraints; and (3) a protein decoder combines prefix guidance with full-memory injection to let different conditions act at different levels, improving controllability while preserving docking constraints during generation. We further curate a docking-language multimodal dataset with over 100,000 samples. Extensive experiments under a four-stage evaluation pipeline show that DockWizard outperforms strong baselines for this task, with average gains of 13% in docking confidence and 19% in complex stability.

Most recent real-world image super-resolution (Real-ISR) methods employ pre-trained text-to-image (T2I) diffusion models to synthesize the high-quality image either from random Gaussian noise or directly from the input low-quality image. These approaches train ControlNet or LoRA modules while keeping the pre-trained model fixed, which often introduces over-enhanced artifacts and hallucinations, suffering from limited robustness to inputs with varying degradations. Recent visual autoregressive (AR) models, such as pre-trained Infinity, can provide strong T2I generation capabilities while offering superior efficiency by using the bitwise next-scale prediction strategy. Building upon next-scale prediction, we introduce a robust Real-ISR framework with a generation pathway control mechanism, namely Next-Scale Autoregressive Modeling (NSARM). Specifically, we train NSARM in two stages: a transformation network is first trained to map the input low-quality image to preliminary scales, followed by an end-to-end full-model fine-tuning. Such a comprehensive fine-tuning enhances the robustness of NSARM in Real-ISR tasks without compromising its generative capability. Extensive quantitative and qualitative evaluations demonstrate that as a pure AR model, NSARM achieves superior visual results over existing Real-ISR methods, with fast inference speed and comparable fidelity. Most importantly, it demonstrates much higher robustness to varying input quality, showing stronger generalization performance.

Low-Rank Adaptation (LoRA) fusion enables the composition of subject and style representations for controllable generation without retraining. However, existing approaches primarily operate through weight-level merging, without explicitly modeling how independently trained LoRAs interact in the shared parameter space. We adopt a geometric perspective on LoRA fusion, interpreting content and style LoRAs as occupying overlapping, non-orthogonal low-rank subspaces, where such overlap can lead to conflicting parameter updates that affect generation quality. This observation motivates us to reformulate LoRA fusion not merely as parameter combination, but as a problem of controlling how updates from overlapping subspaces are combined. Based on this insight, we propose Null Space Projection LoRA (NP-LoRA), a training-free framework that employs projection as a fusion operator to explicitly modulate cross-LoRA interactions. Specifically, NP-LoRA uses principal directions of the style LoRA to define a projection subspace and projects the content LoRA onto the complementary subspace (i.e., the null space of the style LoRA), suppressing interference along dominant style directions while preserving complementary information. To avoid the overly aggressive suppression of hard projection, we further formulate soft projection as a regularized optimization problem that balances content preservation against style-subspace suppression. This objective admits a closed-form solution, yielding a projection operator controlled by a single parameter that continuously interpolates between linear merging and hard projection. Extensive experiments across multiple pretrained LoRA pairs show that NP-LoRA achieves more balanced content-style composition compared to strong baselines, without requiring retraining.

Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a static font remains a labor-intensive process requiring expert typographic design and manual specification of glyph variation data. We introduce NIV (Neural Axis Variations), a method that automatically converts a static font into a fully functional variable font. Given glyph outlines and a set of desired design axes, NIV predicts per-point displacements. The model operates directly on vector glyph geometry and employs a novel Property Embedding mechanism that captures interactions between multiple axes, enabling consistent multi-axis variation within a unified framework. We train NIV on a newly constructed dataset derived from variable Google Fonts, comprising over one million variation tuples. The resulting model generalizes across unseen code points, unseen font styles, high-complexity CJK glyphs, and even out-of-distribution handwriting inputs. The generated outputs are standard variable font files supporting continuous interpolation via existing rendering engines. To facilitate research, we release the dataset, the complete training and inference implementation, and trained models. Beyond typography, our approach demonstrates how structured geometric objects with continuous parametric variation can be synthesized using neural deformations.

Task-oriented dexterous grasp generation aims to produce dexterous grasp poses that are both physically plausible and functionally suitable for specified manipulation tasks. Existing diffusion-based methods often address these two requirements in a decoupled manner: they first train a grasp diffusion model for task alignment and then rely on post-generation refinement to improve physical plausibility. However, this after-the-fact correction strategy applies physical plausibility guidance only once the grasp has already been generated, leaving the generation trajectory itself unguided by physical constraints and potentially leading to suboptimal grasps. To address this problem, we propose a novel framework that directly injects physical plausibility guidance into the denoising process of a task-aligned grasp diffusion model in a practical and effective manner, even when physical plausibility constraints are non-differentiable. This allows physical plausibility to shape grasp generation throughout denoising while preserving task alignment. Extensive experiments demonstrate the efficacy of our framework.

Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, making multi-view generation expensive at inference time. We introduce NAMVIS, a diffusion-free framework that reformulates multi-view image synthesis as geometry-conditioned next-scale autoregression. Instead of generating target views through repeated denoising, NAMVIS predicts discrete visual tokens through a small number of coarse-to-fine scale steps, while sampling all tokens within each scale and across target views in parallel. To anchor this generation process to explicit camera geometry, we propose Multi-scale Projective Pose Encoding, which injects source and target camera transformations into both target-view self-attention and source-to-target cross-attention at every resolution. NAMVIS further combines global conditioning with dense geometry-aware cross-attention, enabling the model to preserve source-view appearance while maintaining target-view consistency. Across Objaverse, GSO, and OmniObject3D, NAMVIS outperforms diffusion-based baselines in PSNR, SSIM, and LPIPS, while running over 3× faster than the evaluated diffusion baselines under the same evaluation setting. These results suggest that geometry-conditioned next-scale autoregression is a promising and efficient alternative to diffusion for sparse-view multi-view synthesis.

Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrade model quality. We introduce N-vium, a mixture-of-exits transformer that partially parallelizes computation across depth on standard hardware, increasing effective FLOPs per second rather than minimizing compute per token. N-vium attaches prediction heads at multiple depths and defines the next-token distribution as a learned mixture over these exits, with token-adaptive routing. This formulation strictly generalizes the standard transformer, which is recovered exactly when routing assigns zero mass to all intermediate heads. Sampling from the mixture is exact, and complete KV caches are recovered by deferring the upper-layer computation and batching it with later tokens. We pretrain N-vium at scales up to 1.5B parameters. Our largest model reaches 57.9% wall-clock speedup over a parameter- and data-matched standard transformer at no perplexity cost.

Motion generation has made substantial progress in synthesizing human motion from language, yet text alone remains ambiguous for specifying fine-grained spatial, temporal, and interaction details. Visual observations offer a powerful source of complementary context: even sparse images can reveal intermediate body states, object affordances, and interaction relations that are difficult to specify precisely in language. Whatmore, contextual constraints like visual, object, and partial-motion conditions can ground motion genetration with higher physical fidelity and controllability. We therefore study a unified multimodal context-aware framework for motion generation under diverse conditions. The key challenge is that heterogeneous conditions impose constraints at different spatial-temporal granularities and exhibit token- and phase-dependent relevance. Moreover, incorporating multiple modalities without a robust motion prior may entangle modality-specific semantics and cause negative transfer. In these regards, we combine global multimodal modulation with fine-grained motion token-level context selection, enabling the model to adaptively exploit relevant signals during generation. We further adopt progressive training that first learns a strong language-conditioned motion prior, then extends to multimodal context with different modality combinations. To support multimodal training, we introduce Mo900H, a large-scale benchmark integrating 21 motion datasets with over 900 hours of human motion. Our method reduces FID by 21.1% and 48.3% compared with SOTA methods on the HumanML3D and Mo900H datasets, while also improving motion captioning and enabling diverse multimodal-conditioned generation.

Judging image quality is not only ecologically relevant to everyday humans tasks, but also underpins many machine vision tasks such as image generation. This paper proposes a framework to understand the inherent perceptual space underlying image quality judgment in humans. We propose a multi-dimensional observer model that represents images as distributions in a latent perceptual space and that models human judgment as comparing noisy samples. Being constrained by neural representations in the primate ventral stream and fit to large-scale behavioral data, the model enables analysis of perceptual structure while matching the predictive power of existing metrics. Using this model, we find that the perceptual spaces needed to account for image quality judgment in humans are extremely low-dimensional compared to the image space even when considering its sparsity. The exact structure of the space (e.g., dimensionalities, information encoded) varies between low-level and high-level quality judgments, suggesting that, despite a shared retinal encoding in the beginning, humans selectively construct task-dependent perceptual spaces in visual decision making.

Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numerical and categorical structured data. However, they lack native support for unstructured modalities such as text and image, and rely on frozen, pretrained embeddings to process them. On established Multimodal Tabular Learning benchmarks, we show that tuning the embeddings to the task improves performance. Existing benchmarks, however, often focus on the mere co-occurrence of modalities; this leads to high variance across datasets and masks the benefits of task-specific tuning. To address this gap, we introduce MulTaBench, a benchmark of 40 datasets, split equally between image-tabular and text-tabular tasks. We focus on predictive tasks where the modalities provide complementary predictive signal, and where generic embeddings lose critical information, necessitating Target-Aware Representations that are aligned with the task. Our experimental results demonstrate that the gains from target-aware representation tuning generalize across both text and image modalities, several tabular learners, encoder scales, and embedding dimensions. MulTaBench constitutes the largest image-tabular benchmarking effort to date, spanning high-impact domains such as healthcare and e-commerce. It is designed to enable the research of novel architectures which incorporate joint modeling and target-aware representations, paving the way for the development of novel Multimodal Tabular Foundation Models.