NeurIPS 2026 Papers

Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bidirectional dependencies between language and motion, allowing early prediction errors to persist as fixed context and degrade both temporal coherence and cross-modal consistency. Masked discrete diffusion, which models sequences through iterative bidirectional prediction, offers a natural remedy. We therefore propose BiMoGen (Bidirectional Motion-text Generation), a unified masked discrete diffusion framework for bidirectional motion-text modeling. To stabilize training, we design Decoupled Uni- and Cross-Modal Training, in which masked pretraining first establishes cross-modal correspondence on paired motion-text sequences, after which supervised fine-tuning specializes the model for bidirectional generation. Masked diffusion nonetheless introduces its own source of error, as the model is trained on clean ground-truth context yet encounters self-generated and potentially erroneous context at inference, with errors committed under heavily masked states propagating through subsequent steps. We further introduce Generation-Aware Self-Correction that exposes the model to its own predictions during training and applies correction passes at early sampling steps to revise unreliably committed tokens. Extensive experiments on HumanML3D and KIT-ML demonstrate competitive performance on both tasks, validating the effectiveness of the proposed two-stage training and self-correction designs.

Reinforcement learning (RL) has emerged as a paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks. A fundamental limitation remains *the curse of diversity collapse*, where the objective formulation and optimization landscape inherently collapse the policy to a Dirac delta distribution. To address this challenge, we propose **DRIFT** (**D**ive**R**sity-**I**ncentivized Reinforcement **F**ine-**T**uning for Versatile Image Generation), an innovative framework that systematically incentivizes output diversity throughout the on-policy fine-tuning process, reconciling strong task alignment with high generation diversity to enhance versatility essential for applications that demand diverse candidate generations. We approach the problem across three representative perspectives: i) **sampling** a reward-concentrated subset that filters out reward outliers to prevent premature collapse; ii) **prompting** with stochastic variations to expand the conditioning space, and iii) **optimization** of the intra-group diversity with a potential-based reward shaping mechanism. Experimental results show that DRIFT exhibits clear Pareto dominance in task alignment and generation diversity, achieving 7.19\%$\sim$93.40\% higher diversity at matched alignment and 13.23\%$\sim$60.13\% higher alignment at matched diversity.

Existing underwater image enhancement (UIE) methods predominantly rely on unidirectional mapping, which lacks often results in over-enhancement or under-enhancement, without user-friendly controllability. To address this, we propose a paradigm of unsupervised trajectory learning for Omnidirectional Controllable UIE that treats restoration as a omnidirectional dynamical system, namely OC-UIE. The main purpose of our work is the realization of a consistent omnidirectional mapping across the representation space, which allows the model to master complex underwater dynamics beyond traditional unidirectional constraints. To implement this, we propose an Omnidirectional Training that optimizes over arbitrary source-target positions sampled from the data distribution. We decompose this purpose into two synergistic stages: first, Representation Trajectory Flattening is employed to organize intricate degradations into a unified adaptation axis; second, Trajectory Omnidirectional Integration is introduced to model the enhancement as a path integral over the resulting manifold. By optimizing only the relative shifts, OC-UIE isolates structural preservation from fluid degradation dynamics. This approach provides a controllable space for intensity-parameterized enhancement with state-of-the-art performance.

Semi-supervised medical image segmentation has gained increasing attention for its outstanding performance with limited annotations. Most methods follow the Mean Teacher framework, where Exponential Moving Average (EMA) is primarily used to stabilize predictions, while its potential roles beyond stabilization are largely overlooked. In this work, we interpret EMA as a first-order infinite impulse response (IIR) low-pass filter with inherent frequency selectivity, which enables the preservation of semantic information at different levels. Motivated by this insight, we propose a dual-EMA teachers framework, including a long-term teacher focusing on global structures and a short-term teacher emphasizing local details. Furthermore, to help the student better exploit the guidance from both teachers, a First-In-First-Out replay queue is designed to reuse historical high-confidence pseudo-labeled pairs, leveraging pseudo labels provided at different training stages and reducing the sensitivity to sampling bias. Meanwhile, we impose feature-level global semantic and local relation consistency constraints to alleviate the impact of noisy pseudo labels, facilitating robust transfer of complementary representations from dual teachers to the student model. Experiments on three public medical image datasets demonstrate that our method outperforms current SoTA methods, verifying its effectiveness.

Conditional diffusion models have emerged as a powerful paradigm for image restoration, leveraging pre-trained generative priors to recover high-fidelity details from degraded inputs. However, existing methods typically follow a static inference protocol, treating the degraded observation as a fixed control signal throughout sampling. This overlooks the potential of inference-time scaling, where guidance can be refined on the fly to improve output quality. Recent test-time optimization approaches that manipulate noise variables or denoising trajectories often compromise structural fidelity, while those targeting text embeddings lack the spatial precision needed for restoration. To address this, we propose Condition-Aware Test-Time Optimization (CATTO), a training-free method that iteratively refines the visual conditioning signal at inference time. CATTO performs reward-aligned condition refinement under the pre-trained prior to enhance perceptual quality. To avoid complex backpropagation, we design a gradient-free optimization strategy guided by a joint objective: maximizing a perceptual reward while enforcing trajectory- and condition-level consistency to explicitly preserve structural fidelity. This process is accelerated by optimizing within a low-dimensional frequency subspace and reusing reliable updates across nearby denoising steps. Experiments on image restoration benchmarks show that CATTO improves perceptual quality over strong diffusion-based baselines without updating model parameters.

Recent advancements in generative models have increasingly leveraged self-supervised visual representations to guide the synthesis process, significantly improving generation quality and semantic consistency. However, the reverse paradigm of harnessing the generative process to enhance visual representations remains largely underexplored. In this paper, we investigate the semantic properties of class tokens synthesized by generative models. We observe that generative models equipped with representation entanglement can generate class tokens that exhibit stronger discriminative capabilities than those extracted from the original pretrained visual models. Motivated by this observation, we propose a completely new Generation-to-Perception Knowledge Distillation (GPKD) framework, where our method generates class tokens as teacher signals to instill global semantics into the student model. To prevent the degradation of local details, we further incorporate a new masked patch-level distillation objective. This dual distillation strategy enhances global representations while mitigating the forgetting of local details, thereby producing more robust representations. Extensive experiments demonstrate that GPKD obtains consistent improvements across various downstream tasks compared with the base visual encoder, achieving a gain of more than 2\% in $k$-NN accuracy on ImageNet-1K.

Autoregressive graph generation is commonly formulated by linearizing a graph into a sequence via a flat walk over its nodes and edges. While effective for small molecular graphs, such representations do not scale gracefully to large, structurally complex biological graphs, where higher-order organization is both dense and functionally important. To address this limitation, we introduce a hierarchical graph abstraction together with a coarse-to-fine autoregressive pipeline that separates coarsening, tokenization, and decoding, and we study sequence constructions that expose hierarchy in the tokenization. Concretely, we compare a flat representation with two hierarchical alternatives: one that explicitly factorizes communities and their interactions, and another that induces hierarchy through a depth-first traversal. Across eight benchmarks, differences are modest on simpler domains but become pronounced on larger, more structurally complex settings such as cyclic peptides, proteins, and hard conditional-generation tasks. In these regimes, flat representations degrade in validity or distributional fidelity, while the traversal-based hierarchical representation is consistently more reliable; the explicitly factorized variant is generally weaker, suggesting that how hierarchy is exposed in the sequence matters. Finally, the same coarse-to-fine representation naturally enables motif-conditioned generation without architectural changes: on larger and more structurally complex graphs, conditioning on higher-level prefixes yields strong motif retention together with high validity, uniqueness, and novelty, whereas flat representations can exhibit severe mode collapse under comparable constraints.

Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, making simple resampling or perturbation insufficient for stress scenario generation. Existing outlier synthesis methods typically rely on sparse neighborhoods, low support latent regions, or classifier boundary crossings, which can be heuristic, unstable, and tied to specific modalities or architectures. We therefore propose Sinkhorn Boundary Outlier Generation (SBOG), a structured framework for latent-space outlier generation that couples Sinkhorn optimal transport geometry with distributionally robust boundary modeling. The resulting Sinkhorn-induced support cost guides the sampler toward weakly supported boundary regions, while semantic constraints prevent uncontrolled drift from the intended context, yielding controlled deviations from the in-distribution reference measure rather than arbitrary sparse-region samples. Experiments on time series anomaly generation and image outlier synthesis show that our framework produces informative, semantically controlled outliers and improves downstream robustness evaluation across modalities, providing a foundation for stress scenario generation beyond empirical support.

Image fingerprinting assigns each distributed copy a user-specific watermark for source tracing. Averaging collusion is a central threat: several recipients can average their differently marked copies to suppress each individual mark. Discrete bit-string watermarks are especially vulnerable because averaging drives bit evidence toward ambiguous decisions. We introduce **OrthoMark**, which maps user keys to near-orthogonal high-dimensional unit vectors and identifies colluders by cosine similarity to the extracted watermark direction. Under ideal averaging of $K$ vector keys, the normalized average preserves a cosine signal of order $1/\sqrt{K}$ for each colluder, while unrelated keys remain concentrated near zero. The spherical geometry of random directions gives a shifted-Beta null distribution for cosine similarities, enabling analytic per-key false-positive-rate control after validation on unwatermarked images. OrthoMark uses a neural watermark encoder and centered extractor trained with JND masking and a progressive curriculum for photometric, geometric, and collusion robustness. Experiments on MS-COCO, OpenImages, and AI-generated images show that OrthoMark maintains robust single-key detection under photometric and geometric distortions, and achieves the best strict all-colluder detection among evaluated methods, while preserving high visual quality.

Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation. Although flow matching places no restriction on the source distribution, most existing systems still inherit a standard Gaussian from diffusion models, and the source is rarely treated as an optimization target at this scale. Recent works have begun to revisit this choice through condition-dependent or learned sources, yet evidence that such designs are effective in modern text-to-image systems—with high-dimensional latents and tightly integrated conditioning—remains limited. In this work, we study condition-dependent source distributions for flow matching along three axes: _why_ source learning helps, through the lens of the intrinsic variance term in the flow-matching objective; _how_ to make it work in modern text-to-image systems, where variance-only regularization and directional source—target alignment are critical for stable end-to-end training; and _when_ it is most beneficial, by connecting source design to recent representational advances in generative modeling and identifying target representation regimes in which learning the source yields the largest gains. Extensive experiments across multiple text-to-image benchmarks, backbones, and scales demonstrate that principled source design yields consistent and robust improvements—including up to $\mathbf{3.01\times}$ faster convergence in FID and $\mathbf{2.48\times}$ in CLIP score—and outperforms representative prior conditioned-source and condition-aware coupling methods.

We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the target is singular, i.e.\ supported on a lower-dimensional data manifold. We also show that the one-step generative map associated with this flow is the unique solution of a simple conservation equation, which can be used to learn the map directly from samples. These autonomous flows and maps give a dynamical meaning to the flux constraint of Beckmann's transportation problem. Their construction provides a unifying framework that recovers, for instance, the closed-form Poisson-flow generative model and equilibrium matching with a quadratic flow-matching regression loss. We illustrate how this theory corrects inconsistencies in existing methods and improves their performance on large-scale image generation.

Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space. Existing VA-based image editors are typically training-based and domain-specific, limiting them to VA regions seen during training and making them brittle across datasets and domains. Meanwhile, despite producing high-quality edits, state-of-the-art foundation image editors are not continuous affective controllers: non-linear emotion representations in text encoders makes prompt interpolation poorly calibrated, so accurate and continuous VA control remains unreliable. To fill this gap, we introduce Barycentric Guidance, a training-free framework that turns pretrained foundation image editors into continuous affective controllers over VA, requiring no finetuning, extra data, or model changes. Our method maps any target VA point to simplex-based guidance weights, enabling VA controllability in three aspects: (i) accurate targeting of VA states, (ii) broad coverage across the VA plane, and (iii) continuous control. Across three face datasets, we improve VA target accuracy by 25–29\%, expand VA-plane coverage by 28–50\%, increase expression diversity by 29–49\%, over the strongest baseline. Unlike prior domain-specific methods, our training-free approach exploits the foundation model’s inherent versatility, enabling generalization beyond human faces to animals, artworks, and complex scenes within a single framework. Code will be made publicly available.

Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinforcement learning (RL). However, existing captioning-RL methods and evaluation metrics often emphasize a narrow notion of caption quality, inducing trade-offs across core dimensions of captioning. For example, utility-oriented objectives can encourage noisy, hallucinated, or overlong captions that improve downstream question answering while harming fluency, whereas arena-style objectives can favor fluent but generic descriptions with limited usefulness. To address this, we propose a more balanced RL framework that jointly optimizes utility-aware correctness, reference coverage, and linguistic quality. In order to effectively optimize the resulting continuous multi-objective reward formulation, we apply GDPO-style reward-decoupled normalization to continuous-valued captioning rewards and show that it improves performance over vanilla GRPO. Additionally, we introduce length-conditional reward masking, yielding a more suitable length penalty for captioning. Across LLaVA-1.5-7B and Qwen2.5-VL 3B and 7B base models, our method consistently improves caption quality, with peak gains of +13.6 DCScore, +9.0 CaptionQA, and +29.0 CapArena across different models.

Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic. While many proposed detection methods perform well under controlled settings, they often collapse when tested on real-world data. A potential root cause are subtle biases in the detectors' training data. As a result, detectors may rely on spurious correlations instead of learning true forensic artifacts. While a recent line of work has identified the problem, there is not yet an established protocol to evaluate how biased a detector actually is. In this work, we therefore take a step back: First, we discuss what it means for a detector to be biased, and how this differs from a lack of robustness. Second, we propose BIAS-ID, a transparent framework for analyzing and quantifying the presence of transformation biases in AI-generated image detectors. We validate our framework by performing an evaluation of six detectors across two datasets, revealing that several state-of-the-art detection methods are strongly affected by biases. Our results highlight the importance of bias-aware evaluation for developing reliable AI-generated image detectors.

Generative video models have achieved remarkable visual fidelity and temporal coherence, yet intentional camera control remains elusive. Existing frameworks treat camera motion as a byproduct of pixel synthesis, producing trajectories that are stochastic, spatially inconsistent, and indifferent to the human subject driving the scene. In this work, we present Auteur, a method for language-driven, human-centric camera framing in generative video. Our core insight is that professional filmmakers conceive shots not as world-space trajectories but as framings defined relative to the actor, encoding shot size, angle, and composition as functions of human pose and motion. We formalize this intuition as a human-centric camera parameterization and introduce a Domain-Specific Language (DSL) that is convertible to standard 6-DoF camera parameters. A fine-tuned multimodal large language model then acts as a virtual director, mapping natural language descriptions and coarse human motion to sparse DSL keyframes that are deterministically inter-polated into continuous camera trajectories, which are then provided as input to video generators. We train and evaluate Auteur on a new dataset of 30K aligned text, human motion, and DSL-annotated camera trajectories drawn from procedural synthesis and real-world movie footage from the CondensedMovies dataset. Auteur enables cinematographic framing of human-centered scenes, a capability largely absent in prior generative models. To assess this behavior, we propose new framing-focused metrics, and our experiments show that Auteur consistently outperforms existing methods.

Instruction-driven mask-free image editing with large-scale Diffusion Transformers (DiTs) has enabled high-fidelity local edits while maintaining global consistency. However, these models incur a prohibitive inference cost due to a pronounced semantic-–computation mismatch, where dense computation is performed over all high-resolution tokens regardless of localized editing instructions. Our analysis reveals that non-edit regions converge early with highly stable block-level outputs, exhibiting a distinct spatio-temporal asymmetry. Furthermore, we identify a critical system-level challenge that introducing such asymmetric computation into pipeline-parallel environments for ultra-large models triggers severe workload imbalance and pipeline bubbles, preventing algorithmic savings from translating into actual latency gains. To address these issues, we propose AsymPipe, a training--free algorithm–-system co-design framework that accelerates large-scale DiTs editing. The framework identifies edit versus non-edit regions on the fly using a zero-overhead velocity-based probe that reuses the predicted velocity field from the existing denoising process. For non-edit regions, AsymPipe caches and reuses block-level outputs, while for edit regions, it introduces a source-aware sparse attention module to precisely eliminate redundant contributions from static text and image sources. Finally, to bridge the gap between algorithmic optimization and hardware speedup, the framework incorporates a load-balanced token rearrangement scheme. This scheme deconstructs the original spatial topology to distribute tokens evenly across devices, physically restoring compute balance in pipeline-parallel systems. On HunyuanImage-3.0-Instruct and Qwen-Image-Edit-2511, AsymPipe achieves end-to-end speedups of 2.862x (PSNR 33.69) and 2.707x (PSNR 29.74), respectively, delivering substantial acceleration with virtually no loss in editing quality. Our anonymized source code is available at https://anonymous.4open.science/r/AsymPipe-6553.

Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors. In many applications, especially in scientific domains, such models are attractive to explore conditions for which real samples are rare, expensive, or not yet observed. However, this creates a circularity for evaluation: standard conditional quality metrics require a reference target distribution, but in the extrapolative regime that distribution is unavailable by definition. We address this problem with a post-hoc, per-sample trust score for assessing conditional samples using only the training distribution. The score combines two estimable quantities: global realism, measuring compatibility with the real data manifold, and attribute-wise faithfulness, measuring whether a sample is closer to the requested attributes than to plausible alternatives. We show that the score can recover meaningful comparisons across extrapolated generations, under a mild coverage condition on the observed attributes. These comparisons enable effective filtering, ranking, and abstention of generations and can be used directly on off-the-shelf pretrained models. In biological imaging, selected samples preserve real morphological structure better and improve downstream predictive performance, while similar gains are observed on controlled vision benchmarks. Finally, we show how the score can be applied during generation, enabling abstention before full decoding.

Today, we improve models by training and evaluating them on problems at the frontier of their abilities. Creating such problems is itself a demanding task, requiring the ability to probe model limits and generalize beyond existing question distributions. It also means placing problems at a precise difficulty level, which requires understanding what it takes to solve them. In short, generating problems calibrated to a model's current frontier demands capability beyond it, an increasingly burdensome constraint as models improve. Our key insight is that we can leverage this constraint to our advantage: a model that can generate problems consistently calibrated to a given frontier must possess capability beyond it. Accordingly, we present Ask-E, an environment that benchmarks and trains models on their ability to write questions at a given skill level, rather than answer them. Concretely, we define target skill levels as ranges bounded by the capabilities of two existing language models. A generated question is successfully calibrated if exactly one of the two models can solve it, placing it precisely within the target range and differentiating the capabilities of these models. Ask-E serves both as a benchmark and a training environment, where models generate problems calibrated to a variety of skill levels. We find that even frontier models achieve below 50\% calibration on the benchmark, leaving significant headroom to measure future progress. We also show that training on this environment leads to improvements across a number of downstream math benchmarks even with no new math data, no interaction with stronger models, and no correctness-based reward.

Reward guidance algorithms steer a learned generative process toward the reward-tilted measure at inference time. While empirically powerful, these methods are prone to reward hacking: the guided model over-optimizes the reward at the cost of fidelity to the learned distribution. Prior work has attributed this to the complexity of neural reward functions or implicit biases in diffusion training, but its fundamental origins remain poorly understood. We show that reward hacking arises from an approximation made in most practical implementations of reward-guided diffusion---finite-particle plug-in estimation of the Doob $h$-function---even in the simplest non-trivial settings of Gaussian and Gaussian mixture targets with quadratic rewards. In closed form, we isolate two distinct failure modes of the plug-in estimator: it leads to reward hacking within each mode and it cannot select high-reward modes. We propose a closed-form reward damping schedule that corrects the within-mode bias with no additional compute, and clarify the role of best-of-$n$ sampling in compensating for the mode selection failure. Experiments on Gaussian mixture targets, a 2D checkerboard, and FLUX.1 text-to-image generation confirm that our theoretical insights carry over to practical settings.