NeurIPS 2026 论文列表

Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates, leading to overconfident predictions. Conformal Prediction (CP) offers a principled framework for uncertainty quantification, yielding prediction sets with rigorous coverage guarantees. Recent work has proposed integrating conformal objectives into training, optimizing for overall set size. Nevertheless, shaping the prediction sets in a class-conditional manner is not straightforward and typically requires prior knowledge of the data distribution. While class-conditional efficiency has been studied at the post-hoc calibration level (e.g., via class-conditional scoring or calibration partitioning), no prior conformal training method explicitly addresses it. To fill this gap, we introduce Class Adaptive Conformal Training (CaCT), which formulates conformal training as an augmented Lagrangian optimization problem that adaptively shapes prediction sets class-conditionally without relying on parametric distributional assumptions, beyond a user-specified global coverage constraint. Experiments on multiple benchmark datasets, including standard and long-tailed image recognition as well as text classification, demonstrate that CaCT consistently outperforms prior conformal training methods, reducing prediction set size while maintaining the desired coverage guarantees.

Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together, these elements demand a level of fine-grained control beyond current text-to-video models. Existing work addresses each axis in isolation: multi-subject personalization, temporal control, multi-shot synthesis, or camera control; no prior framework jointly integrates all four. We present $\textit{CineOrchestra}$, a unified video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously. Our key insight is that these heterogeneous cinematic elements share a fundamental structure: each is an entity acting over a specific temporal interval, which can therefore all be expressed through one shared structure of entity-centric conditioning primitives, augmented with reference images for visual entities. This formulation reduces the architectural challenge to a single positional encoding problem, which we solve with two parameter-free coordinated rotary embeddings: (i) an interval-sampled temporal RoPE that yields consistent attention behavior across events of dramatically varying duration, and (ii) a 2D entity-temporal cross-attention RoPE that disambiguates per-entity conditions and routes each to its corresponding spatiotemporal region. On two new benchmarks, $\textit{CineOrchestra}$ outperforms six per-axis specialists on dense caption following and shot-transition timing, with consistent gains in a pairwise user study and component ablations.

Imagine Mr. Bean stepping into Tom and Jerry--can a video model generate interactions that stay faithful to each character's identity, behavior, and style across worlds? We present MiMix, a unified framework for multi-character text-to-video generation that goes beyond visual appearance to capture character-specific personas generalizing to unseen scenes and partners. To evaluate this, we introduce the Cross-Character Generalization Benchmark, a social stress test placing each character in novel scenes with previously uncoexistent partners. MiMix combines Cross-Character Embedding (CCE), which disentangles identity and behavior via text-aligned annotations, with Cross-Character Augmentation (CCA), which synthesizes cross-style training data while preserving native appearance. Experiments show consistent gains in identity fidelity, interaction quality, and style consistency over prior personalized and general video models.Additional results and videos are available on our project page: https://mi-mi-x.github.io.

Compositional prompts in text-to-molecule generation often specify several structural constraints at once, such as scaffolds, functional groups, substituents, and finer modifiers. Existing molecular latent diffusion models typically inject the full prompt once and apply it uniformly throughout denoising, which can cause later constraints to overwrite or distort earlier structural commitments. We propose **Chain-of-Generation (COG)**, a training-free progressive latent diffusion strategy for text-guided molecular design. Technically, CoG realizes progressive generation as multi-stage latent denoising over cumulative molecular sub-prompts: it factorizes a prompt into chemistry-informed coarse-to-fine segments, conditions each stage on the cumulative sub-prompt, and continues denoising from the previous-stage latent using a fixed partial-denoising schedule. This turns one-shot conditional generation into a sequence of stage-wise denoising steps that help retain earlier structure while introducing later constraints. Across benchmark, out-of-distribution, and ablation settings, CoG improves structural prompt fidelity over one-shot conditioning on the tested latent graph-diffusion backbones.

Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask $y$ for a given image $x$. In this work, we first reveal the latent nature of artifacts and thus reinterpret IML as a latent-variable problem, $P(y| x)=\int P(y|z) P(z|x)dz$, where $z$ denotes the artifacts. Following this interpretation, we pinpoint the cause for the current IML models' insufficiency as their implicit artifacts modeling strategy, highlighting the necessity of modeling $z$ in an explicit manner. Without direct labels, feature disentanglement is the most appropriate solution for this explicit modeling. Accordingly, we propose a two-stage learning paradigm with the Pairwise Artifacts Learning (PAL) and Standard Localization (SL) phases to estimate $P(z|x)$ and $P (y|z)$ via edit relations. To support our edit-relation-based learning, we further curate EditGroup-45K, a source-anchored dataset organized into edit groups for pair construction. Extensive experiments show that our PAL paradigm yields consistent improvements across diverse IML architectures, and empirical analyses further verify that PAL does capture artifacts explicitly through feature disentanglement. Code and dataset will be publicly available.

Passive image provenance asks whether pixels alone can reveal where an image came from: a human, an aggregate AI class, or a particular generator. This becomes a robustness problem once a source image can be edited before the verifier sees it. We study the problem as source--target verification under adversarial distribution shift. Our first result gives the exact best-case limit for any image-only verifier: the largest robust target-acceptance gap equals the minimum total-variation distance between the target distribution and the set of attacked source distributions. This quantity depends on the source, target, and edit class, not on the verifier architecture. Our second result explains why deployed public verifiers can fail before this statistical limit is reached. If the verifier can be emulated on the attack region to error $\varepsilon$, then a surrogate black-box attack reaches target acceptance within $2\varepsilon$ plus optimization error of the white-box optimum; score-revealing logistic and softmax heads over public features are identifiable, and approximate score access gives stable recovery bounds. Experiments on same-prompt real/diffusion benchmarks support this separation. Public CLIP-based interfaces collapse under targeted attacks, and stronger clean or adversarial retraining does not restore positive operational separation. In our evaluated settings, positive empirical upper bounds on the robust gap appear only under private low-bandwidth binary interfaces with abstention. The main lesson is simple: robust passive provenance requires both a source--target statistical analysis and an interface-aware evaluation of what the verifier reveals.

Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp LiDAR contours, yielding edge depths that back-project to points floating between surfaces. We identify this as a major, directly correctable decoder bottleneck and introduce CRISP: a pixel-space diffusion decoder with a backbone-agnostic latent adapter, DiT-based denoiser, and support mask predictor. CRISP replaces convolutional decoders commonly inherited from image and video VAEs while keeping the encoder and latent generator fixed. Across KITTI-360, SemanticKITTI, and nuScenes, replacing only the decoder reduces FSVD/FPVD by 50.5% on average across frozen backbones; for generic video VAEs, the reductions reach 71%/74%. On the LiDAR-native LiDM backbone, FRID drops by 71%, with the largest gains at depth discontinuities. Inside a pretrained LiDM world model, the same zero-shot replacement improves FSVD by 15.5%, narrowing the sim-to-real gap. Code and checkpoints will be released upon acceptance.

Achieving high-fidelity object-level control in diffusion transformers remains a significant challenge despite the introduction of structural priors like depth and Canny maps. Current object-level conditional generation methods frequently suffer from visual artifacts and struggle to maintain precise control over objects within small localized regions. To address these limitations, we propose Cascaded Object-Level Latent Refinement (COLLAR), a training-free framework that progressively optimizes object-level features via the Field-of-View (FoV) expansion. First, we propose the Cross-Scale Semantic Alignment (CSSA) module to address spatial-semantic gaps by injecting object-level features into extended-FoV branches via attention mechanisms. To further optimize these features, the Cyclic Feature Injection (CFI) module introduces a reciprocal background feedback mechanism. It leverages a frequency-based adaptive strategy to selectively update the global backbone with context-aligned local information. Finally, the extended-FoV branch serves as a hub for feature optimization, ensuring that object-level features are integrated into the global generation process without compromising final image quality. Extensive experiments on the COCO-MIG and COCO-POS benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods across semantic alignment, image quality, and spatial fidelity.

Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, layouts, or semantics from the style reference appear in the generated output. Although prior data-driven and training-free methods can reduce leakage, they often face a leakage--degradation dilemma: stronger content suppression may weaken style fidelity, while richer style preservation may reintroduce unwanted reference content. We identify this dilemma across the full style-transfer pipeline, including feature separation, feature-space grounding, and diffusion generation. To address these issues, we propose CLeaR, a training-free framework for content-leakage-resistant style transfer. CLeaR first uses Orthogonal Subspace Projection to define content-reduced style targets in each vision foundation model (VFM) feature space. It then performs Ensemble Inversion, which optimizes a shared pixel-space style anchor satisfying style constraints across multiple VFMs. Finally, Energy-Guided Calibration maintains style alignment during diffusion sampling by steering the denoising trajectory toward the ensemble-defined style manifold. We further provide a theoretical analysis showing that the style-anchor estimation error decreases with the number of VFMs. Experiments on StyleBench demonstrate that CLeaR improves style alignment, reduces content leakage, and achieves better LLM-as-Judge evaluation compared with existing methods.

Robust visual recognition under degraded imaging conditions is critical for real-world vision systems.Task-oriented image restoration addresses this problem by enhancing degraded inputs before recognition, but it must preserve natural visual appearance without disrupting the evidence used by downstream recognizers.Existing optimization strategies struggle to satisfy these requirements simultaneously.Restoration driven mainly by visual quality may weaken task-critical cues or introduce texture shifts that are visually plausible but poorly matched to the recognizer.Directly tuning restoration networks with recognition loss is also insufficient, as the restoration model may exploit recognizer-specific shortcut cues and produce visually unnatural artifacts while improving task scores.We propose Causal Mediated Unrolling (CaMeU), an optimization-based framework for task-oriented image restoration with frozen recognition models.Starting from a joint optimization objective, the proposed framework derives a Half-Quadratic Splitting formulation that separates restoration into alternating task-driven and prior-guided updates.The task-driven update introduces an explicit mediator for front-door guided optimization, reducing shortcut bias induced by the recognizer.The prior-guided update performs manifold projection, pulling the intermediate result back toward the natural-image manifold to preserve visual quality.Through this mediated alternating process, the restored image is progressively optimized for both recognition performance and visual quality.Experiments on VOC Haze, VOC Dark, and degraded CUB-200-2011 show that CaMeU consistently improves downstream recognition while reducing visual artifacts.

Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic optimization, which can break down when the target 3D shape does not admit a feasible structure under predefined constraints, or generate brick sequences without explicitly modeling the underlying 3D geometry and assembly relations. In this work, we present BrickAnything, a geometry-conditioned autoregressive framework for generating buildable brick structures from diverse 3D representations. BrickAnything uses point clouds as a unified geometric interface and predicts brick sequences that reconstruct the target shape under assembly constraints. To model structural dependencies among bricks, we introduce a structure-aware tree tokenization, which represents brick structures through local attachment relations. This formulation makes sequence generation more consistent with the physical construction process, and reduces invalid intermediate states. We further introduce preference-based alignment post-training, validity-constrained decoding and adaptive rollback to improve buildability objectives such as stability and geometric fidelity. Extensive experiments demonstrate that BrickAnything produces geometrically faithful and physically realizable brick structures, and that the proposed tokenization effectively reduces rollback and regeneration compared with conventional ordering strategies.

Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-data regime: improving data quality typically comes at the cost of increased memorization of training samples, thereby weakening privacy protection. This tradeoff arises because small training sets make it difficult for dataset-specific generative models to distinguish generalizable structure from sample-specific patterns. To address this, we propose DiffICL, which formulates tabular data generation as an in-context learning problem. Instead of fitting each dataset from scratch, DiffICL leverages pretrained structural priors learned from a large collection of datasets, enabling it to infer data distributions from limited context rather than memorizing individual samples. We evaluate DiffICL on 14 real-world datasets. Results show that DiffICL improves both data quality and privacy, and generates synthetic data that provides effective data augmentation. Our findings suggest that the quality–privacy tradeoff can be improved through better training paradigms.

Self-supervised learning (SSL) is increasingly applied to scientific imaging domains, such as microscopy, where labels are scarce but noisy data is abundant. These domains exhibit significant signal-dependent noise, which can create a representational shortcut: since both augmented views derive from the same noisy image, the encoder can boost view agreement by also encoding noise patterns rather than semantic content. To enable noise-robust representation learning without knowledge of underlying noise models, we introduce Self-Aligned Noise Augmentation (SEANA), a drop-in module that generates noise-aware SSL views. SEANA learns an invertible variance-stabilizing transform (VST) from noisy data, then estimates the clean signal and resamples independent noise in learned VST space, requiring no clean targets, no dataset-specific denoiser training or use at inference, and no changes to the SSL objective or encoder. On CIFAR-10 and ImageNet-100 under synthetic Gaussian, Poisson–Gaussian, and multiplicative noise, SEANA improves clean-test linear-probe accuracy across contrastive/non-contrastive SSL methods, indicating higher-quality representations. On real fluorescence microscopy, SEANA improves Jurkat cell-cycle stage classification by up to 28.6pp over standard SSL and 27.5pp over denoiser-preprocessed baselines. In both settings, SEANA outperforms the denoiser-preprocessed SSL pipeline, demonstrating that learned VST-space noise-aligned resampling yields more robust representations than denoising alone. Code will be made publicly available.

Progressive image compression reconstructs images from prefixes of a single bitstream and therefore involves sequential latent inference under partial observations. Existing trit-plane codecs mainly treat decoded trits as discrete symbols for probability prediction, without fully exploiting their role as interval constraints on the underlying continuous latents. We propose bound-conditioned latent inference (BLI), a framework that reformulates progressive trit-plane coding as sequential latent inference under shrinking interval constraints. At each coding step, the trits decoded so far define lower and upper bounds for each latent element. BLI uses the center and width of these bounds, together with hyperprior information shared by the encoder and decoder, to jointly refine the Gaussian parameters $(\mu,\sigma)$ for entropy modeling and the latent estimate $\hat{y}$ for reconstruction. As additional trits are decoded, the feasible intervals shrink monotonically, yielding tighter constraints for the next inference step and enabling flexible in-plane refinement schedules. Experiments on Kodak, CLIC, and JPEG-AI show that BLI achieves state-of-the-art rate-distortion performance among progressive image codecs, reducing BD-rate by $17.80\%$ over DPICT and by $7.98\%$ over CTC on Kodak, while replacing CTC's plane-specific predictors with a shared model that uses about $5.3\times$ fewer parameters and achieves lower latency than CTC.

Text-to-image diffusion models can synthesize high-quality images, yet the outcome is notoriously sensitive to the random seed: different initial seeds often yield large variations in image quality and prompt–image alignment. We revisit this “seed effect” and show that attention dynamics over prompt core tokens, the content-bearing words, measured during the first few denoising steps, strongly predict final generation quality. Building on this observation, we introduce Attention-Based Seed Selection (ABSS), a training-free, plug-and-play method that ranks seeds for a given prompt by leveraging cross-attention to core tokens during the denoising process. ABSS requires no finetuning and does not alter the initial noise; it scores and ranks all candidate seeds, keeps only the top-k for full generation, and discards the rest, without relying on a fixed accept/reject threshold. Operating purely at inference time, ABSS can serve as a lightweight pre-selection add-on for existing seed-optimization pipelines, enabling additional gains. Across three benchmarks, extensive experiments show that ABSS enables consistent improvements in text–image alignment and visual quality for Stable Diffusion variants, as corroborated by human preference and alignment metrics.

Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain decoding can be boosted by augmenting small fMRI datasets with synthetic data generated by a pretrained model of fMRI responses to stimuli. We use TRIBE v2, a large encoding model pretrained on more than 1000\,hours of fMRI responses to video, audio and language. For each dataset, we evaluate systematic grids that show how the performance of image decoders varies with the amount of synthetic data used for training. Our results, based on two datasets (the 7T fMRI Natural Scenes Dataset and 3T fMRI BOLD5000), show up to 68\% improvement in Top-10 image-retrieval accuracy compared to decoders trained only on real data. Importantly, the proportion of augmented data required to reach a given image decoding performance needs to be adjusted depending on the data source. Surprisingly, image decoders trained exclusively on synthetic fMRI perform above chance in some settings, suggesting that TRIBE v2 can support zero-shot fMRI-to-image decoding. Together, these results show how large-scale models of the fMRI responses to sight, sound and language may provide a foundation to improve the data efficiency for image decoding.

Recently several methods have shown promise for purely in silico “de novo” design of antibodies which bind to drug targets. The methods which have shown success in vitro propose candidates via either a generative diffusion model or hallucination-based sequence optimization, and then filter these candidates with a structure predictor model. Hallucination methods rely on compute-intensive backpropagation through a structure predictor model for each candidate, and the generative methods require expensive structure re-prediction and filtering of many (often mostly non-passing) candidates, limiting their application to test-time generation of large libraries. Given the highly variable per-drug-target hit rates of current methods, screening large libraries is a well established route to improved antibody candidate discovery. Thus, we propose an alternative approach, Bonobo, which instead formulates this problem as black-box optimization of a per-drug-target generative model, amortizing candidate generation into training. This is accomplished by using a structure predictor as a reward signal to directly train a GFlowNet generative model. We show that this approach can match or exceed the in silico metrics of state-of-the-art approaches, while allowing for dramatically more efficient generation of diverse and arbitrarily large numbers of antibody binder candidates.

Adversarial perturbation defenses protect images from generative editing by adding imperceptible noise. Yet none of these methods have been evaluated against AI nudification, despite AI-generated non-consensual intimate imagery (AIG-NCII) being among the most documented harms of generative AI and produced largely through inpainting. We argue this is a problem-formulation failure: prior threat models do not correspond to nudification attacks, and the misalignment is structural, spanning mask shape, prompt distribution, and success criterion which causes existing evaluations to systematically overestimate protection effectiveness. We reformulate adversarial inpainting protection around this documented harm. We introduce BodyBench, a benchmark of 648 fully synthetic clothed adult subjects, each paired with 11 mask variants spanning body, face, context, and dilation regions, along with nudification and redressing attack prompts. We propose three harm-aligned metrics: sexualization, naturalness, and recognizability that jointly determine whether an inpainted output constitutes a successful nudification attack. Auditing four recent protection methods (PhotoGuard, DiffusionGuard, AdvPaint, DiffVax) on BodyBench, we find that protection succeeds in only one configuration of one method, and only under exact mask alignment between defender and attacker---a condition no real defender can guarantee and one that we show is easily always bypassed. We also show that standard metrics such as PSNR are essentially uncorrelated with nudification protection. BodyBench makes this misalignment measurable and provides a foundation for defenses grounded in documented harm.

We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $\mathbf{2^{256}}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256$\times$256, BitDance achieves an FID of **1.24**, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using **5.4$\times$** fewer parameters (260M) and achieving **8.7$\times$** speedup. For text-to-image generation and image editing, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024$\times$1024 images, BitDance achieves a speedup of over **30$\times$** compared to prior AR models. We release code and models to facilitate further research on AR foundation models.