NeurIPS 2026 论文列表

JEPAs often regularize one-view embeddings toward an isotropic Gaussian, implicitly baking Euclidean symmetry into the representation. We show that this is not merely a benign default. For a known structured downstream geometry $H\succ0$, the minimax and maximum-entropy covariance under a Hamiltonian energy budget is $(c/d)H^{-1}$, and Euclidean isotropy incurs a closed-form price of isotropy. More importantly, when the downstream geometry is unknown, no geometry-independent fixed marginal target is canonical: every fixed covariance shape can be maximally misaligned for some structured geometry. We further show that even oracle one-view marginals do not identify the JEPA view-to-view predictive coupling. These results suggest that the structural bias in JEPAs should enter the cross-view coupling rather than a fixed encoder marginal. We instantiate this principle with \textbf{HamJEPA}, which encodes each view as a phase-space state $(q,p)$ and predicts view-to-view transitions with a learned Hamiltonian leapfrog map, while non-isotropic scale and spectral floors prevent collapse. In a deliberately headless token protocol, HamJEPA improves over SIGReg on CIFAR-100 by $+4.89$ kNN@20 and $+3.52$ linear-probe points at 30 epochs, and by $+6.45$ kNN@20 and $+10.64$ linear-probe points at 80 epochs, while a matched MLP predictor ablation shows that the symplectic coupling is the ingredient driving the neighborhood-geometry gain. On ImageNet-100, HamJEPA-$q$ improves by $+4.82$ kNN@20 and $+7.52$ linear-probe points at 45 epochs.

Sparse Autoencoders (SAEs) are increasingly used to interpret foundation models, but their role as an actionable intervention space remains less understood, especially in vision. We study whether sparse visual features can be used not only for post-hoc analysis, but also to steer frozen vision-language models. We introduce \textbf{Visual Sparse Steering (\ours)}, a label-free method that trains a top-$k$ SAE on unlabeled activations from a frozen CLIP image encoder and, at test time, constructs an interpretable steering vector by amplifying the input's active sparse features and decoding the induced change. We show that this procedure admits a closed-form decomposition as \emph{centroid-deviation steering}: each input is moved along its deviation from the SAE-learned centroid. The residual term is controlled exactly by the SAE's per-sample reconstruction error, measured by FVU, yielding an FVU-based residual bound and motivating a reliability gate that falls back to zero-shot CLIP when SAE reconstruction is unreliable. With target-domain SAEs trained on unlabeled CLIP image-encoder activations, \ours improves zero-shot accuracy across nine image-classification datasets, achieving gains up to $+4.12\%$ with less than $0.1\%$ additional inference compute. Finally, a controlled upper-bound study, \oursplus, shows that selective amplification of sparse features can yield gains up to $+21.44\%$, exposing a \emph{reconstruction-vs-task saliency gap}: features salient for reconstruction need not align with features useful for downstream prediction.

We prove the identifiability of deep generative models (DGMs) with Piecewise-Affine (PWA) decoders and Gaussian Mixture Model (GMM) priors, in a purely unsupervised setting. We introduce three algebraic contrast principles for symmetry breaking: *domain contrast*, which trivializes the mixture symmetry group; *mechanism contrast*, which ensures every decoder branch is witnessed by a unique boundary; and *interaction contrast*, which forbids parameter conspiracies between latent components and decoder branches. Together they exploit the interplay between the discrete combinatorics of the PWA map and the continuous symmetry structure of the latent GMM. Continuity is replaced by algebraic symmetry conditions; injectivity is decoupled from structural identification and required only for pointwise inversion. Our results form a hierarchy: from *law identifiability* (LID; latent distribution up to a global affine map) through *map identifiability* (MID; decoder up to the same map) to *posterior* and *pointwise identifiability*. The ICA-form ambiguity emerges under diagonal covariance conditions; classical ICA is a further specialization under independence. Assumptions are only on the data-generating process, not on learning methods, except for the interaction contrast.

Binary diffusion models typically require a massive number of function evaluations (NFEs) to generate high-quality samples, making practical inference computationally expensive. Reducing NFEs while preserving sample quality without relying on distillation or additional training remains a significant challenge. However, existing binary diffusion models sequentially define a discrete one-step forward path and subsequently derive the reverse posterior. In low-NFE scenarios that require cross-step sampling, these models incorrectly approximate the true multi-step likelihood using a single-step likelihood formulation, which severely degrades sample quality. To address this fundamental limitation and completely decouple the generative dynamics from fixed discrete time steps, we propose Bernoulli Flow Models (BFM). Rather than building upon sequential one-step Markov diffusion chains, we predefine a unified continuous global Bernoulli probability flow path between data distributions and pure noise, from which we derive analytical closed-form posterior transitions over arbitrary time intervals. Consequently, reducing the inference NFE in BFM is no longer an approximation of skipped discrete steps; it simply requires re-evaluating the analytical posterior on the new time intervals. This eliminates the structural training-inference mismatch inherent to discrete chains, yielding strictly self-consistent low-NFE sampling. Experimental results show that BFM is highly robust to aggressive NFE reduction. On the LSUN Churches 256x256 dataset, a 256-step-trained BFM achieves an FID of 9.31 when sampled with only 16 steps, whereas the state-of-the-art discrete baseline severely degrades to 204.10. BFM also remains competitive with both continuous and discrete generative baselines under standard full-step inference. Ultimately, these results establish BFM as a theoretically rigorous, self-consistent, and practically effective framework for fast binary data generation.

We trace factual sycophancy across three post-training pipelines (OLMo 3 7B Think and Instruct, Llama 3.1 8B Instruct, Tulu 3) on AMPS and MedQuAD with a dual-track evaluation: a GPT-4o-judged generative track and a log-probability track reporting $\Delta$LogOdds separately under \emph{in-context} rebuttals and \emph{preemptive} wrong-answer assertions. Decomposing by challenge type $\times$ context reveals three patterns. (1) \textbf{Challenge type:} the sycophantic shift is alternative-conditioned, so simple pushback with no asserted answer produces no shift, while ethos/justification/citation challenges produce substantial positive shifts. (2) \textbf{Context:} every base model's sycophantic shift concentrates preemptively, with weak or defensive in-context response. On computational, IC diverges into four pipeline-specific endpoints. On medical, the same four pipelines converge with no defensive IC developing on any recipe. (3) \textbf{Behavioral vs log-probability:} post-training produces a \emph{surface-predictive dissociation}: matched-subset behavioral flip rate drops significantly on identical items while preemptive $\Delta$LogOdds on those same items grows.

Aligning multimodal generative models with human preferences demands reward signals that respect the compositional, multi-dimensional structure of human judgment. Prevailing RLHF approaches reduce this structure to scalar or pairwise labels, collapsing nuanced preferences into opaque parametric proxies and exposing vulnerabilities to reward hacking. While recent Rubrics-as-Reward (RaR) methods attempt to recover this structure through explicit criteria, generating rubrics that are simultaneously reliable, scalable, and data-efficient remains an open problem. We introduce Auto-Rubric as Reward (ARR), a framework that reframes reward modeling from implicit weight optimization to explicit, criteria-based decomposition. Before any pairwise comparison, ARR externalizes a VLM’s internalized preference knowledge as prompt-specific rubrics, translating holistic intent into independently verifiable quality dimensions. This conversion of implicit preference structure into inspectable, interpretable constraints substantially suppresses evaluation biases including positional bias, enabling both zero-shot deployment and few-shot conditioning on minimal supervision. To extend these gains into generative training, we propose Rubric Policy Optimization (RPO), which distills ARR’s structured multi-dimensional evaluation into a robust binary reward, replacing opaque scalar regression with rubric-conditioned preference decisions that stabilize policy gradients. On text-to-image generation and image editing benchmarks, ARR-RPO outperforms pairwise reward models and VLM judges, demonstrating that explicitly externalizing implicit preference knowledge into structured rubrics achieves more reliable, data-efficient multimodal alignment, revealing that the bottleneck is the absence of a factorized interface, not a deficit of knowledge.

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.

Physics-Informed Neural Networks (PINNs) struggle to capture high-frequency components of PDE solutions, a failure mode known as spectral bias. Most existing fixes adjust the loss, the sampling, or the input encoding, but the network itself remains agnostic to the equation it is supposed to solve. We take a different route and embed physics priors into the architecture. The network's internal spectrum is shaped to match the spectrum of the target PDE through three components that are trained end-to-end: a bounded coordinate warping that contracts regions where the solution varies sharply, a spectral attention layer driven by the PDE residual that emphasizes physically active Fourier modes, and an inter-harmonic gate that lets low-frequency channels modulate high-frequency ones. We test the design on four forward PDEs (Helmholtz, Wave, Klein--Gordon, Burgers), the Burgers inverse problem, and seismic reconstruction, including Gulf of Mexico field data. Across these tasks, the architecture clearly outperforms seven PINN baselines on accuracy and produces noticeably fewer artifacts. Code will be made publicly available upon acceptance.

Denoising generative models predict clean data ($x$), noise ($\epsilon$), or velocity ($v$), and are trained with a squared loss that can be computed in any of these three spaces. Prior work treats these choices as interchangeable up to loss reweighting, but practitioners observe sharp qualitative differences that remain unexplained. We systematically decouple the \emph{prediction space}, what the network outputs, from the \emph{loss space}, where supervision is applied, creating a $3{\times}3$ design matrix. We show that loss-space changes only reweight how much each noise level contributes to training, while prediction-space changes alter what the network is asked to learn: two fundamentally different mechanisms. This decoupling exposes two regimes. Without a representational bottleneck, the loss choice primarily controls denoising quality (MSE) while the prediction choice primarily controls sample quality (FID). The reason is that ODE sampling must convert the network's output back into a clean image, and this conversion amplifies errors differently across prediction types, turning sub-one-percent gaps in training error into 22-fold gaps in FID that are invisible to standard training diagnostics. Under a representational bottleneck, the prediction choice instead dominates both metrics: recovering noise or velocity requires reconstructing high-dimensional information that the bottleneck has destroyed, while recovering the clean image remains feasible because real images lie on a low-dimensional manifold. We validate these findings on a U-Net and the full 130M-parameter JiT architecture across CIFAR-10 and Imagenette, and give architecture-dependent recommendations for both regimes.

Diffusion models have achieved state-of-the-art (SoTA) performance in generative tasks through their iterative denoising mechanism, yet they remain computationally intensive and energy-prohibitive. Spiking Neural Networks (SNNs) represent a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Their binary spike encoding enables the conversion of multiplication operations into addition operations, which is a key attribute for reducing energy consumption. Integrating the strong generative capabilities of diffusion models with the energy efficiency of SNNs thus forms a highly promising research direction. In adherence to the design goal of all-additive computation, this paper proposes a novel spiking diffusion model with attention enhancement. Specifically, we adopt non-negative ternary spiking neurons (NNTSN) to construct the U-Net backbone, which helps mitigate information loss during feature processing. To align NNTSN with the requirement of using only additive operations, we further design two core components: a membrane potential attention mechanism and an all-additive shortcut module. Leveraging a reparameterization technique, the proposed diffusion model is developed to retain only additive operations during inference, ensuring strict compliance with energy-saving design principles. Extensive experiments on relevant benchmarks demonstrate that our method achieves SoTA performance in generative tasks. It also substantially outperforms other SNN-based generative models while using fewer time steps, which validates both its effectiveness and efficiency.

Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly multimodal action distributions. Their expressivity, however, comes at a significant inference cost: generating each action typically requires simulating many steps of the generative process, compounding latency across sequential decision-making rollouts. We introduce flow map policies, a novel class of generative policies designed for fast action generation by learning to take arbitrary-size jumps---including one-step jumps---across the generative dynamics of existing flow-based policies. We instantiate flow map policies for offline-to-online reinforcement learning and formulate online adaptation as a trust-region optimization problem that improves the critic's $Q$-value while remaining close to the offline policy. We theoretically derive Flow Map $Q$-Guidance Training (FMQ), a principled closed-form learning target that is optimal for adapting offline flow map policies under a critic-guided trust-region constraint. We further introduce $Q$-Guided Beam Search (QGBS), a stochastic flow-map sampler that combines renoising with beam search to enable iterative inference-time refinement. Across $12$ challenging robotic manipulation and locomotion tasks from OGBench and RoboMimic, FMQ achieves state-of-the-art performance in offline-to-online RL, outperforming the previous one-step policy MVP by a relative improvement of $21.3$% on the average success rate.

Spatial resolved inference (SRI) is a critical technique in biomedical research, which aims to measure RNA sequence abundances by associating them with whole-tissue images at a fine-grained molecular scale. However, the extremely low throughput and distributional discrepancies involved pose significant challenges to cellular morphologies captured in hematoxylin and eosin (H&E) connection benchmarking, and existing SRI frameworks suffer from coverage gaps. In this paper, we propose MollFlow, a novel generative Mollified Flow matching model for spatial resolution inference, which is designed to learn the probabilistic dependency between the distributions of whole-slide images and expression profiles. We innovatively reframe the anchors of flow matching as a sequential informative prior derived from whole-slide images rather than isotropic Gaussian noise. During inference, MollFlow progressively refines the prior trajectory, ultimately converging to biologically plausible inferences. For the challenging coupling, which requires enabling intermediate states to explore pathologically aligned subspaces, we carry out a boundary mollified strategy to stabilize the velocity field, rather than being constrained by rigid trajectories. Endowed by this, our unique modulation generation perspective allows the model to retain coupling alignment for manifold incommensurability and prevent coverage gaps. Empirically, we find that MollFlow is capable of accurately inferring gene expression while exhibiting excellent performance in a variety of application scenarios.

Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encoders has recently been shown to provide a powerful inductive bias, improving both convergence and sample quality. In this work, we extend this idea to inverse problems, where pretrained generative models are employed as priors. We propose applying representation alignment (Repa) between diffusion or flow-based models and a DINOv2 visual encoder, to guide the reconstruction process at inference time. Although ground-truth signals are unavailable in inverse problems, we empirically show that aligning model representations of approximate target features can substantially enhance reconstruction quality and perceptual realism. We provide theoretical results showing (a) that Repa regularization can be viewed as a variational approach for minimizing a divergence measure in the DINOv2 space, and (b) how under certain regularity assumptions Repa updates steer the latent diffusion states toward those of the clean image. We integrate Repa into multiple state-of-the-art inverse problem solvers, and provide extensive experiments on super-resolution, box inpainting, Gaussian deblurring, and motion deblurring confirming that our method consistently improves reconstruction quality, while also reducing the number of discretization steps required to reach the same performance level as the underlying solver.

The rapid advancement of generative models has led to a new era of AI-generated images (AIGIs). Early generation models often suffered from basic defects, such as geometric distortions and poor text-image alignment. Modern generation models have largely overcome these issues, narrowing the perceptual gap between AI-generated and real-world images, thereby shifting evaluation from basic correctness to high-level aesthetic excellence. In this paper, we introduce **AesGI-Bench**, a comprehensive and multidimensional benchmark for fine-grained aesthetic evaluation of AIGIs. Our benchmark covers 18 representative generation models, 19,485 images, and 117K pairwise comparisons from perspectives of visual aesthetic, technical quality, and style alignment. Based on AesGI-Bench, we propose **AesGI-Assessor**, a lightweight and human preference-aligned aesthetic evaluation model for AIGIs. Built upon Qwen3.5, AesGI-Assessor achieves a **pairwise-to-score** learning paradigm, using tie-aware pairwise loss for training and three lightweight score heads for multi-dimensional aesthetic score prediction. Although trained from pairwise human preferences, it learns to predict precise continuous scores for individual images, enabling both fine-grained image-level evaluation and model-level ranking. Furthermore, we explore the potential of large multimodal models (LMMs) as automatic aesthetic evaluators. Experiments show that LMM-based evaluators better align with human aesthetic preferences than conventional metrics. AesGI-Assessor achieves the highest agreement with human judgments, validating its effectiveness as a lightweight, preference-aligned evaluator for fine-grained AIGI aesthetic assessment. The database and codes are publicly available.

Pretrained generative models, such as diffusion and flow matching models, have demonstrated exceptional capabilities in synthesizing high-quality data. However, steering their generative trajectories to satisfy strict constraints—such as identity preservation in image editing or obstacle avoidance in robotics—remains fundamentally challenging. While recent training-free guidance methods enforce path-safety, they rely heavily on min-norm controllers that only account for immediate constraint satisfaction. These myopic approaches often yield suboptimal trajectories that hover dangerously close to the boundary, severely restricting the model's ability to optimize toward its target. In this paper, we propose AegisFlow, a training-free, non-myopic path-safe guidance framework that recasts guided generation as a safety-critical optimal control problem with global scope. By integrating a time-varying barrier function directly into the global cost functional, AegisFlow naturally enforces constraints along the entire generative path. To compute the global optimal control, we derive a tractable, mathematically grounded approximation of the Hamilton-Jacobi-Bellman (HJB) equation. Comprehensive experiments demonstrate that AegisFlow outperforms state-of-the-art methods across text-guided image manipulation and robotic planning, establishing a significantly better trade-off between target alignment and constraint satisfaction.

Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without exchanging patient records. This privacy promise, however, is increasingly contested: a malicious or honest-but-curious server can launch model inversion attacks (MIAs) that reconstruct private patient images directly from shared model updates, and recent scalable, closed-form attacks penetrate even secure aggregation at clinically realistic batch sizes. Existing defenses face an unsatisfactory dilemma. Gradient-perturbation methods such as differential privacy and pruning trade away the diagnostic accuracy on which clinical reliability depends, while cryptographic protocols add system complexity yet still leave updates exposed to these scalable attacks. We propose Aegis, a principled client-side defense that breaks this dilemma without perturbing patient data or modifying the FL protocol. Our key insight is that the success of every known MIA is fundamentally bounded by the local batch size relative to the model's leakage capacity; once this limit is exceeded, distinct samples collide and reconstructions collapse into indistinguishable mixtures. Aegis turns this universal bottleneck into a defense: each client superimposes onto its real update a masking gradient computed on locally synthesized, task-relevant data, deliberately pushing the effective batch beyond the attack's recovery capacity. We complement the design with theoretical convergence guarantees under standard convex assumptions and evaluate Aegis on MNIST, CIFAR-10, and three MedMNIST modalities (chest X-ray, abdominal CT, colon pathology). Aegis neutralizes three state-of-the-art MIAs while preserving model utility and incurring only modest overhead, offering a practical privacy primitive for medical FL.

Many high-throughput scientific experiments produce distributional outputs, requiring conditional generative models to interpret the data, and to predict outcomes at new inputs. Acquiring experimental data at new conditions (e.g., for a new antigen or a new cell type) is often expensive, motivating the need for automated experimental design methods. However, existing active learning frameworks are limited as they assume scalar or vector-valued outputs. We first show that the Wasserstein test risk of a conditional generative task decomposes into a mean term and a shape term, demonstrating why mean-based active learning is blind to distributional shape. To capture both, we propose the transport Neural Tangent Kernel (tNTK), a tractable kernel that measures how sensitive a generative model's predicted distribution is with respect to its parameters. The tNTK recovers the standard NTK in the deterministic limit and upper-bounds the posterior Fréchet variance of the prediction under small parameter perturbations. It admits a closed form for affine Gaussian transport and tractable Monte Carlo estimators otherwise. Treating the tNTK as a Gaussian process covariance function, we greedily select inputs that minimize the total GP posterior variance across the pool, yielding a distribution-aware active learning strategy. We evaluate this strategy across four classes of conditional generative models, including conditional flow matching and conditional Monge gap, on synthetic distributions, intent-labeled sentences, and single-cell perturbation responses. In these experiments, the tNTK outperforms the baseline NTK computed from the conditional mean, demonstrating the value of distribution-aware active learning for generative-modeling tasks.

Generative compressed sensing uses the range of a pretrained generator as a nonlinear model for recovering structured signals from limited measurements. We study a conditional version of this problem for image recovery from subsampled Fourier measurements using prompt-conditioned generative models. Our framework separates two roles of conditioning: the prompt used to design the sampling distribution and the prompt used to define the recovery model. For ReLU and Lipschitz conditional generators, we prove stable recovery bounds showing that prompt-matched Christoffel sampling retains the same Christoffel complexity constant as existing near-optimal generative compressed sensing theory, while prompt mismatch incurs an explicit compatibility penalty. Experiments with Stable Diffusion show that prompts meaningfully reshape Christoffel sampling distributions and influence image recovery. Overall, our results suggest that prompts should be treated as design variables with distinct effects on sensing, approximation, and recovery.

Personalization histories vary across domains because surface actions and objectives differ: users watch or skip movies, click or ignore news, or request recommendation, summarization, question answering, or response generation. Rather than build a separate history encoder for each action space, we rewrite native interactions into a shared *action-on-item event schema*: a specific action on a specific item becomes positive, negative, or command-like evidence over a target-locus embedding whose geometry induces item-kind abstraction. Task requests become generalized commands over the active item-side abstraction. The modeling problem is how this evidence enters a user-specific state, persists, and is read out by the active command. We formulate the Multi-Timescale State Hypothesis (MTSH), where signed and command-like evidence flows through long-term stable interests, recency-sensitive short-term interests, and bursty episodic traces. We instantiate MTSH with $\texttt{PerTIDE}$, an action-conditioned multi-timescale encoder that learns a user-specific preference-history state and uses command-conditioned readout to produce a task-ready state. We evaluate $\texttt{PerTIDE}$ on MovieLens, MIND, and PENS for prediction, and PENS/OpenAI-Reddit for generation. $\texttt{PerTIDE}$ improves over the strongest baselines without explicit trace factorization by +2.36/+3.47, +10.05/+9.01, and +0.90/+1.42 MRR/nDCG on MovieLens, MIND, and PENS; and over the strongest two-shot large language model by +0.10/+0.18 and +0.04/+0.19 PerSEval-JSD/PerSEval-METEOR on PENS and OpenAI-Reddit. Removing action conditioning and command-conditioned readout degrades MIND by 6.52 MRR / 9.13 HR@10 and 4.14 MRR / 4.42 HR@10. Trace diagnostics show L/S/E specialization, while the full $L+S+E$ model is strongest under mixed stable, recency-sensitive, and episodic command evidence. These results support action-conditioned multi-timescale preference flow as a reusable user-history encoding principle.