NeurIPS 2026 论文列表

The electrocardiogram (ECG) is a time series that records the electrical activity driving cardiac motion through electromechanical coupling (EMC), where the depolarization captured by ECG triggers each heartbeat. The temporal delay and low spatial resolution of such multichannel dynamic data often cause temporal and spatial misalignments along the physical motion, limiting the application of ECG data to event/segment-based downstream clinic practice. Although many efforts have been made to recover spatiotemporal trajectories conditioned on ECG waveforms, the intrinsic principle of EMC that results in various temporal delays remains underexplored in current generative models. To address this limitation, we propose Time2Track, a mechanics foundation model that generates individualized 4D heart motion from same-day ECG recordings by encoding electrophysiology as delay-aware token embeddings. Specifically, a learnable wavelet decomposition extracts the electromechanical delay, which is then formulated as a physics-based conditioning signal for motion diffusion. Time2Track generates 4D motion with individual-specific kinematics, avoiding the collapse to a population average, i.e., a common failure scenario when diffusing motions whose inter-subject differences are subtle. On $n=40,577$ subjects from the UK Biobank, our Time2Track accurately synthesizes 4D cardiac trajectories from ECG and preserves inter-subject diversity, a regime in which generic motion diffusion baselines collapse to a population average.

Neural decoders have shown strong potential for error correction in short- and moderate-length regimes, yet a fundamental tension remains between decoding accuracy and inference latency. Recent attempts to leverage diffusion probabilistic models for channel decoding typically adopt a fully generative paradigm, initializing the reverse process from an observation-agnostic prior, which overlooks a key structural property of channel decoding: the received signal already contains substantial information about the target codeword. In this work, we propose Channel Residual Diffusion Model (ChRes-DM), a principled diffusion-based decoding framework that reinterprets channel decoding as a directed restoration process anchored at the noisy observation. Instead of sampling from a generic prior, ChRes-DM constructs a geometric residual bridge between the received signal and the clean codeword, explicitly modeling the conditional transport induced by the channel and leading to a deterministic Probability Flow Ordinary Differential Equation (PF-ODE) that governs the decoding dynamics. By eliminating redundant stochastic sampling inherent in conventional diffusion models, ChRes-DM enables efficient iterative decoding with flexible step-skipping, offering fine-grained control over the accuracy–latency trade-off. Extensive experiments across representative channel coding benchmarks demonstrate that ChRes-DM achieves competitive or superior decoding performance compared to existing neural decoders while significantly reducing inference iterations, highlighting diffusion-based residual transport as a promising and scalable paradigm for neural channel decoding.

The application of diffusion transformers is suffering from their significant inference costs. Recently, feature caching has been proposed to solve this problem by reusing features from previous timesteps, thereby skipping computation in future timesteps. However, previous feature caching assumes that features in adjacent timesteps are similar or continuous, which does not always hold in all settings. To investigate this, this paper begins with an analysis from the frequency domain, which reveal that different frequency bands in the features of diffusion models exhibit different dynamics across timesteps. Concretely, low-frequency components, which decide the structure of images, exhibit higher similarity but poor continuity. In contrast, the high-frequency bands, which decode the details of images, show significant continuity but poor similarity. These interesting observations motivate us to propose Frequency-aware Caching (FreqCa) which directly reuses features of low-frequency components based on their similarity, while using a second-order Hermite interpolator to predict the volatile high-frequency ones based on its continuity. Besides, we further propose to cache Cumulative Residual Feature (CRF) instead of the features in all the layers, which reduces the memory footprint of feature caching by 99%. Extensive experiments on FLUX.1-dev, FLUX.1-Kontext-dev, Qwen-Image, and Qwen-Image-Edit demonstrate its effectiveness in both generation and editing. Codes are available in the supplementary materials and will be released on GitHub.

Dense cardiac dynamics are essential for assessing cardiac function, yet in practice clinicians often observe only extremely sparse measurements. However, recovering full dynamics from such sparsity is a highly ill-posed problem due to the lack of temporal cues. While auxiliary time-series signals like ECG provide physiological guidance, effectively exploiting them is challenging due to the cross-modal representation gap and phase misalignment. In this work, we present CardioFAD, a Focus--Align--Diffuse paradigm that reformulates sparse cardiac dynamic synthesis as a recoverability--alignability anchor discovery problem. Our core insight is that resolving extreme temporal ambiguity requires a compact set of anchor states that are both sufficient to recover the visual trajectory and reliable for ECG--visual alignment. We therefore learn motion-salient keyframes under two complementary principles: trajectory sufficiency, which preserves trajectory-critical intra-modal motion content, and anchor alignment, which promotes consistent inter-modal correspondence at these anchors. We implement the Focus and Align modules optimized by the two aspects.Diffuse then performs two-stage generation by first synthesizing keyframes and subsequently interpolating the remaining frames. Extensive experiments on cardiac MRI and echocardiography videos demonstrate superior visual quality, temporal coherence, and clinical fidelity under the extreme one-shot regime.

We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transformer to learn a velocity field over graph representations with node and edge attributes. Our model promotes topology-aware alignment through optimal transport-based coupling and encourages global structural coherence through regularisation. To incorporate domain-driven structural priors, we introduce graphettes, a new probabilistic family of graph structure models that generalize graphons via controlled structural edits for motifs such as rings, stars, and trees. We theoretically analyze the coupling, invariance, and structural properties of the framework, evaluate it on synthetic and molecular benchmarks, and isolate the contributions of the structural prior, the optimal-transport coupling, and the regularisation terms through controlled ablations. Flowette achieves competitive performance overall, attaining state-of-the-art results on several metrics across multiple benchmarks, highlighting the effectiveness of combining structural priors with flow-based training for modeling complex graph distributions.

Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Methods such as Flow-GRPO and CPS cast the denoising process as a Markov Decision Process and apply PPO-style ratio clipping to enforce a trust region. However, we argue that *ratio clipping is structurally ill-suited for flow models*: the probability ratio between new and old policies is a noisy, single-sample estimate of the true policy divergence, leading to over-constraining in some regions of the trajectory and under-constraining in others. We propose **Flow-DPPO** (Flow Divergence Proximal Policy Optimization), which replaces ratio clipping with a divergence proximal constraint. A key observation is that the per-step policy in flow models is Gaussian, enabling *exact* and *cheap* computation of the KL divergence between old and new policies. Flow-DPPO employs an asymmetric divergence mask that blocks gradient updates only when they simultaneously move away from the trusted region and violate the divergence threshold. Experiments show that Flow-DPPO achieves higher rewards with better KL-proximal efficiency, alleviates catastrophic forgetting, promotes balanced multi-objective optimization, and enables stable multi-epoch training where ratio clipping degrades.

A cover song re-renders an existing piece by preserving its tonal content—melody and chord progression—while reshaping other musical attributes. This suggests a natural formulation for generative cover synthesis with pretrained music language models: explicitly control tonal content, while specifying lyrics and style through text. Existing approaches condition on frame-level features, tightly coupling generation to the reference’s timing and structure, and limiting flexibility such as tempo variation, structural rearrangement, or partial conditioning. To this end, we present FlexCover, a cover generation model that conditions a pretrained text-to-song foundation model on a symbolic lead sheet. Our design enables alignment-free control: generated outputs preserve the tonal signature of the source without frame-level alignment, allowing flexible timing, structure, and segment-level generation. We further introduce a training curriculum with partially mismatched audio–symbolic pairs, improving diversity and robustness. We evaluate FlexCover with objective metrics, standard subjective ratings, and in-depth expert interviews that provide fine-grained diagnostic insights. Experiments show that FlexCover achieves state-of-the-art performance among open-source systems and is competitive with leading commercial models such as Suno-v5.5.

Diffusion models have become a leading approach in visual generative modeling, particularly effective in text-to-image and controllable generation. Their main drawback is slow inference as it requires many sampling steps. While recent distillation methods enable one-step text-to-image generation, extending these techniques to controllable generation is still challenging. Existing controllable models, such as ControlNet, rely on dual-branch design that make distillation difficult. In this work, we adopt a simpler strategy. Instead of adding extra conditional branches, we finetune a selected subset of layers in the original text-to-image diffusion model to directly enable controllable generation. This results in DeltaControl, a single-flow, multi-step diffusion model capable of supporting spatial-condition generation while matching the performance of dual-branch approaches. Building on this teacher model, we further apply step-distillation to obtain FlashControl, a one-step model capable of spatial controllable generation with a single forward pass.

Training robust tool-calling agents requires large-scale trajectory data that is both realistic and verifiable, yet existing datasets still rely heavily on costly human annotation or synthetic pipelines with weak correctness guarantees. In this work, we introduce \projectname, a large-scale synthetic and verifiable dataset for tool-calling agents built on real-world MCP servers. Unlike prior pipelines that first generate tasks and then attempt to solve them, \projectname inverts this process: a strong LLM first self-explores real MCP servers and records reachable execution states, after which natural-language tasks are synthesized backward from observed outcomes. This back-chaining design guarantees task reachability and label correctness by construction. To support scalable offline training and evaluation, we further construct a retrieval-augmented simulator from explored trajectories, enabling reproducible tool execution without relying on live MCP servers. Each instance includes a task description, tool schemas, ground-truth trajectories, ground-truth answers, and tool-call responses for offline replay, and is automatically checked for determinism and semantic consistency. \projectname aggregates thousands of verifiable tasks across diverse real-world MCP servers and supports scalable agent learning without human annotation. Experiments show that models trained on \projectname achieve significant improvements on tau2-Bench and BFCL, demonstrating the effectiveness of realistic, verifiable synthetic data for training generalizable tool-calling agents.

Evaluation of foundation models often rely on aggregate scores from benchmarks that lack comprehensive coverage and metadata for a fine-grained evaluation. We introduce a framework for automated benchmark generation. Our framework generates evaluation problems grounded in reference material, such as textbooks, producing benchmarks with broad coverage, rich metadata, and robustness to contamination. The pipeline employs a multi-agent architecture for problem generation and a solution-graph-driven strategy that significantly improves the reliability of ground truth solutions. Using the framework, we generate three benchmarks in Machine Learning, Corporate Finance, and Personal Finance. Expert review finds a significantly lower ground-truth error rate than previous benchmarks such as MMLU and GSM8K. Evaluation of 12 commercial and open-source models shows that our benchmarks achieve near-uniform competency coverage and surface performance differences across models that existing benchmarks fail to capture. We open-source the framework and our curated benchmarks.

With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced significantly. However, despite the inherent reasoning capabilities of unified MLLMs for self-reflection and self-refinement, their use in text-to-image generation remains largely underexplored. Meanwhile, existing multimodal reasoning–based image generation methods mostly rely on prompt augmentation or holistic image–text alignment judgments, without fine-grained reflection and refinement of detailed prompt attributes, leading to limited fine-grained control. To address this limitation, we propose FiRe, a Fine-grained Multimodal Reasoning method for enhanced image generation by MLLM. In specific, FiRe performs a fine-grained multi-step reasoning by first decomposing the prompt into key visual requirements and then self-judging their satisfaction in the generated image, followed by localized refinement according to self-generated precise feedback. In addition, to further strengthen the MLLM's multimodal reasoning ability, we introduce FiRe-GRPO, a reinforcement learning method tailored to FiRe. Since standard Group Relative Policy Optimization (GRPO) suffers from sparse, outcome-based rewards in multi-step reasoning, we formulate our reasoning process as a step-level decision-making problem, design step-specific rewards, and compute step-level advantages for granular credit assignment within GRPO. Extensive experiments demonstrate that FiRe consistently outperforms competitive text-to-image baselines, including existing reasoning-based methods, with particularly substantial gains on compositional text-to-image benchmarks.

Recent hand avatar reconstruction methods achieve high-quality results through multi-view observations or per-subject optimization, limiting scalability and practical applicability. We present \emph{FF3DGS-Hand}, a feed-forward framework that reconstructs animatable hand avatars from a single monocular image using 3D Gaussian Splatting. Our method establishes a stable canonical geometry for thin and articulated hand structures, then reconstructs appearance by leveraging source image evidence instead of relying solely on global latent features. We further introduce a rendering-aware, source-conditioned appearance framework that selectively updates Gaussians according to their rendering contribution, recovering input-specific details while suppressing artifacts in unobserved regions. The resulting representation is animatable via standard hand pose parameters and supports efficient rendering. Experiments demonstrate strong visual quality and quantitative performance in the single-image setting.

4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and difficult to scale to longer sequences. We introduce a training-free approach that accelerates 4D mesh generation while improving temporal correspondence quality. Our key observation is that temporal correspondences emerge inside a 4D backbone long before its generated meshes become visually accurate. We exploit this with a general framework we call Spatio-Temporal Attention Chain which propagates information across space and time. Starting from vertices on an anchor mesh, the chain maps vertices to latent tokens. It then follows temporal correspondences in latent space, and recovers frame-specific vertices through latent-to-vertex attention. This design avoids expensive explicit matching while preserving anchor mesh details and thereby improving dynamic mesh geometry and temporal consistency. Compared to state-of-the-art, our method generates a 4D mesh in 9 seconds, achieving a $13\times$ speedup while producing higher-quality results. Moreover, our approach scales to videos up to $16\times$ longer without degrading mesh quality. Beyond generation, the improved correspondences enable competitive zero-shot performance on two downstream tasks: 2D object tracking and 4D tracking. We further show that our framework enables reliable camera estimation, a capability not supported by prior 4D mesh generation methods.

Identity-preserving text-to-video generation (IPT2V) empowers users to produce diverse and imaginative videos with consistent human facial identity. Despite recent progress, existing methods often suffer from significant identity distortion under large facial pose variations or facial occlusions. In this paper, we propose FaithfulFaces, a pose-faithful facial identity preservation learning framework to improve IPT2V in complex dynamic scenes. The key of FaithfulFaces is a pose-shared identity aligner that refines and aligns facial poses across distinct views via a pose-shared dictionary and a pose variation–identity invariance constraint. By mapping single-view inputs into a global facial pose representation with explicit Euler angle embeddings, FaithfulFaces provides a pose-faithful facial prior that guides generative foundations toward robust identity-preserving generation. In particular, we develop a specialized pipeline to curate a high-quality video dataset featuring substantial facial pose diversity. Extensive experiments demonstrate that FaithfulFaces achieves state-of-the-art performance, maintaining superior identity consistency and structural clarity even as pose changes and occlusions occur. The code and dataset pipeline will be released.

Recovering $360^\circ$ HDR environment maps from unconstrained portraits is a fundamental yet ill-posed challenge in inverse rendering. Existing approaches typically suffer from a critical trade-off: traditional facial reflection-based methods yield low-fidelity, blurry results that lack practical utility, while recent generative models often resort to unconstrained panoramic hallucination (i.e., synthesizing environments with no physical grounding), which leads to severe overfitting and inaccurate lighting. In this paper, we present FaceProbe, an inpainting-driven framework that treats the human face as a reliable, physically-grounded light probe. Instead of synthesizing the environment from scratch, we reformulate the task as a masked completion and outpainting problem. By preserving the peripheral context of the input image and utilizing the portrait as a structural condition, our architecture leverages the generative priors of Diffusion Transformers (DiT) to extend sparse observations into high-fidelity, sharp $360^\circ$ HDR panoramas. To further eliminate color shifts and highlight inaccuracies, we introduce OLAT-GRPO, a post-training alignment mechanism based on Group Relative Policy Optimization. By subjecting the generated maps to image-based relighting on One-Light-At-a-Time (OLAT) datasets, we evaluate their physical validity through relighting consistency. This allows us to explicitly prune inconsistent denoising paths and align the model with multi-objective rewards, ensuring energy conservation and spectral accuracy. Extensive evaluations demonstrate that FaceProbe achieves a paradigm-shifting improvement. Most notably, in the critical portrait relighting task, our method reduces estimation errors by 54.4\% in Angular Error and 48.5\% in si-RMSE over state-of-the-art methods, delivering a robust and highly usable solution for photorealistic rendering and world model applications.

Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates a practical challenge: a provider may pass governance certification with one generator and later silently switch to a cheaper and lower-quality one for deployment, compromising public trust or even safety in high-stakes domains. We study integrity auditing at deployment time and propose FARE (Forensic Acceptance Region Estimation). A certified generator is enrolled by training FARE on images sampled from that generator. After deployment, FARE can determine whether a generated image is consistent with the enrolled generator-using only that image. FARE's features are based on image generator-specific artifacts that have been proposed for forensic applications. FARE amplifies these features during training by finding hard samples that tighten the acceptance region and increase sensitivity to subtle changes in the certified generator. Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under adaptive attacks that actively manipulate images to evade swap detection.

As text-to-image diffusion models (T2I DMs) become widely deployed, adversarial advertising has emerged as a realistic threat: an attacker may compromise a T2I DM so that it implants target product brands into images generated from users' non-advertising prompts. Two key challenges remain largely unresolved in this setting: achieving natural and semantically coherent adversarial advertisement and ensuring robust adversarial advertisement. To address these challenges, we develop a new estimation algorithm for the multivariate continuously scaled phase-type with Lévy (MCPHL) distribution to capture the intrinsic distribution of natural advertisement prompts in the prompt-embedding space. With the estimated MCPHL, we construct an attack that pushes non-advertising prompts toward high-density regions of this distribution, making the resulting perturbed prompts better aligned with natural advertising prompts. We then propose a masked parameter smoothing approach grounded in mollification theory, yielding a smoothed T2I DM equipped with a dimension-invariant certified guarantee against advertisement degradation under model fine-tuning. The masking mechanism preserves utility by avoiding unnecessary smoothing on sensitive parameters, and our theoretical analysis shows that the smoothed model better preserves adversarial advertisements under fine-tuning, while maintaining better generation quality.

Variable-order Markov models generate by backing off to the longest usable suffix of the generated history, while regular constraints describe finite horizon controls such as fixed endings, metrical patterns, and anti-copy rules. Existing belief-propagation methods give exact regular-constrained sampling for first-order Markov chains, but a first-order state merges histories that a variable-order generator deliberately keeps distinct. We give the corresponding variable-order construction: replace the Markov state by the sparse observed context state and compose this context graph with the regular constraint automaton. For a fixed context graph and automaton, inference is linear in the horizon and in the number of reachable product edges, without expanding to all $|\mathcal{V}|^K$ histories; the same source-row interface handles reversible augmentation by inverse count lookup, without materializing transformed corpora. Tiny enumerated examples verify exact partition functions and conditional probabilities; Bach Prelude experiments show sparse regular-constrained sampling at $K=6$ and 12-key virtual transposition semantics from $592$ stored events.

Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as reversing information loss across scales. We introduce **SKILD**, a **S**cale-invariant **K**-Space **I**mage **L**earning **D**iffusion model that unifies generation and continuous super-resolution within a single unconditional framework. Both natural images and critical physical systems exhibit scale invariance, and we leverage it to design a forward process that attenuates image content from fine to coarse scales while injecting spectrum-matched Gaussian noise, making scale an explicit coordinate of the diffusion dynamics. The same trained reverse process performs generation and continuous super-resolution by varying only the starting timestep: *no task-specific architecture, no conditioning branch, no classifier-free guidance, no retraining per scale factor*. Empirically, SKILD reaches FID $2.65$ and Inception Score $9.63$ on unconditional CIFAR-10, performs $2\times$–$8\times$ super-resolution on ImageNet from a single unconditional checkpoint while outperforming conditional models across perceptual metrics, and reconstructs critical Ising models whose connected four-point correlations closely track the ground truth.