NeurIPS 2026 论文列表

Knowledge editing aims to update outdated or incorrect knowledge in large language models without full retraining. Existing parameter-editing methods remain limited for long-sequence knowledge: single-point editors provide insufficient coverage for extended targets, while fixed chunk-based editors distribute editing effort uniformly across rigid predefined windows, failing to prioritize difficult, information-dense tokens. In this work, we show that token entropy is closely associated with both editing difficulty and semantic importance: high-entropy regions are harder to edit and often contain key factual content. Motivated by this observation, we propose DynEdit, a dynamic framework for long-sequence knowledge editing. DynEdit uses token-level entropy to guide non-uniform sliding, increasing the coverage of difficult high-entropy regions, and further applies entropy-guided token-level optimization to strengthen updates on information-dense tokens within each editing window. Experiments on three benchmarks and two backbone models show that DynEdit consistently outperforms competitive baselines across diverse long-form editing settings, achieving gains of up to +14.0 BLEU points on challenging paraphrase queries and near-saturated semantic scores on several diverse knowledge domains.

Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of drag-based editing. However, existing drag-based methods often struggle to balance drag accuracy with natural, plausible, and intent-aligned generation. We propose MoRe-Drag, a motion-grounded drag-based editing method. Our key insight is to treat pixel-space warping as coarse motion evidence, and to inject this evidence into the generative sampling trajectory. Specifically, MoRe-Drag performs region-aware latent recomposition over refinement, inpainting, and anchor regions, coupled with stage-adaptive conditioning that progressively shifts from motion-grounded structure formation to semantic refinement. We further support an instruction-free interface by adapting the MLLM-based text encoder for drag-aware instruction inference. Experiments on DragBench-SR and DragBench-DR show that MoRe-Drag substantially improves drag precision over strong base editors and achieves superior drag accuracy among SOTA drag-based methods, while delivering strong semantic consistency and visually realistic results. Code and dataset will be publicly released.

Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage a pre-trained vision encoder to extract a semantic delta that encodes the visual transformation between the two images. This semantic delta is injected into a pre-trained image editing model via a Perceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enabling single-pair supervision without requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce a semantic delta consistency loss that aligns the semantic change of the generated output with the ground-truth semantic delta extracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be made publicly available.

Diffusion and flow models enable powerful generative modeling, but controlled editing remains challenging due to the need to balance source fidelity and edit compliance along the generative trajectory. We propose a principled, inversion-free framework for flow-based image editing from a trajectory centric perspective. Rather than relying on inversion or heuristic guidance, we introduce a one-step look-ahead mechanism that evaluates the effect of candidate updates on both edit and fidelity rewards estimated via denoising, and adjusts the current update based on this evaluation. At each step, our method applies reward-driven corrections guided by the look-ahead evaluation under an explicit fidelity constraint, resulting in stable and coherent trajectory evolution. This look-ahead design mitigates gradient interference between edit and fidelity objectives by anticipating their interaction in future states, leading to improved trade-offs. Experiments demonstrate that the proposed approach achieves stronger semantic edits while better preserving source content compared to existing strong baselines.

Video detectors are usually evaluated on observational splits, where target events co-occur with environmental and capture factors. A high AUC under this setup can reflect either the event itself or its surrounding conditions, and standard reporting cannot tell them apart. This work addresses the entanglement with CARVE, a counterfactual video editing protocol. For each source clip, CARVE generates a matched quartet $(V^0, V^E, V^A, V^{AE})$ that holds camera pose, layout, and surrounding traffic fixed while independently intervening on environment and event. A two-phase generator first produces a layout-conditioned reference image, then performs reference-guided video editing; a three-layer reference / objective / VLM-panel protocol filters the output. Each quartet supports thresholded and continuous diagnostics for false-positive purity, event faithfulness, environment-induced drift, and held-out factor compositions. Thresholded scores are paired with continuous ones so that a conservative detector cannot suppress scores everywhere and look robust. The same audit drives training: CGAA-IC samples weak factors weighted by measured brittleness and adds quartet-level purity, consistency, and faithfulness losses; the inference graph is unchanged. On CCTV accident detection, the audit shows that clean AUC does not predict robustness rankings: a VideoMAE detector with $91.4\%$ AUC on TAD scores only $72.3\%$ CPS on the night slice of CARVE-Q. CGAA-IC raises purity from $84.2\%$ to $89.9\%$ and cross-dataset average AUC from $80.3\%$ to $84.5\%$.

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.

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.

Depth-of-field control is a fundamental tool in photography, yet post-capture bokeh editing from a single image remains challenging. A practical editor should handle images captured under arbitrary focus and aperture settings. Existing methods typically assume an all-in-focus input, or first recover an all-in-focus image before rendering new bokeh. Such pipelines can discard useful blur cues from the source image and propagate reconstruction artifacts into the final edit. We introduce **AnyBokeh**, a physics-guided framework for **any-to-any bokeh editing**. Instead of treating source blur merely as a degradation to be removed, AnyBokeh estimates the **source blur state** with a signed circle-of-confusion map and a disparity map. By modeling the linear relation between signed circle of confusion and disparity difference, AnyBokeh estimates a source-specific **optical fingerprint** and transfers the source optical characteristics to the desired focus and aperture setting. A generative editor conditioned on both source and target circle-of-confusion maps then performs relative blur synthesis, enabling spatially adaptive deblurring, preservation, and defocus rendering. To support physically supervised learning, we further construct a high-fidelity synthetic dataset with accurate depth, focus distance, and full EXIF metadata. Experiments on real-world benchmarks show that AnyBokeh achieves faithful and controllable editing across any-to-any bokeh editing, all-in-focus-to-bokeh rendering, and defocus deblurring, while avoiding all-in-focus reconstruction and test-time bokeh-level calibration commonly required by existing approaches. The code and dataset will be made publicly available.

Text-based image editing has recently been reinterpreted in large multimodal transformers as conditional generation, where source image tokens are concatenated with text and noise tokens. While effective, this design incurs substantial computational overhead in attention layers. To mitigate this inefficiency, we propose {\em TokenDrop}, an efficient editing framework that shift the computational burden from dense source-token conditioning to a lightweight regularized sampling update. By transferring the influence of source tokens into a closed-form sampling update, the method preserves source consistency with negligible cost. To support this regularized dynamics, we introduce a deviation-guided adaptive masking strategy that selectively drops redundant tokens while maintaining editing fidelity. Across FluxKontext and Qwen-Image-Edit, our training-free method achieves an average 22.4\% improvement in inference speed on PIEBench, while better preserving non-edited regions. The method delivers up to 1.8$\times$ speedup at 1024$^2$ resolution and 2$\times$ speedup at 2048$^2$ resolution. The code will also be released publicly.

Editing a video stream in real time, without access to future frames, is essential for interactive applications. Existing training-free video editing methods, however, assume full offline access to the entire sequence, while streaming approaches either rely on additional training or are limited to specific editing techniques. We show that this limitation can be removed by analyzing the self-attention dynamics of DMD-distilled autoregressive video diffusion models. We uncover a key asymmetry: queries and keys remain stable and aligned with their source counterparts across denoising timesteps, while values diverge and encode the evolving content. This observation leads to a simple mechanism: a single forward pass suffices to capture source identity in the value features, which can be reused throughout generation. Starting from pure Gaussian noise and injecting only these source values reconstructs the input with fidelity matching inversion-based methods, effectively eliminating inversion. Based on this insight, we introduce AR-Edit, a training-free, inversion-free method that caches source values once and selectively injects them during autoregressive denoising, enabling real-time, structure-preserving streaming video editing with minimal overhead. Code will be released.

Image-text offset alignment, which refers to the consistency between visual transformation directions and their corresponding textual transformation directions, has become an important relational property in multimodal contrastive representation space (e.g., CLIP or SigLIP). This property is widely utilized in text-guided generative modeling tasks, such as generative model domain adaptation and text-guided Image-text offset alignment, which refers to the consistency between visual transformation directions and their corresponding textual transformation directions, has become an important relational property in multimodal contrastive representation spaces such as CLIP and SigLIP. This property plays a central role in text-guided generative modeling tasks, including generative model domain adaptation and text-guided image editing, where textual offsets are used to guide visual transformations. These methods fundamentally rely on the assumption that image and text offsets are well aligned, such that textual transformation directions can provide reliable guidance for corresponding visual transformations. However, the validity of this assumption has not been systematically examined. **In this work**, we question this foundational assumption by conducting a comprehensive empirical analysis of image-text offset alignment in multimodal contrastive representation space. Our findings reveal not only noticeable offset misalignment but also a meaningful positive correlation between image-text offset misalignment and semantic concept distance across six large datasets and eight contrastive vision-language models. Based on this discovery, we propose Adaptation with Iterative Refinement (AIR), a method that iteratively refines text offsets through anchor sampling and our proposed concept description learning to reduce image-text offset misalignment and improve guidance accuracy. Comprehensive experiments on zero-shot generative model domain adaptation and text-guided image editing, including qualitative, quantitative, and user studies, consistently show that AIR enables state-of-the-art performance in these tasks. **Code and additional experiments are available in the supplementary material.**