NeurIPS 2026 论文列表

Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene editing. To support this, we introduce MorphGen, a state-of-the-art diffusion-based generative model for fluorescent microscopy that enables controllable generation across multiple cell types and perturbations. MorphGen is trained with an alignment loss that matches its representations to phenotypic embeddings from a biology foundation model, encouraging meaningful morphological patterns consistent with real cell images. Unlike prior approaches that compress multichannel stains into RGB images, sacrificing organelle-specific detail and focusing on a single cell type, MorphGen generates the complete set of fluorescent channels jointly, preserving per-organelle structure and enabling post-generation interpretation. We demonstrate biological consistency with real images via CellProfiler features, and MorphGen attains an FID score over 35% lower than the prior state-of-the-art MorphoDiff. Finally, in a compositional generalization test that holds out cell type--perturbation combinations during training, MorphGen achieves in-distribution quality on 55% of unseen pairs under a seen-calibrated criterion.

Instruction-guided image editing models are mostly trained and evaluated on executable requests, yet many real user instructions are non-operative for the given image. Reliable editing therefore requires reasoning not only about how to edit, but also whether to edit and what to ignore. We introduce \bench{}, a large-scale benchmark for non-operative image editing, with 1.1M instructions over 31K images across seven categories spanning symbolic, grounded, physical, and logical non-executability, plus a 35K-sample partial-edit test set evaluating selective execution when one clause is feasible and another is not. A 700-sample internal audit confirms a 94.9\% true no-op rate with $\kappa=0.87$. Evaluating 13 systems reveals a clear action--abstention dissociation: frontier editors achieve strong standard editing quality but still over-edit more than 40\% of no-op samples. Three complementary perceptual, feature-level, and vision-language metrics further surface distinct failure modes across architectures. As a reference baseline, JUDGE-THEN-EDIT BAGEL—a feasibility-aware BAGEL variant trained on a 150K mixture of full-edit, partial-edit, and no-op data—reduces over-edit rate from 41.7\% to 12.6\%. Our results show that abstention is a distinct capability that current benchmarks fail to measure.

Existing locate-then-edit Knowledge Editing (KE) methods typically decompose editing into two stages: upstream target representation optimization and downstream constrained parameter optimization. The optimization across the two stages is disconnected: upstream applies uniform regularization without observing downstream realization of the planned residual, hindering a refined accuracy–editability trade-off. Since this realization is request-specific and depends on downstream constraints, uniform regularization can over-shrink high-association requests, causing insufficient editing, while it can under-regularize low-association requests, producing over-large planned residuals that reduce downstream editability. To bridge this disconnect, we propose $\textbf{MetaKE}$ ($\textbf{Meta}$-learning for $\textbf{K}$nowledge $\textbf{E}$diting), a new framework that unifies upstream and downstream stages into a bi-level optimization problem. The inner level optimizes parameter updates for the target representation, while the outer level optimizes representation using feedback from downstream constraints, achieving a better semantic accuracy-editability trade-off. To avoid costly multi-layer backpropagation, we introduce a Structural Gradient Proxy to approximate and propagate this feedback. Extensive experiments show that MetaKE outperforms strong baselines, offering a new perspective on KE. Code at: https://anonymous.4open.science/r/MetaKE-C160.

Multimodal large language models (MLLMs) enable prompt-driven image editing, introducing a new challenge for image forgery detection and localization (IFDL). Unlike traditional or mask-guided editing, MLLM-based editing may re-synthesize both tampered and non-tampered regions, weakening the correspondence between tampering regions and pixel-level forensic discrepancies. To study this emerging paradigm, we introduce MLLM-Edit, a large-scale benchmark containing approximately 250k tampered images with pixel-level tampering annotations. MLLM-Edit is constructed through a structured pipeline that combines diverse real-image collection, human-like prompt design, multi-source MLLM-based editing, and semi-automatic mask annotation. Specifically, we collect natural and text images from 30 sources, formulate prompts with target content, tampering type, and consistency constraints, generate forgeries using 18 representative open-source and closed-source MLLM-based editors, and obtain pixel-level tampering masks through a semi-automatic annotation pipeline. We further establish four evaluation protocols covering traditional editing, mask-guided editing, fully synthetic generation, and MLLM-based editing. Experiments show that existing IFDL methods degrade substantially on MLLM-based forgeries, especially in pixel-level localization, highlighting the need for dedicated benchmarks and methods. A demo subset of MLLM-Edit is available at the \href{https://kaggle.com/datasets/8441a4e0594aa02aa7a59ee3eead454410d34716d14423d56a56c7bbbac80655}{project page}.

Recent work has pointed out current models' limitations in sampling according to prespecified target distributions. In this work, we show that current models nevertheless possess a coarse form of control over their output distributions: by instructing a range of open-source models to maximize or minimize output certainty in a two-alternative forced-choice task with no correct answer, we find that several models can shift entropy in the instructed direction. Through contrastive activation analysis, we further identify a linear direction in activation space that models recruit under uncertainty-modulation instructions and that can be used to causally modulate entropy via activation steering in the absence of any such instructions, suggesting that deliberate entropy control has an identifiable internal representation.

Image vectorization reconstructs raster images as compact scalable vector graphics (SVG) representations. Most of existing vectorization methods optimize for pixel-level rendering fidelity, where the reconstructed SVGs often lack alignment with human-perceived object-part hierarchies, making them difficult to manipulate. To address this, this work for the first time studies the problems of hierarchical image vectorization and SVG semantic labeling. We propose Lang-SVG, a tree-structured SVG representation that encodes SVG primitives with language tags and parent-child hierarchy. Lang-SVG novelly incorporates granularity-controllable segmentation priors and vision-language priors into the optimization-based vectorization pipeline. It introduces a Tree-Guided Pruning and Merging module to reduce redundant multi-granularity SVG primitives into coherent semantic structures, and an L1-SAM Prompter to recover missing regions. It also includes a new SVG semantic tagging method that integrates SVG tree context into multimodal large language models (MLLMs) to assign language tags to SVG primitives. We also propose a new evaluation protocol for image vectorization, measuring structural quality and semantic part alignment beyond reconstruction fidelity. Experiments demonstrate that Lang-SVG achieves state-of-the-art performance in both rendering and structural quality.

What if you could edit an image simply by drawing on it---circling an object, writing a short label, or sketching an arrow---with minimal or even no typed prompt? We first reveal a surprising capability: when a text-driven image editor is paired with a strong Vision-Language Model (VLM) at inference time, the combined system can directly interpret visual instructions embedded in the image itself, such as on-image text, bounding boxes, and directional arrows. However, this pipeline incurs additional VLM-planning latency, introduces brittle cross-model error propagation, and depends on external APIs or auxiliary model weights. To address these limitations, we introduce Siphon, a framework that internalizes this externally elicited visual-instruction-following competence into a single diffusion-based editor. Rather than relying on an external VLM at inference time, Siphon first uses a VLM planner to synthesize paired visual-instruction supervision, and then transfers this competence into the editor through lightweight LoRA fine-tuning. The resulting model reads, grounds, and executes on-image annotations directly from pixels, while keeping the diffusion sampling cost close to that of the base editor. Across multiple diffusion architectures, Siphon substantially improves spatial controllability, instruction adherence, and marker removal over text-driven baselines, while matching VLM-assisted pipelines without their additional planning latency, API dependence, and cross-model fragility. We do not position rendered long text as a replacement for conventional text prompts; rather, on-image text is mainly intended for short labels and spatially grounded edit intents, while long or complex instructions can still be provided through the standard text channel.

Instruction-based image editing exhibits heterogeneous difficulty not only across cases but also across regions of an image, motivating refinement approaches that allocate correction to where the model struggles. Existing refinement signals arrive late, after a fully generated image or a completed denoising step. We ask whether such a signal can act *within* an ongoing forward pass. To investigate this, we probe a frozen image-editing model and find that although generation capability emerges only in the last few layers, the error pattern is already set in early layerss (rank correlation ρ = 0.83 with the final-layer error map). Based on this, we introduce **Inline Critic**, a learnable token that critiques a frozen model's predictions at its intermediate layers and steers its hidden states to refine generation during the forward pass. A three-stage recipe is proposed to stabilize the training from learning how to critique to steering generation. As a result, we achieve state of the art on GEdit-Bench (7.89), a +9.4 gain on RISEBench over the same backbone, and the strongest open-source result on KRIS-Bench (81.92, surpassing GPT-4o). We further provide analyses showing that the critic genuinely shapes the model's attention and prediction updates at subsequent layers.

Feature caching accelerates Diffusion Transformers by reusing intermediate computations across denoising timesteps, yet existing methods share a fundamental blind spot: they treat the standard reference denoising trajectory as the optimisation target, approximating its per-step states and thus inheriting its output as an implicit quality ceiling. We argue that this target is arbitrary --- what truly matters is the ideal result, not the intermediate path taken to reach it. We propose IdealCache, a training-free framework that abandons step-wise imitation of the reference trajectory. Instead, it anchors caching on a per-input ensemble of ideal trajectories constructed from candidate target results, aggregates their geometry into a single graph, and discovers optimal non-uniform schedules via a constrained shortest path on this graph. By decoupling from the reference process, IdealCache breaks the performance bottleneck of trajectory-approximation; our schedules can attain near-lossless fidelity in generation and even surpass on task-specific metrics (e.g., maintaining higher semantic consistency in image editing). Without retraining, IdealCache achieves $6.35\times$ speedup on Qwen-Image, $5.84\times$ on Qwen-Image-Edit, and $4.32\times$ on HunyuanVideo, with competitive or superior quality across tasks.

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training. Though existing general image-editing models demonstrate strong capabilities, they lack necessary embodiment-specific priors to fully bridge this gap. In this work, we present HandEdit, a unified large-scale embodiment-aware image-editing dataset and benchmark specifically designed to transform human hands and arms into various dexterous robotic embodiments within egocentric frames. HandEdit comprises over 200M editing instances derived from five diverse source datasets, covering 26 distinct URDFs, including 13 hand-only and 13 hand-arm configurations. Alongside the dataset, we establish a unified benchmark protocol with two tracks: Hand-only and Hand-Arm, supporting URDF-conditioned evaluation. We conduct extensive evaluations of 11 representative image-editing baselines using a multi-dimensional metric suite, including generic similarity metrics, VLM-based judgment, and embodied-aware metrics. HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI. Code, datasets, and benchmarks are available at \url{https://anonymous.4open.science/r/HandEdit}.

Modern image generation and editing systems can produce photorealistic, prompt-aligned images, but still often render familiar objects at implausible relative sizes. To measure this failure mode, we introduce GenScale, a benchmark and evaluation protocol for real-world relative object scale in image generation and editing. GenScale contains 900 image-level entries and 1,643 pairwise anchor-target scale relations across common-object generation, human-product generation with metric dimensions, and scale correction from failed generations. We further design a human-calibrated ordinal judge for scalable pairwise scale evaluation. Last but not the least, we introduce ReScale, a model-agnostic post-processing agent for localized scale correction without modifying the source generator. Experiments reveal that state-of-the-art image generators and editors cannot reliably observe relative scale yet, while ReScale consistently improves scale plausibility across generated and edited images. Together, GenScale establishes relative object scale as a distinct, measurable, and actionable capability for image generation systems.

We present GPT-IMAGE-EDIT-1M, a million-scale instruction-guided image editing dataset in which every sample includes an auditable curation decision and a traceable instruction trail. Open training data for image editing often suffers from instruction-image mismatch, weak edits, and opaque filtering; simply scaling raw data does not address these issues. To improve data quality, we regenerate edited outputs using GPT-Image-1, augment samples with complex multi-step edit instructions, and apply a policy-based quality control pipeline that assigns KEEP/RELABEL/DROP decisions with reason tags. RELABEL samples receive aligned instructions that better match the observed edit, and a second judge independently re-runs the same protocol to measure decision stability. After quality control, average quality improves from 6.90 to 7.67 under a shared rubric. Across two frozen encoder configurations, T5 and QwenVL+T5, models fine-tuned on GPT-IMAGE-EDIT-1M outperform the public FLUX.1-Kontext-dev baseline on key benchmarks, including GEdit-EN (7.22 vs. 6.26), ImgEdit (3.93 vs. 3.52), and KRISBench (66.23 vs. 49.54), while remaining competitive on Complex-Edit and OmniContext SINGLE. Controlled data-swap experiments show that GPT-Image-1 regeneration provides the dominant downstream gain, while quality control yields a smaller but consistent improvement over a size-matched random regenerated subset. Concatenating frozen QwenVL embeddings with T5 substantially improves inferential-complex benchmarks such as RISEBench and KRISBench without degrading general-editing performance. Finally, as an auxiliary diagnostic rather than a primary ranking protocol, inference-time instruction rewriting raises GEdit-EN to 7.75 and KRISBench to 78.55, suggesting that prompt specificity exposes additional instruction-following capacity in the fine-tuned editors.

Text-to-image prompt optimization is often treated as prompt rewriting: given a user prompt, a language model expands it with richer style, composition, and photographic details. While this paradigm can improve visual appeal, it may silently weaken the user's actual intent, especially when the prompt specifies fine-grained objects, attributes, and relations. We argue that faithful prompt optimization requires a different objective: improving perceptual quality while preserving the compositional structure that makes the prompt semantically correct. We propose \ours{}, a single-step reinforcement learning framework that transforms noisy scene-graph feedback into a learnable prompt-editing policy. Instead of freely rewriting the entire prompt, \ours{} first extracts a calibrated scene graph from the input and then performs conservative deletion, reordering, and insertion actions conditioned on both text and graph representations. This design exposes structured intent to the optimizer while restricting unnecessary prompt drift. To make object--relation feedback usable for policy learning, \ours{} replaces sparse thresholded grounding rewards with uncertainty-aware smooth rewards and estimates advantages with environment-aware grouping over multiple image samples from the stochastic text-to-image generator. Across prompt-optimization datasets and generation backbones, \ours{} improves the faithfulness--aesthetics trade-off, with particularly consistent gains in relational fidelity where generic rewriting and aesthetics-oriented optimization often fail. We further evaluate with both human preference and Gemini-VQA, showing that the improvements are not confined to the SG reward used during training. Appendix additionally compares against a high-budget iterative GPT-4o structural-feedback baseline to contextualize the efficiency of amortized policy learning. More broadly, our results suggest that structural constraints should not remain only post-hoc evaluators of generated images; when properly calibrated and stabilized, they can be internalized as training signals for reliable, intent-preserving prompt policies.

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.

Text-guided video editing requires applying a semantic change while keeping source motion, layout, and unchanged content stable. Recent inversion-free flow editors derive effective edit signals from target-source velocity differences, yet typically inject these signals directly at each solver step. In neural video sampling, however, this direct execution rule overlooks an important distinction: the velocity difference is an online, state-dependent observation rather than a fixed endpoint-displacement command. As the target branch evolves under previous injections, direct injection may accumulate temporal fluctuations and off-target perturbations, producing motion drift, identity flicker, or unintended changes in weak-response regions. We propose FlowTrack, a causal execution controller for inversion-free flow video editing. FlowTrack leaves the pretrained generator and raw edit signal unchanged, but executes the signal through a carried displacement-like direction and response-aware attenuation. We derive this update as the closed-form solution of a local execution objective that balances current signal tracking, temporal carrying, and weak-response suppression. On the full FiVE-Bench protocol, FlowTrack improves motion fidelity, unedited-region fidelity, and structure consistency over strong training-free diffusion- and flow-based editors while maintaining competitive target-prompt alignment. Code is available at https://anonymous.4open.science/r/FlowTrack.

In recent years, agentic workflows have been widely applied to solve complex human tasks. However, existing workflow construction still faces key challenges, including human-dependent workflow construction, the lack of graph-level execution feedback, and the inability to repair errors in-loop during long-horizon construction. To address these challenges, we propose FlowSteer, a new paradigm of Agent Designing Agentic Workflows — a single agent itself end-to-end designs the workflow that a downstream executor runs. To support this paradigm, we introduce the Workflow Canvas, a novel executable graph-state environment that returns syntax-checked execution feedback for every atomic edit. Built on the canvas, we further propose Reinforced Progressive Canvas Editing, in which a lightweight policy agent issues one atomic edit per turn conditioned on real canvas feedback, and is trained end-to-end via reinforcement learning. Moreover, FlowSteer provides a plug-and-play framework that supports diverse operator libraries and interchangeable LLM backends. Experimental results on twelve datasets show that FlowSteer significantly outperforms baselines across various tasks. Our code is available at https://anonymous.4open.science/r/FlowSteer-9B2E.

Knowledge editing has become a key technique for updating knowledge in large language models. However, existing methods neglect the geometric structure of the knowledge representation space, leading to two key limitations: they cannot distinguish which pre-trained knowledge should be preferentially preserved (core vs. non-core) nor which new edits are inherently difficult (hard vs. easy). To address this, we propose \textsc{GEdit}, a \textit{geometry-aware} framework built on a Low-Rank Plus Diagonal (LR+D) factor model that captures the intrinsic low-rank structure of Transformer FFN hidden representations. \textsc{GEdit} features two complementary mechanisms. First, the \textit{low-rank regularization} applies targeted protection on a low-rank core knowledge subspace. Second, the \textit{adaptive regularization} dynamically adjusts the regularization strength based on each edit request's editability: relaxing constraints for hard edits to ensure success, and tightening them for easy edits to better preserve pretrained knowledge. Extensive experiments on \texttt{GPT2-XL}, \texttt{GPT-J}, and \texttt{LLaMA-3-8B} under large-scale editing scenarios—including both mini-batch and fully sequential editing settings with up to 10,000 continuous updates—show that \textsc{GEdit} consistently and significantly outperforms state-of-the-art baselines in both editing success and knowledge preservation.

Vision–language models (VLMs) are increasingly used as automated judges for image-editing benchmarks, yet their reliability is poorly understood. Auditing these judges is difficult because real edited images lack controlled ground truth and entangle the requested change with model artifacts, unintended side effects, and ambiguous degrees of success. In this paper, we introduce EditJudge-Bench, a diagnostic benchmark of 1,500 synthetic before/after image pairs rendered in Blender, with edits parameterized across nine categories derived from real user requests and exact ground truth from known 3D scene parameters. Because every edit is generated by a known intervention on a controlled scene, EditJudge-Bench evaluates VLM judges as binary verifiers of whether an instruction was correctly executed, isolating judge perception from editor quality. Across 18 judge configurations, we find substantial reliability gaps: (a) the widely adopted VIEScore template reaches only 67.1\% AUROC with GPT-4o; (b) fine-tuned judges score 50.9–65.0\%; spatial edits such as rotation and shape remain near chance; (c) and prompt-formatting choices like image–text ordering shift performance by up to 13\%. Despite using only synthetic data, judge rankings on EditJudge-Bench correlate strongly with two real-image human-preference benchmarks ($\rho$=0.93 and $\rho$=0.77), indicating that controlled audits surface failure modes relevant to real evaluation.

Text-guided image editing with visual autoregressive (VAR) generators requires controlling both what the model samples and where the sampled change is written back into the image. Existing VAR editors mainly operate on token streams, features, or flat next-token logits, leaving two native structures of bitwise-residual VAR models underused: the per-bit Bernoulli prediction head and the additive multi-scale residual code field. We propose BitResEdit, a training-free editor for bitwise-residual VAR generators such as \textsc{Infinity}. BitEdit performs source-negative guidance by tilting the post-CFG per-bit log-odds along a source--target contrast computed on a shared edited prefix, then projects each update into a closed-form Bernoulli-KL trust region around the clean CFG sampler. ResEdit converts the resulting sampled bits into per-scale continuous-code residuals, gates them with a localization mask, and re-injects them through the generator's native sum-of-scales. The two components couple decision-time bit guidance with combination-time code composition, preserving masked-out latent features exactly while applying localized, scale-aware edits inside the target region. On PIE-Bench with Infinity-2B, BitResEdit achieves the strongest text alignment among same-backbone VAR editors and competitive background preservation, setting a new state of the art among training-free VAR editing methods. Ablations show that BitEdit and ResEdit play complementary roles in target alignment and background preservation. Detailed ablations show that both code-space residual editing and bitwise phrase-contrast guidance are necessary for robust autoregressive image editing.