NeurIPS 2026 论文列表

Multi-view 3D reconstruction has achieved remarkable progress with the advent of feed-forward 3D reconstruction models. However, these models are typically trained and evaluated under ideal, degradation-free imaging conditions, whereas real-world observations often contain various degradations that differ significantly from such settings. Improving robustness for multi-view 3D reconstruction under degraded conditions therefore remains an important challenge. We present Geometry-Aware Representation Denoising (GARD), a novel framework that performs diffusion-based multi-view restoration directly in the feature space of a feed-forward reconstructor. This design exploits the geometry-aware feature representations of the reconstructor to effectively recover accurate scene geometry. Furthermore, by employing a decoder, the refined representations can also be used to restore high-quality RGB images, thereby enabling the simultaneous recovery of 3D scene geometry and high-quality imagery. Comprehensive experiments on the DA3 benchmark demonstrate the effectiveness of the proposed GARD framework. Our code and weights will be publicly released for full reproducibility.

Worldwide image geolocalization aims to predict precise GPS coordinates for images captured anywhere on Earth, which is challenging due to the large visual and geographic diversity. Recent methods mainly follow two paradigms: retrieval-based approaches that match queries against a reference database, and generation-based approaches that directly predict coordinates using Large Vision-Language Models (LVLMs). However, we observe distinct error profiles between them: retrieval excels at fine-grained instance matching, while generation offers robust semantic reasoning. This complementary heterogeneity suggests that no single paradigm is universally superior. To harness this potential, we propose GeoRouter, a dynamic routing framework that adaptively assigns each query to the optimal paradigm. GeoRouter leverages an LVLM backbone to analyze visual content and provide routing decisions. To optimize GeoRouter, we introduce a distance-aware preference objective that converts the distance gap between paradigms into a continuous supervision signal, explicitly reflecting relative performance differences. Furthermore, we construct GeoRouting, the first large-scale dataset tailored for training routing policies with independent paradigm predictions. Extensive experiments on IM2GPS3K and YFCC4K demonstrate that GeoRouter significantly outperforms state-of-the-art baselines. Our dataset, code, and checkpoints are publicly available to facilitate future research.

State space models (SSMs) provide an efficient route to global image restoration because they model long sequences with linear complexity. Recent restoration architectures further enlarge the effective context by reordering image tokens so that semantically similar pixels become neighbors in the 1D scan. This strategy improves texture aggregation, but it also exposes a mismatch between the geometry of images and the discretization used by SSMs: adjacent tokens in the reordered sequence can be spatially distant, while the recurrent update still treats them as consecutive samples of a continuous process. In addition, existing prompt-based modulation relies on a finite set of discrete prompts, which is poorly matched to the continuous variation of natural textures and offers no explicit compensation for the low-pass tendency of recurrent integration. We propose GeoMamba, a geometry-aware and continuously modulated SSM for image restoration. GeoMamba introduces geometry-adaptive discretization (GAD), which conditions the SSM time-step on the Euclidean distance between consecutive reordered tokens and attenuates history propagation across spatial jumps. It also introduces a continuous dynamic matrix (CDM), which regresses pixel-wise modulation parameters from feature and gradient cues to reduce prompt quantization and preserve high-frequency details. Experiments on lightweight and classical image super-resolution demonstrate consistent improvements over strong Transformer- and Mamba-based baselines, while achieving better or competitive performance in additional denoising and JPEG artifact reduction settings.

Ultra-high-resolution (UHR) remote-sensing imagery captures fine-grained spatial details essential for city-scale Earth observation, yet it exposes a fundamental limitation of current multimodal large language models (MLLMs): native-resolution inputs exceed model capacity, while aggressive downsampling erases the very small objects that many questions hinge on. Generation-understanding unified multimodal models offer a promising remedy, since they can produce intermediate visual observations before answering. However, whether such capabilities genuinely improve UHR remote-sensing understanding remains an open question. Existing UHR benchmarks cannot adjudicate it: they are mostly built from mature datasets covering only a few regions, their image scales remain below real city-level satellite scenes, and they do not systematically identify which tasks benefit from intermediate visual generation. We introduce GeoG2U-Bench, a global benchmark designed to evaluate Generation-to-Understanding (G2U) in UHR remote sensing. GeoG2U-Bench spans 300 cities worldwide, with every raw satellite scene reaching $20{,}000 \times 20{,}000$ pixels, matching the scale at which UHR analysis actually occurs. It comprises 20 subtasks across five capability dimensions and roughly 3,000 expert-verified samples, each requiring models to produce auditable intermediate artifacts such as zoom-in crops, annotated local views, cross-region relation maps, and temporal rewind images. Beyond data, GeoG2U-Bench contributes a dual-protocol evaluation: Direct (answer from the raw input) versus Generate-then-Answer (generate an intermediate visual artifact, then answer), which isolates and quantifies the contribution of generation to downstream understanding. Experiments across several model families reveal that generation is not uniformly beneficial: unfaithful or off-task artifacts can actively degrade performance, whereas reliable, task-relevant artifacts yield consistent gains in cross-scale localization, multi-region comparison, temporal reasoning, and spatial layout understanding. These results show that visual generation can support UHR remote-sensing understanding, but only when it provides reliable visual evidence for the task.

Text-to-image models can generate plausible street scenes from prompts such as “a street in New York City,” but it remains unclear whether these images resemble the requested place or show a generic city appearance. Commonly used evaluation metrics mainly measure image quality or text alignment and do not directly assess geographic fidelity. We introduce GeoFidelity-Bench, a benchmark for evaluating whether generated street-view images match a target location at the level of named street blocks. The benchmark contains 112 street blocks from 25 cities across six continents, each paired with a panel of real Mapillary images and geographic metadata. From this metadata we build prompts with the city only, with street and neighborhood names, and with these names plus raw GPS coordinates. Across six open-weight text-to-image models, street and neighborhood names improve similarity to the target location, while raw GPS coordinates add only a small and uneven gain. Control prompts that swap in the names of other blocks while keeping the city fixed reduce retrieval accuracy, with larger decreases when the neighborhood name or both names are replaced. Generated images also distinguish their target blocks from local alternatives less reliably than held-out real images. CLIP alignment between generated images and their prompts is nearly uninformative about whether the images achieve block-level geographic fidelity. GeoFidelity-Bench provides a controlled testbed for studying location-conditioned generation and for measuring progress beyond generic city-level plausibility.

Pruning reduces the model size and inference cost of large language models (LLMs), yet preserving performance across diverse downstream tasks remains challenging. Existing structured pruning methods commonly rely on local discrepancy measures or teacher-forced predictive criteria, such as Block Influence (BI) and perplexity (PPL). While these methods are effective on classification benchmarks, the resulting pruned models often degrade severely on generative reasoning tasks. We study redundancy in Transformer-based models and observe that pruning-induced generation drift is strongly correlated with the functional importance of modules, suggesting a more faithful criterion for identifying redundant modules. Based on this insight, we propose GDPruner, a calibration-free, generation-drift-guided block pruning method. GDPruner constructs lightweight self-generated probe trajectories, estimates module importance using tail-position generation drift, and removes redundant modules through adaptive search. Extensive experiments show that GDPruner surpasses state-of-the-art structured pruning baselines while better preserving overall performance and complex generative reasoning ability, offering a promising direction for generation-robust LLM deployment.

Despite rapid advances in text-to-image generation, faithfully realizing user intent remains challenging, often requiring manual multi-turn trial and error. To automate this process, existing systems rely on either simple prompt rewriting or closed-loop agents driven by hand-crafted rules, rather than learning to adapt actions to the evolving generation process. In this paper, we reformulate image generation as a state-conditioned action-making problem and propose Generation Navigator, a multi-turn T2I agent that learns to dynamically steer the generation trajectory and output the next action. However, training this agent via reinforcement learning introduces a critical credit assignment challenge: naively rewarding a trajectory based solely on a single state assigns equal credit to all actions in the rollout, ignores the quality dynamics across turns, and fails to distinguish actions that improve the trajectory from those that degrade it or waste turns without progress. We resolve this with PRE-GRPO (Peak-Retention-Efficiency Group Relative Policy Optimization), a trajectory-level reinforcement learning objective that explicitly rewards discovering a high-quality image (Peak), avoiding subsequent quality degradation across turns (Retention), and minimizing unnecessary turns (Efficiency). Experiments show substantial improvements across benchmarks, reaching a WISE score of 0.90 and 79.06% reasoning accuracy on T2I-ReasonBench.

Individual-level wildlife identification often suffers from data scarcity, as varying observations of the same animal under diverse poses, viewpoints, and motions are rarely available. Image-to-video (I2V) generation offers a promising way to mitigate this limitation by synthesizing additional observations from a single reference image. However, existing I2V models mainly emphasize global layout, semantics, and motion, and therefore often fail to preserve fine-grained local appearance cues that distinguish one wildlife individual from another, such as fur texture, stripe boundaries, spot configurations, and contour transitions. We observe that these identity-critical cues are closely related to high-frequency information, which is essential for distinguishing individuals. To address this challenge, we propose WildIcon, a high-frequency-guided I2V framework for wildlife individual consistency. Specifically, WildIcon introduces a frequency-aware identity encoding branch that extracts individual-specific high-frequency cues from the reference image. Combined with isolated foreground information, the resulting identity tokens are then injected into cross-attention blocks as identity conditioning. Building on a frozen backbone with lightweight identity adaptation, WildIcon strengthens fine-grained identity preservation while retaining the motion controllability and semantic fidelity of the base I2V model. In addition, to support the training and evaluation of wildlife individual-consistent I2V, we construct WildlifeVid, a wildlife-centric video dataset with high-quality, temporally coherent clips and individual-level identity labels. Experiments on both I2V and downstream animal re-identification (ReID) show that WildIcon achieves stronger individual consistency than existing baselines and provides useful, reliable synthetic data to improve downstream ReID performance.

Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI research, style has long been treated as a surface-level visual property: a filter of color, brushstroke, and texture applied to otherwise content-neutral scenes. We introduce the first dataset to investigate the interplay between gender representation and style in both historical and generated images. StyleGender comprises 74k images spanning 19 artistic styles, comprising art historical images with style and gender annotations, T2I-generated images under controlled style and gender prompts, and a semantically aligned set enabling direct art history-to-generation comparison. By proposing two Set Gender Artifact (SGA) metrics (PixelSGA and MaskSGA), capturing gender signals at the pixel level and in compositional structure, we show that (1) gender representation shapes visual features across artistic styles, (2) style keywords carry these patterns into T2I generation, and (3) generative models tend to amplify gender artifacts beyond what is observed in historical sources.

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.

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a general image-generation agent that can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, a self-evolving framework based on Tool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as a tool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired by on-policy self-distillation, Visual Experience Distillation provides dense token-level supervision, helping the student internalize better search, knowledge activation, reference selection, and prompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks.

Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally constrained by frozen internal knowledge, thus often failing on real-world scenarios that are knowledge-intensive or require up-to-date information. In this paper, we present Gen-Searcher, as the first attempt to train a search-augmented image generation agent, which performs multi-hop reasoning and search to collect the textual knowledge and reference images needed for grounded generation. To achieve this, we construct a tailored data pipeline and curate two high-quality datasets, Gen-Searcher-SFT-10k and Gen-Searcher-RL-6k, containing diverse search-intensive prompts and corresponding ground-truth synthesis images. We further introduce KnowGen, a comprehensive benchmark that explicitly requires search-grounded external knowledge for image generation and evaluates models from multiple dimensions. Based on these resources, we train Gen-Searcher with SFT followed by agentic reinforcement learning with dual reward feedback, which combines text-based and image-based rewards to provide more stable and informative learning signals for GRPO training. Experiments show that Gen-Searcher brings substantial gains, improving Qwen-Image by around 16 points on KnowGen and 15 points on WISE. We hope this work can serve as an open foundation for search agents in image generation, and we fully open-source our data, models, and code.

Recent advances in visual storytelling have leveraged powerful text-to-image diffusion models to generate sequences of images featuring consistent subjects and styles. While such methods achieve impressive identity preservation across the sequence, they often produce scenes with limited structural diversity---images that are overly similar in subject pose and spatial layout. Existing evaluation protocols primarily measure identity consistency using metrics such as DreamSim, which can inadvertently conflate identity preservation with subject pose. The inability to independently evaluate structural diversity inhibits progress on identity preserving image generation methods. We address this limitation by introducing a new metric based on the Gromov Wasserstein distance to quantify structural diversity independently of identity. Our metric provides an interpretable and generalizable measure of spatial variation across generated images, enabling a more balanced assessment of identity consistency and pose diversity. Using this framework, we systematically evaluate several recent visual storytelling and subject-consistent generation methods. Furthermore, we evaluate the effect of latent initialization methods on structural diversity in several popular text-to-image models.

Autoregressive decoding with LLMs is primarily bottlenecked by GPU memory bandwidth, especially in edge-computing settings. While quantization is essential for mitigating this bottleneck, most existing methods treat inference as uniform process and fail to account for the asymmetry between the compute-bound prefill stage and the memory-bound decoding stage. We propose GRINQH (GRaded INput-based Quantization Hierarchy), a weight-only post-training quantization framework that accelerates decoding by unifying quantization and sparsification. GRINQH leverages activation magnitudes as a proxy for computational importance to dynamically assign weight channels to different precision levels, enabling flexible average bit widths during decoding. Evaluated on Llama3 and Qwen3 models, GRINQH outperforms state-of-the-art fixed- and mixed-precision baselines at comparable 3- and 4-bit settings, even enabling effective 2-bit generation. We experimentally verify theoretical speedups by leveraging a hierarchical nested memory layout for multi-precision storage in custom GPU kernels. Together, GRINQH establishes a new state-of-the-art Pareto frontier for LLM generation, enabling a dynamic trade-off between generation quality and inference speed.

Text-to-image flow matching transformers degrade sharply in long-tail settings: tail-class outputs collapse in fidelity and diversity, limiting their value as synthetic augmentation for rare conditions. We trace this to low head-versus-tail gradient alignment during fine-tuning, an optimization-level pathology that conditioning- and sampling-side interventions do not address. We propose GRASP (Guided Residual Adapters with Sample-wise Partitioning): a deterministic partition of the conditioning space, paired with group-specific residual adapters in the transformer feedforward layers, that leaves the flow-matching objective and the sampler untouched. In conditional flow matching, condition values index distinct sets of probability paths, so partitioning along the conditioning is the structurally correct factorization suitable as gradient alignment proxy. Because the partition is static, every tail sample is guaranteed to update its assigned expert, which bypasses extreme longtail failure modes. Crucially, GRASP is non-invasive and composable: on MIMIC-CXR-LT, combining GRASP with self-guided minority sampling at inference time yields the best all-labels IRS we observe, beyond either intervention alone. GRASP itself reduces overall FID by up to 80\% and lifts tail-class coverage by up to 44\% over full fine-tuning, learned-routing MoE, and minority guidance. Used as training data for a downstream DenseNet classifier on NIH-CXR-LT, GRASP synthetics significantly outperform every non-GRASP alternative on macro F1, match the macro F1 obtained from real training data, and yield nonzero F1 on $9$ of $13$ classes versus $3$ of $13$ from full fine-tuning. Results on ImageNet-LT confirm the mechanism is not tied to medical inductive bias.

Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose \textbf{G}ated and \textbf{A}daptive \textbf{R}egularization with \textbf{D}iversity-aware \textbf{O}ptimization (\textbf{GARDO}), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness.

Real-world multi-modal image fusion faces two fundamental challenges: spatial misalignment and complex degradations induced by heterogeneous imaging sensors. These factors are intrinsically coupled, but most existing fusion methods typically address only one of them, inevitably leading to failure under the other challenge. In this paper, we propose G2Fusion, the first fusion framework that simultaneously addresses image registration and information restoration. By developing a novel geometric-to-generative paradigm, it can directly produce high-quality fused images from unregistered and degraded inputs captured by heterogeneous imaging sensors. This framework consists of two key modules: a flow-based geometric deformation reduction module (Flow-GDR) and a DiT-based generative fusion module (DiT-GF). The former reduces large non-rigid discrepancies through dense flow estimation. The latter performs generative fusion, progressively refining residual misalignments while restoring degraded content through iterative denoising. To enable effective interaction among registration, restoration, and fusion, we design two complementary mechanisms. On the one hand, a target distribution mining strategy is introduced to construct a joint objective distribution from registration, restoration, and fusion, effectively guiding the optimization of DiT-GF. On the other hand, we develop a mutual promotion mechanism that establishes a closed-loop interaction between Flow-GDR and DiT-GF by re-estimating the residual deformation between the fused output and the infrared reference. Extensive experiments demonstrate that G2Fusion consistently outperforms state-of-the-art methods in terms of registration, restoration, and fusion.

Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcome this, we introduce G-Zero, a verifier-free, co-evolutionary framework for autonomous self-improvement. Our core innovation is \textbf{Hint-$\delta$}, an intrinsic reward that quantifies the predictive shift between a Generator model's unassisted response and its response conditioned on a self-generated hint. Using this signal, a Proposer model is trained via GRPO to continuously target the Generator's blind spots by synthesizing challenging queries and informative hints. The Generator is concurrently optimized via DPO to internalize these hint-guided improvements. Theoretically, we prove a best-iterate suboptimality guarantee for an idealized standard-DPO version of G-Zero, provided that the Proposer induces sufficient exploration coverage and the data filteration keeps pseudo-label score noise low.. By deriving supervision entirely from internal distributional dynamics, G-Zero bypasses the capability ceilings of external judges, providing a scalable, robust pathway for continuous LLM self-evolution across unverifiable domains.

The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on understanding-side visual token reduction for improving the efficiency of separate-encoder UMMs. While this topic has been widely studied for MLLMs, existing methods typically rely on attention scores, text-image similarity and so on, implicitly assuming that the final objective is discriminative reasoning. This assumption does not hold for UMMs, where understanding-side visual tokens must also preserve the model’s capabilities for editing images. We propose G$^2$TR, a generation-guided visual token reduction framework for separate-encoder UMMs. Our key insight is that the generation branch provides a task-agnostic signal for identifying understanding-side visual tokens that are not only semantically relevant but also important for latent-space image reconstruction and generation. G$^2$TR estimates token importance from consistency with VAE latent, performs balanced token selection, and merges redundant tokens into retained representatives to reduce information loss. The method is training-free, plug-and-play, and applied only after the understanding encoding stage, making it compatible with existing UMM inference pipelines. Experiments on image understanding and editing benchmarks show that G$^2$TR substantially reduces visual tokens and prefill computation by **1.94$\times$** while maintaining both reasoning accuracy and editing quality, outperforming baselines on almost all benchmarks.