NeurIPS 2026 论文列表

Conditioning diffusion models on visual features is essential for controlled image generation and counterfactual reasoning across diverse applications, from creative design to medical imaging. Existing conditioning methods, such as timestep embedding and cross-attention mechanisms, suffer from limited control over feature accuracy, require substantial architectural modifications, and struggle to maintain conditioning precision throughout the denoising process, as the model's focus shifts toward fine-grained details in later steps rather than the desired conditioned features. We present a novel approach that addresses these limitations by learning to shift the initial noise distribution in the latent space. Our method assigns each visual feature a distinct direction in the latent space and applies proportional shifts during both training and inference, enabling precise control over continuous and discrete visual attributes. We introduce a combined loss function that explicitly enforces shift accuracy alongside denoising, ensuring consistent conditioning across all denoising steps. Furthermore, we extend this framework to datasets with hidden or inaccessible visual features by employing a Variational Autoencoder to extract latent representations in an unsupervised manner. This enables high-fidelity counterfactual generation on complex, real-world datasets where explicit feature annotations are unavailable or prohibitively expensive to obtain.

Classifier-free Guidance (CFG) is central to modern diffusion models, but operates as an inference-time correction where the training objective learns the unbiased conditional score, while guided sampling targets a posterior-tilted score that never explicitly learned. Rather than another inference-time patch or distillation, Model-guidance (MG) directly integrates the posterior tilt into the training objective and eliminates CFG during inference. MG serves as a plug-and-play module compatible with existing methods, yet accelerates convergence by online self-bootstrapping and doubles inference speed. Unlike distillation surrogates, a reparameterisation property reveals that MG implicitly learns the vanilla score without mode collapse. Extensive experiments demonstrate that MG matches and even surpasses CFG baselines, and achieves a state-of-the-art FID of $1.34$ on ImageNet $256$ benchmark.

Diffusion language models (DLMs) provide a bidirectional generation framework naturally suited for infilling, yet their performance is constrained by the pre-specified infilling length. In this paper, we reveal that DLMs possess an inherent ability to discover the correct infilling length. We identify two key statistical phenomena in the first-step denoising confidence: a local Oracle Peak that emerges near the ground-truth length and a systematic Length Bias that often obscures this signal. By leveraging this signal and calibrating the bias, our training-free method CAL (Calibrated Adaptive Length) enables DLMs to approximate the optimal length through an efficient search before formal decoding. Empirical evaluations demonstrate that CAL improves Pass@1 by up to 47.7% over fixed-length baselines and 40.5% over chat-based adaptive methods in code infilling, while boosting BLEU-2 and ROUGE-L by up to 8.5% and 9.9% in text infilling. These results demonstrate that CAL paves the way for robust DLM infilling without requiring any specialized training.

Recent text-to-image (T2I) diffusion models have achieved remarkable advancement, yet faithfully following complex textual descriptions remains challenging due to insufficient interactions between textual and visual features. Prior approaches enhance such interactions via architectural design or handcrafted textual condition weighting, but lack flexibility and overlook the dynamic interactions across different blocks and denoising stages. To provide a more flexible and efficient solution to this problem, we propose Diff-Aid, a lightweight method that adaptively adjusts per-token text and image interactions across transformer blocks and denoising timesteps. Beyond improving generation quality, Diff-Aid yields interpretable modulation patterns that reveal how different blocks, timesteps, and textual tokens contribute to semantic alignment during denoising. As a plug-and-play module, Diff-Aid can be seamlessly integrated into downstream applications for further improvement, including style LoRAs, controllable generation, and zero-shot editing. Experiments on strong baselines (SD~3.5 and FLUX) demonstrate consistent improvements in prompt adherence, visual quality, and human preference across various metrics. Our code and models will be released.

More than 80% of the 1.6B English speakers do not use Standard American English (SAE), yet LLMs often fail to correctly identify non-SAE dialects and generate stereotyped responses for their speakers. We introduce **DialectLLM**, the first large-scale framework for generating high-quality multi-dialectal conversational data encompassing the three pillars of written dialect---lexical (vocabulary), orthographic (spelling), and morphosyntactic (grammar) features. DialectLLM produces a dialect-parallel dialog dataset spanning nine English dialects. Partnering with native linguists, we design and validate SAE-to-dialect transformation rules, ensuring authenticity. Our approach challenges the prevailing practice of applying a single morphosyntactic feature set to both user utterances and model responses, showing that models should not reproduce up to 90% of the grammatical features of a dialect. Human evaluation confirms data quality, with annotators preferring DialectLLM over prior methods in 98.8% of pairwise comparisons for dialect naturalness. We then construct **DialectLLM-Bench**, a dialect-parallel benchmark with 50k+ dialogs, resulting in 97k+ QA pairs, and evaluate 17 LLMs on dialect identification and response generation tasks. Even frontier models achieve under 70% accuracy, fail to reach 50% for prominent dialects like Canadian English, and systematically misclassify non-SAE dialects as American or British. Beyond benchmarking, we show that DialectLLM data also serve as a scalable LLM post-training resource, suggesting a practical path toward dialect-aware conversational AI.

Diffusion Transformers (DiTs) achieve state-of-the-art image generation quality but incur substantial memory and computational costs at inference. While aggressive Post-Training Quantization (PTQ) to 4-bit precision offers significant efficiency gains, it typically results in severe quality degradation. Existing approaches, including smoothing-based methods, mixed-precision schemes, rotation techniques, and low-rank residual methods, partially mitigate this issue but still leave a noticeable gap to FP16/BF16 performance. In this work, we introduce *DiRotQ*, a W4A4 PTQ framework that mitigates this degradation through rotation-aware activation quantization. DiRotQ identifies a low-rank subspace capturing dominant activation variance via Principal Component Analysis (PCA), preserving coefficients in this subspace at higher precision while quantizing the remaining components to 4-bit. Activations are rotated into the PCA basis at inference time using calibration-derived orthogonal transformations, while the inverse rotation is fused into the layer weights offline. Combined with GPTQ-based weight quantization, DiRotQ achieves an FID $(\downarrow)$ of $15.9$ and PSNR $(\uparrow)$ of $19.1$ dB on PixArt-$\Sigma$ over the MJHQ-30K dataset, outperforming the prior state-of-the-art SVDQuant (FID $18.9$, PSNR $17.6$) under the same INT W4A4 setting. Beyond standard metrics, we introduce a *VLM-as-a-Judge* evaluation protocol for diffusion model quantization, the first such evaluation in this setting, providing a more holistic assessment of perceptual quality and prompt alignment under aggressive compression. On the systems side, we implement a *Triton-based custom kernel* to enable efficient end-to-end inference, reducing memory usage of the 12B FLUX.1-dev model by $2.1\times$ and delivering $2.3\times$ speedup over the BF16 baseline, on a 24GB RTX 4090 GPU. Our anonymous codebase is available [here](https://anonymous.4open.science/r/DiRotQ-5BF6/).

Diffusion models are a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in the dense-graph setting through the lens of graphons, the size-agnostic limit object of dense graph sequences, to study how structural graph statistics behave across node-size scales. This perspective leads to DiPhon, a diffusion process for size-scalable graph generation. Specifically, we formulate a continuous diffusion process on the graphon space via a Jacobi stochastic differential equation (SDE), and propose DiPhon, a discretized scheme that imitates it on graphs. We further derive the corresponding reverse-time process, which requires access to the marginal score. For the Jacobi process, this score interestingly admits a tractable form, which we estimate from data via graph denoising and plug into the reverse process to generate graph samples. We prove that DiPhon matches the first moment of the marginal distributions induced by the continuous graphon process exactly, and approximates the second moment up to a closed-form discrepancy. In this way, DiPhon inherits the size-agnostic statistical properties of the graphon dynamics can be used to solve the scaling bottleneck. Empirically, we demonstrate the scalability of DiPhon by training on small graphs and generating substantially larger ones at inference time, without retraining on large graphs.

Diffusion language models (Diffusion-LMs) generate text through iterative denoising, exposing a temporal structure that is largely absent from autoregressive decoding. In this paper, we show that this temporal structure provides a useful control axis for generation diversity: early denoising steps mainly determine high-level semantic trajectories, while later steps refine lexical realization. Motivated by this observation, we propose Time-Annealed Perturbation Sampling (TAPS), a training-free inference strategy that samples nearby conditioning trajectories through time-aware, manifold-constrained perturbations. TAPS encourages semantic branching during early denoising and anneals the perturbation away before refinement, improving exploration while preserving prompt alignment, generation quality, and reasoning ability. Experiments on multiple Diffusion-LM backbones, including non-autoregressive and semi-autoregressive models, show that TAPS consistently improves semantic and lexical diversity across open-ended and instruction-following generation tasks, while preserving reasoning ability on verifiable reasoning benchmarks with negligible overhead.

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.

In recent years, AIGC technologies are capable of generating hyper-realistic forged facial images, posing severe threats to facial security safety. To tackle these challenges, early Deepfake research primarily centered on real-fake classification. Recently, growing efforts have been devoted to the Deepfake Attribution (DFA) of generated content. Nevertheless, existing research on Deepfake detection and attribution faces saturated performance in binary classification, limited diversity in datasets and algorithms, and imperfect evaluation protocols, which severely impede practical application. To address these limitations, we propose a comprehensive deepfake detection and attribution benchmark named DeepfakeGenome (DFG). It contains 100 facial forgery algorithms and 2M images in total, achieving 4× to 100× larger than prior DFA benchmarks. We further designed 4 protocols for practical evaluation, including a novel retrieval-based attribution paradigm. Unlike previous open-set evaluation metrics, the proposed retrieval metrics are more aligned with the real-world active defense situation of blacklist registration mechanisms. Based on these elaborate designs, we investigate the performance ceiling of deepfake attribution task. Over 2k+ experimental evaluations are conducted, and 10 insightful findings are derived. We hope this work can provide new insights into the DFA research field.

Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to the correct entity. A clinical RAG response can pass every automated check (zero hallucinations, near-perfect faithfulness, real citations) while presenting drug $Y$'s clinical evidence as evidence about queried drug $X$. We term this **deceptive grounding** (DG): a failure invisible to faithfulness, hallucination, and citation checks because every claim is sourced from a real document, about the wrong entity. Using a controlled factorial benchmark across 13 models, we find DG rates spanning 8--87\% at peak adversarial conditions. Medical and biomedical fine-tuned models reach up to 86.7\%; domain specialization amplifies the failure rather than mitigating it. A controlled ablation identifies the mechanism: removing entity-specific clinical evidence from retrieved documents eliminates entity-attribution failure entirely, shifting all failures to confabulation. The two failure modes respond to the same trigger, taking different paths. Production measurement across 740 drug--disease pairs finds 7.8\% overall DG in a deployed RAG system, rising to 13.6\% for recently approved drugs. Entity-attribution verification (checking that cited evidence applies to the queried entity) detects DG at 92.3\% precision and 95.8\% DG recall; no existing framework implements it.

Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactual distribution as a standalone generative target, without exploiting its relationship to the observational data. In this work, we show that under standard assumptions, observational and counterfactual outcome distributions are tightly linked: they have identical support and tail behavior, remain statistically close under weak confounding, and share any features of high-dimensional outcomes which are invariant to confounders. These properties motivate learning counterfactual distributions not from scratch, but via a *deconfounding flow* from the observational distribution. We formulate this problem via flow-matching and derive a semiparametrically efficient estimator based on a novel efficient influence function correction. We subsequently extend our estimator to target minimal-energy flows in high-dimensions, which we show can be especially simple targets between observational and counterfactual distributions. In experiments, deconfounding flows outperform existing debiased counterfactual distribution estimators, while also mitigating known failure modes of flow-based methods.

Online reinforcement learning (RL) has emerged as a powerful paradigm for aligning text-to-image flow matching models with complex user intent. However, existing methods typically rely on sparse rewards computed only from final decoded images. This delays credit assignment across the denoising trajectory and makes training computationally expensive. Moreover, simply combining multiple rewards does not reliably produce a Pareto trade-off, where gains in compositional accuracy often degrade aesthetic quality. In this work, we propose \textbf{DeFlowCritic}, an efficient online RL framework based on Dense Latent Reward Alignment. Unlike prior work, our method provides dense supervision on intermediate latents, enabling early-stage guidance for the denoising trajectory. To achieve this, we introduce an internal critic that evaluates noisy latents using features extracted directly from the diffusion model, a representation inherently better suited for latent-space reward estimation than standard image encoders. We construct a high-quality reward-modeling dataset with continuous scores for critic training, jointly capturing compositional correctness and aesthetic preference. The resulting dense reward signals enable efficient online RL training and inference scaling. Experiments show that our method achieves a superior balance between prompt faithfulness and visual quality on GenEval2 and T2I-CompBench benchmarks.

The development of deep learning-based medical AI depends on high-quality annotated data. Yet, dataset creation remains constrained by the need for expert radiologist annotations, limiting curated datasets to relatively small cohorts. To overcome this limitation, recent efforts increasingly aggregate disparate public datasets into large-scale dataset collections. However, combining datasets at scale often obscures data provenance, introducing significant risks of reuse propagation, unintended leakage, bias amplification and distorted data distributions. In this work, we study these problems on a massive collection of 760k 3D radiological images across 1069 public datasets and provide practical resources to address it. Firstly, we formalize dataset collections as a distinct paradigm in medical image analysis and characterize their structural challenges, including reuse propagation, duplication, and provenance fragmentation. Secondly, we perform one of the first large-scale empirical studies of duplication on medical image collections and demonstrate extensive exact and near-duplicate reuse across public datasets via perceptual hashing and manual tracing on our 760k 3D radiological images across 1069 public datasets, with 118k near or exact duplicates. We make these hashes publicly available, thereby providing a standardized reference for identifying potential overlaps and shared provenance for researchers. Thirdly, owing to the insufficient robustness of hashing in isolation, we introduce MAP (Metadata for Aggregation and Provenance), a lightweight, portable metadata profile for documenting provenance, known overlaps, and deduplication decisions in dataset collections. Our contributions enable dataset collections to shift from coarse aggregations to reliable and provenance-aware data scaling mechanisms in medical image analysis. A webtool supporting our work is made available here:

Video diffusion models (VDMs) have achieved impressive progress in text-to-video generation, but their high memory and computational costs hinder practical deployment. Quantization-aware training (QAT) is an effective solution for compressing and accelerating advanced generative models without runtime overhead at inference. % However, existing QAT methods suffer from a distinctive challenge in VDMs: while they often preserve prompt semantics, global layout, and coarse motion, the quantized model severely degrades visual details, texture fidelity, and sharpness. In this paper, we trace this degradation to the timestep-agnostic design of conventional quantization pipelines, which overlooks the stage-wise functionality of video denoising. In VDMs, early denoising steps mainly establish global structure and motion, whereas middle and late steps refine local appearance and high-frequency details. Based on this insight, we propose \textbf{DSAQuant}, a \textbf{D}enoising-\textbf{S}tage-\textbf{A}ligned \textbf{Quant}ization-aware training framework for VDMs. During training, \textit{Denoising-Stage Oriented Supervision} preserves teacher distillation in early steps for stable structure planning, while shifting later steps toward target-driven optimization to enhance detail reconstruction. During inference, \textit{Denoising-Stage Gated Guidance} disables CFG in the final denoising steps to prevent it from amplifying quantization-induced errors into high-frequency artifacts. Extensive experiments on the Wan and CogVideoX families under W4A4 and W3A3 settings show that DSAQuant consistently outperforms the SOTA QAT baseline, improving the VBench average score by up to 6.60 under aggressive W3A3 quantization while preserving strong text-video alignment. These results demonstrate that effective VDM quantization requires not only reducing quantization error, but also aligning quantization training and inference with the stage-wise nature of video diffusion.

AI-generated image detectors face significant challenges when deployed in real-world environments, particularly when test samples are produced by unseen generators that deviate from the source training distribution. Existing detectors often rely on a single type of evidence, such as semantic representations or low-level generation traces, making them vulnerable when the corresponding cue becomes unreliable. Moreover, adapting detectors to newly emerging generators usually requires target labels or retraining, which is costly and impractical. To address these challenges, we propose Dual-Evidence Disagreement-Constrained Adaptation (DEDCA), a test-time adaptation framework for generalized AI-generated image detection. DEDCA constructs a dual-evidence detector by combining CLIP-based semantic evidence with trace evidence extracted from shuffled entropy and residual statistics, and introduces a reliability-aware gate to perform sample-specific adaptive fusion. Our key idea is to exploit the disagreement between the two evidence streams as an unlabeled adaptation signal, enabling the detector to adjust to target-domain shifts without relying on target labels or blindly trusting a single prediction stream. Experiments show that DEDCA achieves superior performance on the GenImage benchmark by outperforming state-of-the-art AI-generated image detectors and test-time adaptation methods with 92.0\% average accuracy.

Pathological whole slide images (WSIs) from different hospitals often exhibit severe domain shifts due to variations in scanning devices, staining protocols, and tissue preparation procedures. This leads to significant performance degradation when a trained model is applied to data from different sources. Although unsupervised domain adaptation (UDA) has achieved strong performance in transferring a model trained on a source domain to an unlabeled target domain in natural images, it is much less studied in the computational pathology field. This is due to the unique properties of WSIs, including ultra-high resolution and strong inter-slide and intra-slide heterogeneity. As a result, existing UDA methods are often unstable and may even cause negative transfer in WSI classification. To address this issue, we propose Dual-level Adversarial learning with domain-aware REgularization (DARE) for WSI Classification. Specifically, we design an adaptive pseudo-labeling framework with a teacher-student architecture to provide stable supervision for the target domain and perform adversarial alignment on both patch-level and slide-level embeddings. This enables joint optimization of patch-level and slide-level semantic embeddings for dual-level alignment across domains. We also introduce domain-aware attention masking to regularize attention and improve cross-domain generalization. We evaluate our method on four public WSI datasets through extensive experiments. The results show that our method consistently outperforms state-of-the-art approaches across various domain shifts.

We present CyCLeGen, an autoregressive framework that integrates layout understanding and layout-to-image generation through cycle consistency: predicted layouts must produce faithful images, and generated images must yield consistent layouts. We enforce this constraint via CycleGRPO, a bidirectional reinforcement learning strategy with complementary geometric and perceptual rewards, enabling self-introspective learning from only 8k RL samples. This creates a natural loop, generation helps understanding by rewarding only those layouts that lead to high-quality images, and understanding helps generation by rewarding only those images whose spatial structure can be faithfully recovered. Extensive experiments show that CyCLeGen achieves significant gains across diverse image understanding and generation benchmarks, with emergent gains on image captioning.

Personalized portrait generation aims to synthesize portraits that align with both the identity of the reference face and the semantics of the text. However, existing methods suffer from two critical challenges. First, they exhibit a trade-off between ID fidelity and text controllability. We identify that this limitation stems from the self-supervised training, where the model learns a trivial mapping rather than understand identity. Second, despite exploring various encoding strategies, recent approaches still tend to discard fine-grained facial details. To address these challenges, we propose CrossID-11M, a large-scale curated portrait dataset comprising 11 million images with high-quality annotations, paired with millions of identity-preserving face video clips. Furthermore, we introduce CrossID, a novel framework integrating a lightweight GatedFaceAdapter and a cross-supervised training paradigm. Additionally, we establish CrossID-Bench alongside new VLM-based evaluation metrics. Extensive experiments demonstrate that CrossID successfully breaks the trade-off between ID fidelity and controllability, achieving state-of-the-art performance by producing photorealistic images with faithfully preserved ID details.