NeurIPS 2026 Papers

Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curation strategies. We introduce DataComp for VLMs (DCVLM), a benchmark for controlled data-centric experiments to improve VLM training. As part of DCVLM, we standardize 160 datasets spanning four data types—image-caption pairs, multimodal interleaved documents, text-only, and instruction-tuning data—into a corpus of 6T multimodal tokens. DCVLM allows participants to test curation strategies (filtering, mixing, formatting, sampling) across 1B–8B models and 6.25B–200B token budgets. Models are then evaluated on a carefully selected suite of up to 52 downstream benchmarks across 9 domains. We conduct extensive experiments on DCVLM and find that data mixing, not filtering, is key to a high-quality training dataset: instruction-heavy mixtures scale better than caption-heavy ones, with gains widening at larger scales. The resulting dataset, DCVLM-BASELINE, enables training an 8B VLM to 63.6% accuracy on our 33-task core suite with 200B training tokens. Compared to FINEVISION, the state-of-the-art open VLM training dataset, this represents an improvement of +5.4pp. DCVLM and all accompanying artifacts will be made publicly available.

Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task. Existing cold-start methods typically either train the backbone throughout the stream and compensate for semantic drift, or freeze a backbone after the first task, producing features biased toward the initial classes. These choices also create a computational tension: drift-compensation methods require repeated backbone training and increasingly expensive updates as the task horizon grows, while frozen-backbone methods are cheap but weak under cold start. We study a third option: a feature extractor that is never fit to image data at all. We propose CIRCLE, a class-incremental classifier built from fixed bidirectional two-dimensional reservoir features, adapted from BiRC2D for image classification, and streaming linear discriminant analysis heads. CIRCLE groups multiple random reservoir instantiations into feature ensembles and averages the softmax outputs of independent SLDA heads, yielding a tunable bias-variance tradeoff between richer random features and prediction-level ensembling. Because the feature extractor is fixed and the head admits streaming closed-form updates, CIRCLE performs sample-wise training without replay, task-boundary information, or backbone backpropagation. On CIFAR-100, TinyImageNet, ImageNet-Subset, and ImageNet-1k, CIRCLE is competitive at 10-20 task splits and substantially outperforms strong CS-EFCIL baselines at 50, 100, and 500 task splits, while training much faster than trained-backbone drift-compensation methods. Ablations show that the BiRC2D-style extractor, SLDA head, and balanced feature/prediction ensembling each contribute to the final performance.

Data-free methods for analysing and understanding the layers of neural networks offer many metrics for quantifying notions of `strong' versus `weak' layers, with the promise of increased interpretability. In particular, random matrix theory (RMT) offers data-free metrics that claim predictive power over the quality of pre-trained models at a layerwise precision and the ability to identify model pathologies, indicating a unique relationship with generalisation. As a result, metrics from RMT are championed for pre-training optimisation strategies and post-training compression. We establish that RMT-based metrics are unrelated to performance or training by showing that functionally indistinguishable reparametrisations of a pre-trained model can have arbitrary metrics. We show this across a range of architectures and scales. To create functionally indistinguishable reparametrisations, we exploit the well-established phenomenon of criticality: some layers can be re-initialised or re-randomised without affecting the functional behaviour of the model -- they are called robust -- while others cannot -- they are called critical. Re-initialising or re-randomising robust layers provides functionally indistinguishable reparametrisations for which RMT-based metrics are arbitrary. Moreover, we show that relationships between metrics offered by RMT are spuriously related to generalisation. We conclude by showing that if many pre-trained models have data-free metrics in a `good' range, it is, in part, dependent on model initialisation.

Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology. Methods that rely on epigenetic marks such as DNA methylation typically operate on aggregated methylation estimates, discarding the pattern-level information carried by individual DNA reads. Existing read-level approaches that exploit this information are scarce, and all remain restricted to few-class settings; scaling them further is an open problem because, at scale, non-discriminative reads dominate and hard labels conflict with the many-to-many mapping between methylation patterns and cell types, preventing classifier convergence. To overcome this, we propose data-driven soft labels that estimate the conditional cell-type distribution for each read, and integrate this scheme into *Syto*, a new modular framework for read-level classification-based deconvolution. On a whole-body atlas of 39 human cell types, *Syto* reduces MSE by $2.56\times$ over SoTA, with gains transferring to an out-of-distribution dataset spanning 16 tissues. *Syto* lays the foundation for modeling increasingly large cell-type panels, with improved applications in biology and healthcare. The proposed soft-labeling scheme is further translatable to any setting with a many-to-many signal-to-label mapping.

Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and feedback loops are present. Existing approaches typically assume acyclicity or rely on global causal structure learning, limiting applicability and computational efficiency. In this work, we study a local, data-driven method for covariate selection based on conditional independence information. While this method is known to be sound and complete in acyclic causal models, its validity in the presence of cycles has remained unclear. Our main contribution is to show that these guarantees extend to cyclic causal models. In particular, our result relies on the invariance of conditional independence assertions under $\sigma$-acyclification. These findings establish a unified, cycle-agnostic perspective on covariate selection and causal effect estimation, showing that the method applies across cyclic and acyclic settings without modification. Empirically, we validate this on extensive synthetic data, showing reliable performance in cyclic causal models.

Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus. As training compute grows faster than the supply of natural language data, pretraining is likely to enter a data-constrained, compute-rich regime where models train for multiple epochs over a finite dataset. We study data-constrained pretraining along two axes, regularization and scaling. For regularization, we study masked-input regularization (MIR), an auxiliary next-token prediction loss on randomly masked inputs. MIR tests whether the random masking central to diffusion language models can benefit autoregressive pretraining without architectural changes or inference overhead. Across 72M to 1.4B parameter models, we find that MIR added on top of strong weight decay improves validation loss over autoregressive strong-weight-decay-only models, with downstream gains at 1.4B. For scaling, we propose SoftQ, a scaling law that couples model size and data size to capture their interaction under repeated data. Classical alternatives such as the Chinchilla law use an additive form that decouples these terms, making them misspecified in the data-constrained regime. We find that SoftQ fits data-constrained experiments substantially better than these alternatives, and estimates MIR's gains as equivalent to roughly 1.3 times as much unique training data.

Recent work has identified a set of emergent symbolic mechanisms that support abstract reasoning in large language models, but it remains unclear what factors drive the emergence of these mechanisms. To address this question, we trained neural networks from scratch on abstract sequence tasks, varying both architectural and data distributional factors, and investigated the effects of these factors on the emergence of symbolic mechanisms. Using a combination of representational, attentional, and casual mediation analyses, we first confirmed that transformer language models trained from scratch on our task developed symbolic mechanisms, partially capturing the set of mechanisms learned by large-scale pretrained models. We then investigated the effect of data diversity, operationalized as vocabulary size, on the emergence of these mechanisms, finding that mechanistic and behavioral signatures of emergent symbol processing scaled with data diversity, including downstream measures of systematic (i.e., out-of-distribution) generalization. Surprisingly, we found that the emergence of symbolic mechanisms was not driven by architectural inductive biases, as the same mechanistic and behavioral signatures emerged in modified transformer architectures and even multilayer perceptrons. These results suggest that data diversity, rather than architectural inductive biases, is the primary driver of emergent symbolic computation in neural networks.

Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-$k$ relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grained softmax attention on the selected tokens. However, the top-$k$ operation assumes the number of relevant tokens for any query is fixed and it precludes the gradient flow between the sparse and dense stages. In this work, we propose DashAttention (Differentiable and Adaptive Sparse Hierarchical Attention), which leverages the adaptively sparse $\alpha$-entmax transformation to select a variable number of blocks according to the current query in the first stage. This in turn provides a prior for the second-stage softmax attention, keeping the entire hierarchy fully differentiable. Contrary to other hierarchical attention methods, we show that DashAttention is non-dispersive, translating to better long-context modeling ability. Experiments with large language models (LLMs) show that DashAttention achieves comparable accuracy as full attention with 75% sparsity and a better Pareto frontier than NSA and InfLLMv2, especially in high-sparsity regimes. We also provide an efficient, GPU-aware implementation of DashAttention in Triton, which achieves a speedup of up to $3.3\times$ over FlashAttention-3 at inference time. Overall, DashAttention offers a cost-effective strategy to model long contexts.

Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing methods mainly focus on algorithmic optimization, with less emphasis on data-level improvements to enhance learning ability and generalization performance. This paper, from a data perspective, reveals three key bottlenecks that limit Offline MTRL performance:(i) ineffective utilization of prompts length under diverse task complexities, and (ii) semantic irrelevance of randomly sampled prompt segments, (iii) misleading supervision induced by fragmented and discontinuous trajectories. To address these challenges, we propose DaCe-DT, a robust offline MTRL framework designed to be insensitive to heterogeneous task complexities and data quality, featuring length-gated prompt masking (LGPM), retrieval-augmented prompt construction (RAPC), and value-adaptive return calibration (VARC). Together, these mechanisms enable DaCe-DT to deliver data-centric prompt adaptation and trajectory refinement, resulting in robust multi-task generalization and stable policy learning amid heterogeneous offline data and tasks. Experimental results on Meta-World show that DaCe-DT consistently outperforms state-of-the-art methods, achieving an average improvement of 11.73\% on optimal datasets and an improvement of 13.34\% on suboptimal datasets, demonstrating its effectiveness in learning stably from imperfect data and improving overall multi-task performance.

Emerging microscopic technologies such as cryo-electron tomography (cryo-ET) provide direct 3D visualization of macromolecules within the cell, enabling analysis of their in situ morphology. This morphology can be regarded as an SE(3)-invariant, denoised volumetric representation of subvolumes extracted from tomograms, termed as subtomograms. Morphology identification from a set of subtomograms is formulated as an inverse problem of estimating a set of template morphologies and per-subtomogram SE(3) transformations with respect to any one of the templates. The existing expectation-maximization-based solution to this end often struggles with high structural heterogeneity and requires manual selection of a large number of hyperparameters. Addressing this issue, we present a novel deep unsupervised learning framework called DUIL. Given a set of subtomograms, DUIL first models their SE(3)-invariant morphological code using a siamese-like neural network with a multi-choice learning module. The learned morphological codes are clustered and used to generate a set of template morphologies through a generator network. The generated templates are used as references to estimate SE(3) transformations for each subtomograms through latent optimization. The subtomograms with identical template morphologies are then aligned and averaged to iteratively refine the templates. Experiments on simulated and real cryo-ET datasets demonstrate clear improvements over prior methods, including the discovery of previously unidentified macromolecular morphologies.

Diffusion and flow-matching based text-to-speech (TTS) models excel in naturalness but often lack explicit emotion control, as emotional signals remain entangled with speaker identity. We discover that emotion embedding emerges as a linearly decodable direction of frozen hidden states, nearly orthogonal to the direction embedding speaker identity. This inspires a plug-and-play framework DUET for emotion control over pretrained diffusion and flow-matching based TTS models. During generation, DUET unifies dual-space control to achieve fine-grained emotion intervention in a single per-step update: hidden space steering shifts generation along the target emotion direction, while mel-space guidance refines spectral details through gradients backpropagated from a differentiable vocoder. We validate DUET on five architecturally diverse pretrained TTS backbones across three datasets, where it outperforms 10 supervised state-of-the-art emotional TTS baselines across paradigms and achieves the highest human-rated emotion appropriateness. To further showcase its qualitative behavior, we deploy DUET on an Ameca humanoid robot, where it produces richly expressive emotional speech on the humanoid, demonstrating the strong potential for plug-and-play affective interaction for embodied agents. The demonstration is available at: https://anonymous.4open.science/w/duet-demo-fresh-D8E0

Recent advances in large language models (LLMs) have leveraged reinforcement learning with verifiable rewards (RLVR) to enhance reasoning capabilities. However, RLVR typically relies on massive training data and extensive rollouts, posing substantial challenges to computational resources. Existing data selection approaches address this challenge in isolation: offline methods perform static, one-time dataset pruning that cannot adapt to the model’s evolving learning needs throughout training, while online methods conduct per-iteration filtering at the expense of significant additional computation. In this paper, we propose DUDS, a Dual-stage Data Selection framework for RLVR, which organically integrates the strengths of both paradigms to improve training efficiency while maintaining competitive performance. Specifically, the offline stage curates a candidate pool from the full dataset by jointly considering quality score and sample diversity via Determinantal Point Processes, providing an informative warm start for subsequent training. The online stage employs Bayesian posterior updates to estimate sample pass rates without introducing additional rollouts, and combines them with a freshness metric for data sampling, then applies asymmetric filtering to phase out mastered or intractable problems. Extensive experiments across four reasoning benchmarks demonstrate that DUDS consistently outperforms existing methods in both offline and online data-selection scenarios, achieving competitive performance with significantly less data and computation. Code is available at here.

Dynamic scene representation and rendering in the visible spectrum has been extensively studied. Compared to visible-light imaging, thermal infrared sensing offers all-weather observation and strong penetration capability, enabling perception in low-visibility environments. However, dynamic reconstruction in thermal infrared scenes is challenged due to atmospheric transport effects and interference from low-emissivity background regions, which often leads to motion blur and details loss in rendered results. To address these issues, we propose DTGS, a physics-driven dynamic 3D scene reconstruction approach. DTGS factorizes dynamic thermal scenes into radiometric and geometric components, modeling radiometric changes via rendering that integrates atmospheric radiative transfer, while capturing geometric deformations through Adaptive Temporal Gaussian basis. In addition, we design a thermal-radiance-weighted structural similarity loss to suppress gradient interference from low-radiance noisy regions. Furthermore, to demonstrate the effectiveness of our method, a large-scale dataset for this field named Dynamic\_LTR is created. Experimental results demonstrate that our method achieves a 2.7 dB improvement in PSNR, a twofold speedup in rendering, and a threefold reduction in training time. The code and datasets will be available after acceptance.

Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates. The key difficulty is that neural architectures are operation-labeled DAGs: edge direction defines computational flow, but existing predictors often treat direction as a local structural cue. We propose DTA-GT, a Direction- and Topology-Aware Graph Transformer based on encoder-wide directional consistency, which preserves directed computational semantics across node initialization, global spectral encoding, pairwise interaction, and feature update. DTA-GT realizes this principle with direction- and topology-aware node initialization, Magnetic Laplacian spectral encoding, direction-dual structural attention, and a Direction-Sensitive MoE-FFN. Across accuracy and latency prediction on NAS-Bench-101 and NAS-Bench-201, DTA-GT consistently outperforms representative sequence-, GNN-, Transformer-, and hybrid predictors under limited-budget settings. Ablations and Magnetic spectral controls show that the improvements come from coordinated direction-aware stages and edge-orientation-sensitive spectral information, while mechanistic diagnostics confirm that the modules exhibit the intended directional behavior. These results support encoder-wide directional consistency as an effective design principle for architecture DAG representation.

Preferential Bayesian Optimization (PBO) aims to find a decision-maker’s most preferred solution in as few pairwise comparisons as possible. Existing approaches rely on Gaussian Process (GP) surrogates, which provide strong performance but limited interpretability. This limits real-world usability in high-stakes domains, such as healthcare, where interpretability and trust are essential. We propose DT-PBO, a novel tree-based surrogate model for PBO that is inherently interpretable while capturing preference uncertainty. Specifically, we introduce a novel splitting heuristic that constructs interpretable shallow decision trees directly from pairwise comparison data, and use Laplace approximation to obtain probabilistic estimates within each leaf. This enables efficient preference modeling without sacrificing interpretability. Across eight benchmark functions, our method achieves competitive convergence to GP-based PBO, particularly on functions with rugged optimization landscapes. Additional experiments show robustness against noise and a fast computational running time. Experiments on four real-world datasets further demonstrate that our model provides interpretable insights into decision-maker preferences that remain opaque under GP-based approaches.

Traffic flow prediction is a fundamental task in intelligent transportation systems. Existing methods primarily rely on temporal dependency modeling and thus face two critical limitations: struggling to effectively capture diverse periodicity; overlooking the relationship between spectral structures in the traffic flow and spatiotemporal traffic behaviors. To address these limitations, we propose DSSNet (Deep Spectral Structure Profiling Network), an adaptive spectral profiling framework. DSSNet follows the paradigm of "spectral structure profiling" and then "dual-domain modeling". Initially, a Spectral Structure Learning module captures the dominant frequency bands of the spectrum to enhance regional representations while simultaneously serving as discriminative spatiotemporal identity between regions. Based on these identities, we construct a global Resonance Graph to uncover cross-region dependencies and introduce a Dual-Domain Knowledge Integration module to aggregate structural knowledge across both time and frequency domains. Extensive experiments on multiple benchmark datasets demonstrate that DSSNet achieves state-of-the-art performance, while highlighting the potential of frequency domain modeling for understanding spatiotemporal traffic behaviors.

The rapid proliferation of personalization techniques in Stable Diffusion has driven the widespread deployment of adversarial perturbations as a protective measure against unauthorized data mimicry. Existing diffusion-based purification methods primarily rely on the noising and denoising paradigm and have proven somewhat effective in neutralizing these protections. However, they suffer from a severe trade-off between purification efficacy and perceptual fidelity, especially under large perturbation budgets. To mitigate this problem, in this paper, we propose Dynamic Sparse Semantic Anchoring (DSSA), a novel purification framework. By starting the reverse denoising process from pure Gaussian noise, DSSA removes the initial adversarial perturbations and circumvents the dilemma of selecting an optimal noising level. To prevent the loss of original semantics caused by pure noise generation, we propose a dynamic sparse anchoring strategy that directly integrates sparse pixels from the adversarial sample to guide the reconstruction. Additionally, we design a perception-guided early-stopping mechanism to prevent the perturbation resurgence caused by this direct pixel integration. Extensive experiments across diverse datasets and protection schemes demonstrate that our method consistently outperforms existing baselines, achieving state-of-the-art performance in efficacy and fidelity, successfully neutralizing even advanced adaptive protections.

Long-horizon time series forecasting requires a careful balance between predictive accuracy and computational cost. Segmented temporal modeling has therefore become a practical strategy for reducing the burden of long-range prediction. However, real-world time series often contain superposed multi-periodic components, whose spectral structures vary across time, variables, and samples. Such variability limits fixed aggregation or frequency-modeling mechanisms, making them less adaptive to non-stationary periodic patterns and potentially causing informative components to be attenuated over long forecasting horizons. To address this limitation, we propose DSR-TSF, a spectrum-driven dynamic routing framework for long-horizon forecasting. DSR-TSF adaptively selects and combines multiple period-specific branches conditioned on the spectral characteristics of the input sequence, thereby improving the model's ability to capture non-stationary periodic structures. It further learns period-aware dynamic weights to modulate the contributions of multi-periodic patterns while retaining the segmented prediction structure. Without complex resampling or explicit period alignment, DSR-TSF provides a compact mechanism for modeling spectral heterogeneity and achieves competitive performance across seven datasets with diverse dimensionalities and temporal characteristics.

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.