NeurIPS 2026 论文列表

Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a static font remains a labor-intensive process requiring expert typographic design and manual specification of glyph variation data. We introduce NIV (Neural Axis Variations), a method that automatically converts a static font into a fully functional variable font. Given glyph outlines and a set of desired design axes, NIV predicts per-point displacements. The model operates directly on vector glyph geometry and employs a novel Property Embedding mechanism that captures interactions between multiple axes, enabling consistent multi-axis variation within a unified framework. We train NIV on a newly constructed dataset derived from variable Google Fonts, comprising over one million variation tuples. The resulting model generalizes across unseen code points, unseen font styles, high-complexity CJK glyphs, and even out-of-distribution handwriting inputs. The generated outputs are standard variable font files supporting continuous interpolation via existing rendering engines. To facilitate research, we release the dataset, the complete training and inference implementation, and trained models. Beyond typography, our approach demonstrates how structured geometric objects with continuous parametric variation can be synthesized using neural deformations.

We introduce NAGO, a noise-aware generative operator for efficient sampling from invariant measures of stochastic partial differential equations (SPDEs) in function space. Our central idea is to treat the noise measure as a design variable rather than a fixed default. While existing generative approaches mainly shape the sampling process through interpolation schedules or guidance heuristics, NAGO directly controls the marginal velocity field in functional flow matching by choosing a noise measure with suitable regularity. We establish conditions under which the resulting noise-induced flows are well defined in infinite-dimensional settings, and show how noise regularity affects the spectral geometry and stiffness of the marginal velocity field. This analysis leads to a principled design rule: match the regularity of the noise measure to that of the target invariant measure. The resulting method enables stable training and fast sampling without relying on downstream correction mechanisms. Experiments on three representative SPDEs with multiscale invariant measures show that NAGO consistently improves the accuracy--efficiency trade-off over standard flow matching baselines, while achieving up to two orders of magnitude speedup compared with classical numerical solvers.

We address the problem of accurate, training-free guidance for conditional generation in trained diffusion models. Existing methods typically rely on point-estimates to approximate the posterior score, often resulting in biased approximations that fail to capture multimodality inherent to the reverse process of diffusion models. We propose a sequential Monte Carlo (SMC) framework that integrates over the full denoising distribution to construct an unbiased Monte Carlo estimator of $p_\theta(y|x_t)$, avoiding the bias of existing training-free guidance methods. To ensure computational tractability, we incorporate variance-reduction schemes based on MultiLevel Monte Carlo (MLMC). We evaluate on three image generation tasks. On CIFAR-10 and ImageNet classifier guidance, our approach improves accuracy by 18 and 38 points respectively over the strongest training-free baseline. On CelebA-HQ joint-attribute guidance, three existing methods (DPS, LGD, TFG) used within our framework all outperform the closest SMC-based comparator (TDS) by 25-37 points, and improve over the strongest baseline by 2-14 points.

Consistency models achieve fast image synthesis by chaining a small number of neural evaluations along a diffusion trajectory, yet the distribution error of $N$-step sampling for $N \geq 3$ has lacked a rigorous characterisation. We establish a tight Wasserstein-$p$ error bound for arbitrary $N$: the total error equals a Lipschitz-weighted sum of per-step local consistency errors, $\sum_{k=1}^{N} L_x^{N-k} \delta_k$, making the propagation mechanism explicit. From this bound we derive a closed-form optimal step count $N^{\star}$ whose structure bifurcates on whether the spatial Lipschitz constant $L_x$ exceeds one: for $L_x > 1$, exponential error growth yields $N^{\star} \approx (\ln L_x)^{-1} \log(\beta\gamma(L_x-1)/(c \ln L_x))$; for $L_x < 1$, geometric-series saturation gives $N^{\star} \approx (\beta\gamma(1-L_x)/(c \, |\ln L_x|))^{1/(\gamma+1)}$. The two-regime structure accounts for ECT's best FID of $2.73$ at $N=2$: with $L_x \approx 0.85 < 1$ (Case 2), the formula gives $N^{\star} \approx 1.7$. Experiments on CIFAR-10 reproduce the predicted U-shaped FID curve with $N^{\star} = 8$ on a capacity-constrained model in which error accumulation is easier to see.

The standard mechanistic interpretation of transformer factual recall attributes storage to deep MLP modules and routing to attention. We study how module roles change when the fact distribution carries subject, relation, or answer-collision structure rather than independent facts. Which module performs the recall depends on the correlation axis and on the model's capacity load. Probes and matched-counterfactual patching disagree systematically on every motif: probes credit the deepest MLP while patching credits the first attention module. We show that probes and patching measure structurally different quantities---cumulative linear decodability versus irreplaceable causal contribution---which under correlated fact distributions need not coincide. A parameter-counting argument singles out subject-axis collision as the unique compression that the attention-routing pathway can exploit cheaply, predicting an order-of-magnitude crossover for that pathway class that we observe empirically: the layer-2 probe gap collapses sharply for subject collisions, and only subject-axis specialization survives the capacity-tight regime. Edge-level path patching uncovers a three-step chain (Attn -> MLP1 -> MLP2) whose dominance tracks the predicted threshold, with a causal-scrubbing test corroborating that the chain is causally relevant on every motif and sharpest in the subject regime. A dense joint sweep shows that subject and answer compressions compete rather than amplify, with the dominant axis set by the stronger compression. On natural CounterFact strata across four frozen pretrained LMs (Pythia-1.4B/2.8B, LLaMA-3.2-1B-Inst/3.1-8B), edge-level path patching reproduces the synthetic ranking: the answer-collision stratum is the most localized on every model. A two-hop extension shows the binding-locality structure carries to compositional facts.

Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation of long-term information, while trajectory optimization methods plan explicitly but must solve a high-dimensional optimization problem at every decision step. We propose Generative Trajectory Planning (GTP), a model-based framework that addresses this tension by learning a generative model as a proposal distribution over action sequences. Instead of optimizing sequences from scratch, GTP samples candidate sequences from a diffusion prior and refines them using learned dynamics, reward, and value models, enabling efficient trajectory-level reasoning. Across continuous control and long-horizon manipulation tasks, GTP matches or exceeds state-of-the-art model-free, diffusion-policy, and model-based baselines, and remains stable in regimes where strong baselines degrade. Our results suggest that generative models are best understood not as policies, but as structured proposal distributions for planning, offering a new perspective on how to integrate generative modeling with model-based decision-making.

Recursive training on synthetic data can drive generative models toward collapse, yet most explanations treat collapse as a statistical phenomenon: tail loss, variance shrinkage, or distributional drift. We develop a singular-geometric view of collapse. In a labeled Gaussian mixture abstraction of recursive self-training, we show that mode extinction induces a monotone trajectory in the real log-canonical threshold: as recursive resampling eliminates active modes, the overcomplete mixture moves through increasingly singular strata, with strictly decreasing singular complexity and growing Fisher-information nullity. We further show that this loss of identifiability begins before exact extinction: as a component's weight becomes small, Fisher curvature in its associated parameter directions shrinks proportionally, so exact singularity is the endpoint of a continuous near-nullity trajectory. Thus collapse corresponds not only to reduced distributional diversity, but also to a progressive loss of locally identifiable parameter directions. This theory separates the singular geometry of collapse from the multinomial/Wright--Fisher absorption dynamics. We also prove that, in the mixture abstraction, injecting enough real data prevents component extinction and keeps the singular-complexity trajectory from descending over a fixed horizon with high probability. We then use the theory to motivate loss-landscape diagnostics for neural recursive training, including Hessian spectra, Gauss--Newton effective rank, decoder-block curvature, feature-rank concentration, and local learning coefficient estimates. Across Gaussian mixtures, VaDE, standard VAEs, and a compact DDPM, we find that synthetic-only recursion exhibits the predicted geometric degeneration, that VaDE geometry diagnostics predict future component loss, and that real-data injection stabilizes both distributional and geometric collapse metrics.

Existing latent graph diffusion models often learn graph representations and generative priors within a homogeneous latent geometry. This assumption is restrictive for graph generation with structural heterogeneity, where hierarchical, cyclic, locally regular, and densely connected patterns may coexist within the target distribution. Under such conditions, forcing all structural patterns into one latent geometry can introduce representation distortion, which may then propagate to generation. We propose CurvDiff, a latent graph generation framework based on mixed-curvature latent diffusion. The main idea is to model structurally different graph patterns in geometry-compatible latent factors rather than representing them in a single-curvature space. Specifically, CurvDiff learns graph embeddings in a product latent geometry consisting of hyperbolic, spherical, and Euclidean components, and maps the resulting representation to a shared tangent-space interface on which latent diffusion is defined. This provides a consistent latent pathway from representation learning to generation. To further preserve topology-relevant structure, CurvDiff uses Ricci-derived structural signals as topology-aware priors. These signals regularize latent representation learning and condition the diffusion prior, helping maintain structural consistency across representation learning and generation. Experiments on synthetic and real-world graph benchmarks show that CurvDiff achieves competitive or superior graph distribution matching compared with strong baselines, with improved fidelity on degree, clustering, and spectral statistics. These results support mixed-curvature latent diffusion as an effective approach to graph generation with structural heterogeneity.

Flow matching has emerged as a powerful alternative to diffusion models for generative modeling, but standard conditional flow matching is fundamentally misaligned with partially observed time series: starting from an uninformed Gaussian source forces the model to transport even already-observed entries from noise to the target, wasting capacity on trivially known regions. We propose MissPath-FM, a partial-observation-aware flow matching framework that redesigns the source prior and probability path for incomplete time series. MissPath-FM employs structured source priors (interpolation priors and Gaussian process posterior priors) to initialize the flow close to the observed signal, paired with a missingness-aware smooth path that allocates transport asymmetrically between observed and missing regions. We evaluate systematically across four datasets, four missingness mechanisms (MCAR, block, MNAR, irregular), two missing ratios, and three sequence lengths ($T \in \{24, 48, 96\}$), totaling 48 settings against eight baselines. MissPath-FM wins 29 of 32 settings at $T{=}48$ and 45 of 48 overall (93.8\%). Our ablations reveal a regime-dependent design hierarchy in which no single component suffices: under random missingness the source prior yields a large initial gain, but under structured block missingness it can fail entirely and the learned flow becomes indispensable; the missingness-aware path contributes a further $12\%$--$26\%$ across all regimes. Only the full pipeline achieves robust performance across all 48 settings. Mechanistically, structured priors shorten observed-region transport by up to $15\times$, concentrating the velocity field on genuinely uncertain entries.

Recent advances in video generative models have enabled flexible conditional generation and editing pipelines, such as text-to-video, image-to-video, and reference-guided synthesis. However, these workflows are inherently one-directional, mapping conditions to videos without providing a mechanism to invert this process. In complex media production settings, the ability to recover controllable textual conditions from a given video is highly desirable, yet remains underexplored. While inverse prompting has been studied for image generation, directly extending these approaches to video is challenging due to the need to capture temporal dynamics beyond static appearance, as well as the substantial computational cost of per-video optimization. In this work, we propose the first practical framework for efficient video inverse prompting. Specifically, we formulate inverse prompting as a meta-learning problem and introduce Meta Video Inverse Prompting (MVIP), which learns a meta-prompt optimized over temporal dimensions to enable fast and effective prompt inference. Our approach amortizes the inversion process, eliminating the need for costly iterative optimization at test time. Experimental results demonstrate that MVIP recovers higher-quality inverse prompts with significantly improved reconstruction fidelity compared to image-based inversion and iterative captioning baselines, while achieving substantial gains in efficiency.

Sparse autoencoders decompose activations into sparse, interpretable features and have become a central tool for mechanistic interpretability. Transcoders extend this approach from representation reconstruction to computation prediction by mapping MLP inputs to MLP outputs, making them attractive for circuit-level analysis. We ask whether a prominent sparse-autoencoder reliability fix, Matryoshka nested-prefix training, transfers to this two-hook setting. In sparse autoencoders, Matryoshka training improves absorption behavior by encouraging broad features to appear in early dictionary prefixes. In transcoders, we find that this protection does not transfer automatically. Across Gemma-2-2B MLP transcoders trained under a matched recipe, Matryoshka models form genuine prefix hierarchies, with reconstruction improving as later groups are added, but they do not inherit the absorption advantage observed for sparse autoencoders. BatchTopK achieves lower full absorption, lower FVU, and higher first-letter F1@1 in the canonical 16k comparison. Bridge controls recover the expected Matryoshka advantage when the cross-space map is removed, showing that the failure is specific to predicting MLP outputs from MLP inputs. We identify the mechanism as hierarchy misalignment. Prefix losses reward covariance with the reconstruction target, so early transcoder groups prioritize target-predictive output geometry rather than broad source-space semantic parents. These results show that hierarchical sparse-dictionary objectives do not preserve interpretability properties by default. For dictionaries that map between activation spaces, the key question is not whether a hierarchy forms, but what geometry that hierarchy is trained to organize.

Pixel-space diffusion models have recently seen a revival in image generation, yet their training remains less efficient than that of their latent-space counterparts. Existing methods reduce this cost by equipping diffusion models with external representations from clean images. In contrast, representation consistency training, a new generative pretraining paradigm, stands out as it eliminates the need of any off-the-shelf semantic encoders. It proposes pre-training the diffusion model to simultaneously capture meaningful visual semantics from clean images while aligning them with data points across various noise levels. Nevertheless, its training depends on contrastive learning with hand-crafted augmentations. This introduces strong semantic prior into the pretraining, limiting model performance when transferred to downstream target distributions that substantially deviate from the pretraining manifold. To address this, we propose Masked Pixel-space Generative Pretraining (MPG), an augmentation-free framework based on masked image modeling. MPG trains the encoder to recover masked image area and aligns those predictions with samples of different noise levels. To measure the performance of pretrained model, we further introduce NC-CKNNA, a metric that quantifies the semantic structure consistency across different noise levels. Under the same architectures and fine-tuning settings, MPG consistently produces better generation performance across seven downstream datasets, while retaining competitive generation quality on ImageNet-256. These results suggest masked generative pretraining as a practical alternative for training pixel diffusion models.

Unpaired image translation is a critical preprocessing strategy for synthesizing missing sequences and harmonizing contrast variability in magnetic resonance (MR) imaging. However, in medical image processing, distribution-level translation is insufficient; it is vitally important that the anatomy is not distorted, corrupted, or lost after translation. Previous methods, like diffusion Schrödinger bridge matching (DSBM) achieve image translation, but do not preserve anatomy. We introduce the pseudometric Schrödinger bridge (PMSB), which trains two independent volume-preserving, isometry-regularized diffeomorphisms to map data from each domain to a latent anatomic pseudometric space. Then, DSBM is used within that space, explicitly minimizing the transport gap while preserving anatomy. Extensive experiments on the multi-contrast OASIS-3 dataset demonstrate that PMSB establishes a new state-of-the-art in structural consistency for cross-contrast synthesis. PMSB outperforms other leading methods and drastically reduces anatomical hallucinations, notably yielding a mean PSNR of 26.70 dB, a mean SSIM of 0.86, and a mean LNCC of 0.24.

Large language model (LLM) agents have shown strong potential in tackling complex, multi-step tasks, yet scaling them to long-horizon settings remains fundamentally constrained by two problems: context window saturation and uniform allocation of inference-time compute across steps of vastly different cognitive demands. We introduce **MOSAIC** (**M**ulti-Scale **O**rchestrated **S**cale-**A**daptive **I**nference **C**ontrol), a hierarchical agent framework that decomposes long-horizon decision making into three temporal scales: strategic, tactical, and operational, each governed by distinct context scopes and reasoning budgets. Central to MOSAIC is a *Scale Scheduler*, a lightweight policy trained via reinforcement learning that dynamically determines (i) which abstraction level should be invoked at each time step, and (ii) how many reasoning tokens to allocate, based on the estimated decision complexity. We further propose *inter-scale context distillation*, a bidirectional information compression mechanism that maintains coherent state representations across scales without redundant token consumption. Theoretically, we show that under a fixed total inference budget, MOSAIC's adaptive allocation policy achieves a strictly lower regret bound than any fixed-rate policy in a hierarchical semi-MDP formulation. Empirically, MOSAIC achieves state-of-the-art or competitive results on four diverse long-horizon benchmarks: SWE-Bench Verified, BrowseComp, WebArena, and GAIA (Level 2\&3), while reducing total inference tokens by 38--57\% relative to flat ReAct baselines. Ablation studies confirm that each component of multi-scale decomposition, adaptive scheduling, and context distillation contributes meaningfully to both performance and efficiency.

Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, multi-step navigation, and future state changes. Yet many agents externalize this computation as long textual thoughts, making interaction slower, supervision more costly, and deployment less efficient. We introduce MIRAGE (Mobile agents with Implicit Reasoning And Generative world modEls), a framework that learns continuous latent reasoning representations from visible textual thoughts. MIRAGE introduces an efficient latent-space learning procedure that transfers explicit reasoning into compact hidden states, allowing the agent to reason internally without decoding long rationales. It further brings a world-model perspective into mobile-agent training: the model’s latent reasoning vectors are aligned with future screenshots, encouraging the agent to predict upcoming interface states in latent space before executing an action. This makes the hidden computation not only a compressed thought trace, but also a forward-looking representation of how the environment may change. At inference time, MIRAGE reasons in continuous latent space, reducing token generation while improving execution efficiency. On AndroidWorld, MIRAGE matches explicit-CoT SFT in the 4B ablation under a 3–5× lower decoded-token budget, and improves a comparable instruction-tuned baseline by 10.2 points; on AndroidControl, it improves action grounding with over 75% fewer generated tokens.

We propose the Monge Inception Distance (MIND), a metric for evaluating generative models that addresses key limitations of the widely adopted Fréchet Inception Distance (FID). The MIND metric leverages the sliced Wasserstein distance to compare distributions by averaging one-dimensional optimal transport distances, efficiently computed via sorting. This approach circumvents the estimation of high-dimensional means and covariance matrices, which underlie FID's poor sample complexity and vulnerability to adversarial attacks. We empirically demonstrate three primary advantages: (i) it is more sample-efficient by one order of magnitude, (ii) it is faster to compute by two orders of magnitude, (iii) it is more robust to adversarial attacks such as moment-matching. We show that MIND with 5k samples can replace the evaluation performance of FID with 50k samples, providing high correlation with this standard benchmark and superior discriminative performance. We further demonstrate that even smaller sample sizes (e.g., 1k or 2k) remain highly informative for rapid model iteration.

This position paper argues that evaluation in generative molecular dynamics (GenMD) exhibits a recurring pattern of comparison against insufficient or misleading benchmarks, which risks over-claiming advantage over classical baselines. The pattern reflects a structural misalignment between common evaluation practices and the scientific tasks the field sets for itself. The reason is structural: the natural ground truth, exhaustive molecular dynamics on the systems of interest, is computationally costly, the very gap that motivates the field. With ground truth inaccessible, evaluation drifts toward convenient proxies, and the resulting norms entrench easily. We identify misaligned evaluation patterns that persist as a community-level equilibrium rather than failures of individual papers, and propose concrete observables, reporting standards, and benchmark designs, both computational and experimental, that better reflect what GenMD aspires to deliver. Our aim is constructive: to give the field shared language and tools for evaluation that match its scientific ambitions before today's conventions calcify into norms.

We revisit regularized regret minimization under full-information and bandit feedback, where a learner optimizes an objective of the form $\langle r, \pi \rangle - \eta^{-1} \psi(\pi)$ for adversarially chosen rewards $r$, policies $\pi \in \Delta_K$, and a known strongly-convex regularizer $\psi(\cdot)$. We establish the minimax regularized regret for a broad class of regularizers, specifically those that are relatively strongly-convex and smooth with exponent $p \geq 1$, Legendre, and coordinate-wise separable. For large $\eta$, the problem approaches unregularized online learning, recovering the standard $\Theta(\sqrt{T})$ scaling. For small $\eta$, the strong convexity of the regularizer dominates, and we identify the regimes that attain $\Theta(\eta \log T)$ minimax regret under both feedback models. Notably, while prior logarithmic regret lower bounds are largely restricted to specific curved loss functions (e.g., squared loss), our lower bounds hold for ***any*** such regularizer against purely linear adversarial rewards. A key technical contribution is our proof strategy, which leverages a Bregman geometric perspective of regularized regret over the quotient space $\mathbb{R}^K / \mathrm{span}(\\{\mathbf{1}\\})$ to resolve the translation invariance of the inverse mirror map. By combining this dual geometric formulation with the ***van Trees inequality***, we reduce the adversarial online learning to Bayesian estimation, offering a unified framework for establishing logarithmic regret lower bounds.

Promptable segmentation foundation models such as SAM and its variants achieve strong performance with informative prompts, yet exhibit a striking prompt asymmetry where dense prompts like bounding boxes yield accurate segmentation, whereas sparse point prompts substantially degrade performance. This performance gap is widely observed, yet its internal decoder mechanism remains underexplored. In this work, we revisit the failure of point prompts from a causal perspective. By adapting activation patching to SAM-style decoders with a token-aligned intervention scheme, we localize the earliest recoverable divergence to a single early operation, i.e., the first decoder image-to-token cross-attention update, where prompt-conditioned information first enters the image stream. A sparse autoencoder further reveals that this mediation is concentrated in approximately 50 feature directions out of 4,096, and reverse corruption confirms that the identified layer is the entry point of a propagated image-stream route rather than a locally sufficient state. Guided by this localization, we propose the Causal Bottleneck Adapter (CBA), a lightweight residual module placed at the identified entry layer with the base model frozen. Evaluated on 97,996 medical image-target pairs across CT, MRI, ultrasound, and endoscopy, and validated across various SAM-family models, CBA closes 0.891 of the box-to-point gap with fewer than 9K trainable parameters, outperforming prior repair baselines up to 50× larger.