NeurIPS 2026 论文列表

Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utility depends on generalizing beyond the training task to unseen samples from the same domain. This suggests that a discriminative model may retain an implicit trace of the data manifold beyond its task labels. We ask whether this trace can be exposed as a generative signal without task-specific guidance. We introduce the Discriminative Score Function (DSF), which extracts an image-space generative field from the loss-gradient geometry of a pretrained discriminative model. DSF provides a training-free update rule that moves images toward model-consistent visual regions without retraining or task-specific targets. We apply DSF to diverse pretrained discriminative models, including ResNet classifiers, DETR detectors, and DINOv2 ViT encoders. DSF generates unconditional images from noise and supports conditional guidance, editing, inpainting, and explanation through the same field. DSF-generated samples also provide effective zero-shot quantization calibration when real images or labels are unavailable, preserving feature geometry better than data-free calibration baselines. These results show that discriminative training leaves a generative functional signal for synthesis and calibration.

Continuous diffusion is a natural framework for non-autoregressive generation but has consistently underperformed masked discrete diffusion (MDM) on discrete sequence generation. We argue this is not a limitation of continuity itself, but of how previous models parameterize the denoiser as a family of timestep-indexed regimes. We introduce \emph{Discrete Stochastic Localization} (DSL), a continuous-state framework with unit-sphere token embeddings whose Bayes-optimal denoiser is invariant to the nominal signal-to-noise ratio (SNR). One trained network then supports an entire family of per-token SNR paths, with masked diffusion as one special case. Fine-tuning a pretrained MDLM checkpoint with DSL substantially improves distributional faithfulness (MAUVE) on OpenWebText across all step budgets from $T{=}128$ to $T{=}1024$, and the same checkpoint supports random-order autoregressive sampling and a hybrid continuous-then-discrete sampler at as few as $T{=}48$ total steps---without distillation or retraining. On Text8, DSL gives the first continuous-state NLL estimate approaching the MDM range. Code is anonymously available at \url{https://anonymous.4open.science/r/DSL_anonymous-94B5/}.

EHR foundation models create new opportunities for scalable clinical prediction across health systems, yet their deployment remains constrained by a basic tokenization bottleneck. The same clinical concept can appear as different ontology codes, local identifiers, synonyms, or free-text descriptions, while standard EHR tokenizers treat these variants as unrelated symbols. We introduce MEDIATOR, a discrete ontology-agnostic medical concept tokenizer that turns EHR harmonization into an open-form tokenization problem. Instead of relying on manual mappings, a fixed ontology vocabulary, or continuous event embeddings, MEDIATOR maps any clinical concept expressed as text into a shared vector-quantized codebook. MEDIATOR is trained with synonym supervision from paired concept descriptions, and further guided by enriched concept descriptions, empirical code co-occurrence, and curated relation graphs so that equivalent expressions collapse while non-equivalent concepts remain organized in a medically meaningful discrete space. The resulting vocabulary is compact, inspectable, reusable by standard EHR Transformers, and open to previously unseen code descriptions or text strings. Used as the token interface of an EHR Transformer, MEDIATOR improves mean AUPRC on 20 MIMIC-III tasks by 4.0% and improves zero-shot transfer to 14 eICU tasks by 8.8%, while supporting transferable attribution across hospitals.

Flow Matching has recently emerged as a popular class of generative models for simulating a target distribution $\mu_1$ from samples drawn from a source distribution $\mu_0$. This framework relies on a fixed coupling between $\mu_0$ and $\mu_1$, and on a deterministic or stochastic bridge to define an interpolating process between the two distributions. The time marginals of this process can then be approximately sampled by estimating the transition rates, or more generally the generator, of its Markovian projection. This framework has recently been extended to the case of discrete source and target distributions, under the name *Discrete Flow Matching* (DFM). However, theoretical guarantees for such models remain scarce. In this paper, we study two DFM models on $\mathbb{Z}_m^d = \\{0,\ldots,m-1\\}^d$, sampled through time discretization, and derive non-asymptotic associated bounds for both of them. In contrast to previous work, we establish non-asymptotic bounds in Kullback--Leibler divergence for the early-stopped version of the target distribution. We also derive explicit convergence guarantees in total variation distance with respect to the true target distribution. Importantly, these bounds rely only on an approximation error assumption, relaxing standard score assumptions used in earlier works, while also yielding improved dependence on the vocabulary size $m$ and the dimension $d$.

Discrete diffusion is a promising framework for categorical generative modeling, but current evaluation protocols often fail to reveal whether models have actually learned the target distribution. We introduce the Discrete Diffusion Playground, a diagnostic benchmark that converts known 2D continuous distributions into binary token sequences through an exact discretized bijection. This creates a rare setting in which models are trained on discrete sequences, but generated samples can be decoded back into 2D and compared directly against a known ground-truth distribution. The benchmark spans smooth Gaussian mixtures and tilted checkerboards with sharp boundaries, disconnected support, and coordinate-dependent structure, allowing it to expose failures that are invisible in standard proxy metrics. It supports exact grid divergences, Wasserstein distances, density visualizations, and nonparametric two-sample tests, providing both quantitative evaluation and interpretable failure diagnosis. Using this testbed, we show that masked diffusion models can learn meaningful adaptive reveal orders but remain less stable than autoregressive baselines on smooth targets and largely fail on tilted checkerboards, where autoregressive models preserve the target geometry. Control experiments with reversed and interleaved token orderings show that autoregressive performance is not explained by favorable bit order alone.The Playground provides a simple but stringent benchmark for stress-testing discrete generative models and for developing evaluation metrics that detect real distributional mismatch rather than proxy performance. Our code is available at https://anonymous.4open.science/r/DiscreteDiffusionPlayground-0CCA/

Discrete diffusion models are often said to outperform autoregressive models on planning tasks, but the specific mechanisms enabling this are not yet well understood. To explain why, we abstract the complexity of studying ``planning'' into graph traversal problems, which offer a minimal setup for evaluating language models' planning and lookahead capabilities. We find that non-autoregressive language models are able to leverage an inherent directional asymmetry in lookahead planning tasks to outperform the training efficiency of their autoregressive counterparts. Through a mechanistic study of the training and inference dynamics of autoregressive and non-autoregressive models, we discover that while forward traversal through junctions in the graph requires complex sequential planning, the reverse path remains deterministic. Consequently, non-autoregressive models naturally develop a fundamentally different solution strategy. Effectively, they learn a reverse-decoding pattern which reduces an $\ell^{th}$-order lookahead problem to a sequence of simple $1^{st}$-order transitions. Latent-space analysis further confirms these divergent strategies by demonstrating the distinct internal representations formed during generation. Ultimately, these findings highlight the advantages of non-autoregressive modeling while clarifying the inherent planning limitations induced by autoregressive architectures.

While all-in-one image restoration models excel in controlled, closed-set environments, they face critical limitations when deployed in open-world settings where paired supervision is unavailable and degradations are unknown and often intricately mixed. To address this challenge, we propose a continual restoration framework that integrates degradation discovery with adaptive restoration. Building on a model pre-trained on known degradation categories, our method discovers unseen degradations in feature space and continually expands the degradation space. We then introduce a discovery- and instance-conditioned descriptor that jointly captures discovered degradation priors and image-specific characteristics, enabling robust restoration under ambiguous and intricately mixed degradations. To further support adaptation under unpaired supervision, we adopt a mean-teacher-based semi-supervised framework equipped with a reliable bank for pseudo-target refinement. Specifically, we propose a discovery-adaptive score that assesses pseudo-target reliability using feature-space distances to degradation categories, enabling pseudo-target selection to remain adaptive to newly discovered degradations rather than relying solely on conventional perceptual quality-based scoring methods. Experiments on both controlled open-world protocols and real-world adverse weather datasets demonstrate that our approach consistently outperforms existing methods across multiple image quality metrics while improving robustness to degradation composition shifts.

Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and deterministic prediction from a prescribed initial condition. We introduce Structured Phase Space GAN (SPS-GAN), a conditional generative model that learns structured dynamics from both Cartesian trajectories and video. SPS-GAN combines a port-Hamiltonian backbone with adversarial conditional generation, allowing conservative, dissipative, and forced systems to be represented in a single architecture. To infer latent mechanical structure from raw observations, we use a cyclic-coordinate loss that recovers both the system degrees of freedom and canonical coordinates without access to the true phase space. Experiments on simulated and real-world systems show that SPS-GAN improves trajectory accuracy over supervised dynamics baselines and video consistency over generative baselines, while consistently recovering the correct latent mechanical coordinates across observation modalities and dynamical regimes.

Constructing mechanistic models of neural circuits is a fundamental goal of neuroscience, yet verifying such models is limited by the lack of ground truth. To rigorously test model discovery, we establish an in silico testbed using neuromechanical simulations of a larval zebrafish as a transparent ground truth. We find that LLM-based tree search autonomously discovers predictive models that significantly outperform established forecasting baselines. However, in-distribution accuracy is a poor proxy for faithful system identification, as models exploit statistical shortcuts. Structural priors prove essential for enabling robust out-of-distribution generalization and recovery of interpretable mechanistic models. Our insights provide guidance for modeling real-world neural recordings and offer a broader template for AI-driven scientific discovery.

Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of established science. We introduce _DiscoverPhysics_, an interactive benchmark that asks a LLM agent to discover the laws of motion of a simulated world whose physics deliberately deviates from our own. We construct eleven worlds governed by, among others, screened and fractional-power gravity, multi-species couplings, hidden dark-matter-like particles, non-coordinate-free physics, and time-varying interactions. Each world is generated on demand by an N-body simulator, for which the agent proposes several rounds of experiments, observes raw trajectory data, and ultimately submits both a natural-language explanation of the world's physics and a Python implementation of the inferred law. Because solving a world requires the agent to design informative experiments and revise its hypotheses, the benchmark probes long-horizon reasoning over an experimental history. We evaluate submissions along two complementary axes: trajectory MSE on held-out particles and an LLM-judged explanation score following an expert-written rubric assessing conceptual understanding of each world. Across ten frontier models, we find that the strongest agents pass only about half of the worlds and consistently fail on those where latent structure must be uncovered. Open-source models lag substantially behind commercial models, both in their ability to design informative experiments and in extracting conclusions from the data. We further find that good predictive accuracy does not guarantee high explanation quality and that conceptual understanding depends on hypothesis refinement through well-chosen experiments.

While Gaussian Splatting-based Feature Fields (GSFFs) have shown promise for visual localization, this paper highlights that photometrically optimized GSFFs are inherently ill-suited for 2D-3D matching. The volumetric extent of each Gaussian induces many-to-one pixel-to-point mappings that destabilize PnP-based pose estimation, while photometric optimization gives rise to superfluous Gaussians devoid of multi-view consistency. To address these issues, we propose SplitGS-Loc, a localization-specialized GSFFs construction framework that disambiguates 2D-3D correspondences by exploiting Gaussian attributes. Our key design, Mixture-of-Gaussians-based splitting, decomposes each Gaussian into smaller Gaussians, replacing ambiguous many-to-one with precise one-to-one correspondences. In parallel, we exploit composition weights from GS rasterization to select Gaussians that significantly and consistently contribute across multiple views and aggregate discriminative features through strong pixel-Gaussian associations, enforcing multi-view consistency. The resulting compact yet discriminative feature fields enable stable PnP convergence, achieving state-of-the-art performance on localization benchmarks. Extensive experiments validate that SplitGS-Loc extends the utility of photometric GSFFs to accurate and efficient localization by exploiting Gaussian attributes, without per-scene training or iterative pose refinement.

Graph Domain Adaptation (GDA) aims to transfer graph classifiers across domains with both semantic and topological shifts. Existing Euclidean adversarial methods face two challenges: Structural Degeneration, where domain confusion entangles and suppresses label-relevant topology, and Optimization Instability, where minimax training induces oscillatory gradients under large structural shifts. We propose DisRFM, a geometry-aware GDA framework that addresses these challenges with Riemannian representation learning and flow-based transport. DisRFM embeds graph representations on a constant-curvature manifold and expresses them in geodesic polar coordinates. Polar endpoint regularization calibrates topologysensitive radial scales via univariate Wasserstein alignment and preserves scalenormalized class semantics through confidence-filtered angular alignment, with radial magnitude modulating pseudo-label reliability. DisRFM introduces topologyconditioned polar flow matching, which couples class-compatible source and target samples by a normalized polar transport cost and learns a metric-corrected vector field along geodesic interpolants. Theoretical analysis characterizes the structural risk of unconditional domain confusion and relates polar discrepancies and flow error to target risk. Extensive experiments under diverse domain shifts demonstrate that DisRFM consistently outperforms state-of-the-art methods.

Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically treated as fully data-driven, SMBO increasingly integrates external domain expertise to accelerate discovery. To overcome the opaque guidance and diminished integration fidelity of standard acquisition re-weighting, Probabilistic Circuits (PCs) have emerged as a generative surrogate alternative, enabling direct knowledge injection via conditional sampling. However, these generative routines lack the formal exploration-exploitation semantics required for rigorous optimization. We introduce the Distributional Conformance Score (DisCo), a principled metric that unifies the flexibility of PCs with a rigorous uncertainty framework. DisCo provides a bounded, $[0, 1]$-normalized measure of model "surprise" that (1) recovers properties comparable to kernel-based uncertainty known from, e.g., Gaussian Processes while maintaining linear-time inference, and (2) enables principled and accurate assessment of conformance of external knowledge w.r.t. model evidence. We then present DisCoMBO, a framework leveraging these properties for robust, knowledge-aware optimization. We prove that DisCoMBO is a zero-regret algorithm and demonstrate its effectiveness across diverse benchmarks from AutoML, material optimization, and wind park optimization.

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.

Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom and in which direction. We show that these directional confusions reveal distinct inductive biases that are invisible to accuracy alone. Using matched human and deep vision model responses on a natural-image categorization task under 12 perturbation types, we quantify asymmetry in confusion matrices and link it to the shape of the information–error trade-off. We characterize this trade-off geometry using three signatures extracted from the rate–distortion (RD) frontier: slope (beta), curvature (kappa) and efficiency (AUC). We find that humans exhibit broad but weak asymmetries, whereas deep vision models show sparser, stronger directional collapses. Robustness training reduces global asymmetry but fails to recover the human-like breadth–strength profile of graded similarity. Mechanistic simulations further show that different asymmetry organizations shift the RD frontier in opposite directions, even when matched for performance. Together, these results position directional confusions and RD geometry as compact, interpretable signatures of inductive bias under distribution shift.

Many bandit systems are deployed with offline historical data, such as past logs from earlier policies. By using these data, one can reduce the amount of online exploration needed early in learning. When the offline and online environments differ, such data can be biased for the online problem. For linear (contextual) bandits, this bias is directional: offline data may be informative in some feature directions and misleading in others. However, prior work typically controls this gap through a known Euclidean bound on the model parameters, which we prove is too coarse to separate easy and hard offline-to-online instances from offline data alone. To address this challenge, we introduce a directional bias certificate $(M_{\mathrm{bias}},\rho)$ that measures the offline-to-online gap through an $M_{\mathrm{bias}}$-induced norm and assigns different bias budgets to different directions. Building on this certificate, we propose \emph{Ellipsoidal-MINUCB}, which augments the online learning with an offline-pooled branch that safely exploits historical data. When the certificate is known, we show that the algorithm matches the standard SupLinUCB rate in the worst case and improves when offline coverage aligns with low-bias directions. When the certificate is unknown, we estimate it adaptively from offline and accumulated online data and establish a corresponding regret guarantee. Numerical experiments support the theory and show gains in aligned regimes.

Recent flow matching (FM) methods improve the few-shot adaptation of vision-language models, by modeling cross-modal alignment as a continuous multi-step flow. In this paper, we argue that existing FM methods are inherently constrained by incompatible geometric priors on pre-trained cross-modal features, resulting in suboptimal adaptation performance. We first analyze these methods from a polar decomposition perspective (\ie, radial and angular sub-manifolds). Under this new geometric view, we identify three overlooked limitations in them: \textit{1) Angular dynamics distortion}: The radial-angular coupling induces non-uniform speed on the angular sub-manifold, leading to regression training difficulty and extra truncation errors. \textit{2) Radial dynamics neglect}: Feature normalization discards modality confidence, failing to distinguish out-of-distribution and in-distribution data, and abandoning crucial radial dynamics. \textit{3) Context-agnostic unconditional flow}: Dataset-specific information loss during pre-trained cross-modal feature extraction remains unrecovered. To resolve these issues, we propose \textbf{warped product flow matching (WP-FM)}, a unified Riemannian framework that reformulates alignment on a warped product manifold. Within this framework, we derive \textbf{direct product flow matching (DP-FM)} by introducing a constant-warping metric, which yields a decoupled cylindrical manifold (\ie, direct product manifold). DP-FM enables independent radial evolution and constant-speed angular geodesic transport, effectively eliminating angular dynamics distortion while preserving radial consistency. Meanwhile, we incorporate classifier-free guidance by conditioning the flow on the pre-trained VLMs' hidden states to inject missing dataset-specific information. Extensive results across 11 benchmarks have demonstrated that DP-FM achieves a new state-of-the-art for multi-step few-shot adaptation.

Reconstructing audio from brain activity remains challenging. A key limitation lies in current audio diffusion models, which are primarily designed for text-based conditioning and lack mechanisms to incorporate non-linguistic continuous signals such as neural responses. While recent work in the visual domain has explored direct conditioning on brain activity, this approach has not yet been extended to audio diffusion models. We introduce a framework that directly guides a latent audio diffusion model with fMRI signals, avoiding intermediate feature prediction for the conditioning stage. Central to our approach is a transformer-compatible adapter that enables the integration of non-linguistic representations into the cross-attention layers of diffusion transformers (DiTs). Although motivated by neural decoding, this adapter provides a general mechanism for conditioning audio diffusion models on arbitrary continuous inputs, not inherently limited to neural signals. We demonstrate consistent improvements over baselines, including two-stage fMRI-to-latent conditioning, across both acoustic and semantic evaluation metrics. In addition, we introduce a fidelity metric grounded in a computational model of auditory neural processing, which allows us to quantify the contribution of individual auditory cortical regions to reconstructed audio representations.

N-dimensional splatting extends 3D Gaussian Splatting with conditioning variables such as view direction and time to model view-dependent and dynamic effects. Each primitive is lifted into a joint distribution over 3D position and conditioning variables, and at render time is conditionally sliced at the query to recover a 3D primitive whose mean, opacity, and covariance vary continuously with view or time. N-DGS (covering 6DGS and 7DGS) and UBS together represent the leading N-dimensional splatting formulations across Gaussian and Beta kernels, but share the same conditional-slicing step: all three effects are derived from the joint covariance matrix per primitive in every forward pass, requiring matrix inversions and regression-matrix multiplications that scale with primitive count. The conditional-slicing step is kernel-agnostic, so a single improvement carries across families. We introduce direct conditional parameterization, replacing the covariance-derived form with explicit per-effect parameters: a Cholesky precision factor for opacity and a displacement matrix with learnable per-dimension coupling for position. Applied to both kernels, the parameterization yields dGS (Gaussian) and dBS (Beta). Across five static and dynamic benchmarks, dGS and dBS match or exceed their covariance-derived counterparts on quality (up to +1.26 dB in the main MCMC evaluation) while running 6.9-7.7x faster at the slicing step and up to 2.66x faster end-to-end rendering. Direct conditioning is a kernel-agnostic drop-in replacement for covariance-derived conditioning in N-dimensional splatting.