NeurIPS 2026 论文列表

The ventral visual stream is widely modeled as a serial feedforward hierarchy in which V1, V4, and IT population codes develop sequentially during object recognition. We ask whether a second, concurrent coding mode exists—one organized not by anatomical order but by joint population structure across areas. Using Partial Information Decomposition applied to simultaneous multielectrode spiking recordings across all three areas at millisecond resolution—the first simultaneous three-area spiking PID analysis of the primate ventral stream—in two macaque monkeys viewing 25,000+ natural images, we decompose population coding into serial (unique per area) and synergistic (joint across areas) components at 5 ms resolution across five CNN target representations spanning low-level spatial features to high-level object identity. Three findings replicate across both animals and all five representations. First, synergistic inter-area coupling emerges before IT carries any unique object-related information—a dissociation of 15–65 ms that replicates in direction without exception across both animals—such that the joint population integrates before the apex encodes; moreover, V1–IT synergy persists for over 120 ms after V1’s unique information reaches zero. Second, although V1↔IT and V1↔V4 coupling emerge simultaneously and rise in parallel, V1↔IT exhibits stronger peak synergy at mid-to-high-level targets in both animals, suggesting a dominant role for non-serial joint coding. Third, when V1 and V4 are treated as an integrated feedforward block, their synergistic coupling with IT emerges last across all tested conditions—the feedforward foundation is the final component to join the synergistic mode, not the first. Together, these results show that serial and synergistic population codes co-occur in the same recordings, overlap in time, but follow different organizational principles, Providing a new level of nuance in our understanding of the primate ventral stream and introducing concrete constraints for biologically grounded models of vision.

Flow Matching (FM) provides an efficient formulation for continuous-time generative modeling via straight optimal transport (OT) trajectories. However, during accelerated sampling, these models face a fundamental variance-fidelity dilemma. Under few-step deterministic Ordinary Differential Equation (ODE) integration, the rigid OT trajectories suffer from mean concentration, leading to severe variance collapse and the loss of high-frequency details. Conversely, traditional Stochastic Differential Equations (SDEs) inject blind, exogenous Brownian noise to recover variance, but this isotropic exploration incurs inherent numerical attrition over long integration paths, bottlenecking peak fidelity. To resolve this, we introduce Self-Aligned Drifting (SADrift) to mitigate these challenges without external priors. SADrift is an inference-time framework that augments flow matching inference with self-interacting dynamics, without additional training. By maintaining a momentum history of the sampling trajectory, SADrift extracts the \emph{kinematic residual} to generate a deterministic extrapolative field. This mechanism forces the trajectory to efficiently explore the local tangent space, effectively decoupling exploratory variance into targeted deterministic dispersion and a minimal stochastic buffer. To safely anchor these dispersed trajectories and prevent out-of-distribution divergence, the residual drift is analytically integrated into a micro-time Ornstein-Uhlenbeck (OU) process, providing a mathematically sound, mean-reverting geometric safeguard. Requiring exactly zero additional Neural Function Evaluations (NFE), SADrift empowers a frozen base model to achieve a highly competitive FID of 2.00 at just 20 NFE on ImageNet-256. By successfully bypassing the numerical attrition of the 250-step SDE baseline (FID 2.06), SADrift establishes a new Pareto frontier for variance injection. Beyond image synthesis, we further validate SADrift on fast video generation paradigms (e.g., TurboDiffusion with Wan2.1-1.3B), demonstrating its universal capability to unlock latent generative quality across different modalities and acceleration regimes.

Diffusion-based generators set the current state of the art for synthetic tabular data, deployed downstream wherever direct access to real records is restricted. These methods approach but rarely exceed real-data utility on downstream tasks, and closing this synthetic--real performance gap has so far been pursued exclusively at training time, via architectural advances, scaling, and retraining of monolithic generators. The inference-time alternative, i.e., refining the outputs of a pre-trained backbone with parameters left untouched, has remained largely unexplored for tabular synthesis. We introduce $\textbf{TARDIS}$ (Tabular generation through Refinement, Distillation, and Inference-time Sampling), an inference-time refinement framework that operates on a frozen pre-trained backbone, configured per dataset by a Tree-structured Parzen Estimator search over score-level guidance during reverse diffusion, with each trial's objective set by an inner grid search over post-hoc sample selectors and an optional soft-label distillation step. The search space encodes a single mathematical pattern we name $\textit{Bidirectional Chamfer Refinement}$ (BCR): the symmetric Chamfer functional between synthetic and real samples is minimized both continuously, via a score-level gradient during reverse diffusion, and discretely, via batch-ranking post-generation. On the majority of datasets the search selects BCR-aligned configurations over alternatives encoded in the search space, evidence both for BCR as the dominant refinement pattern and for TARDIS's per-dataset search as a procedure that recovers this pattern. Across 15 binary, multiclass, and regression benchmarks TARDIS achieves a median $+8.6\%$ downstream-task improvement over models trained on real data (95\% CI $[+3.3, +16.4]$, Wilcoxon $p=0.016$, 11/15 strict wins) and improves over the underlying TabDiff backbone on all 15 datasets (mean $+12.9\%$, $p<10^{-4}$), matching the backbone on manifold fidelity, diversity, and sample-level privacy. The synthetic--real gap is therefore not primarily a training-time problem: on the studied corpus, inference-time refinement of a pre-trained tabular diffusion backbone reaches and exceeds real-data utility in 1 to 80 minutes on a single consumer-grade GPU.

Sparse dictionary learning (and, in particular, sparse autoencoders) attempts to learn a set of human-understandable concepts that can explain variation on an abstract space. A basic limitation of this approach is that it neither exploits nor represents the semantic relationships between the learned concepts. In this paper, we introduce a modified SAE architecture that explicitly models a semantic hierarchy of concepts. Application of this architecture to the internal representations of large language models shows both that semantic hierarchy can be learned, and that doing so improves both reconstruction and interpretability. Additionally, the architecture leads to significant improvements in computational efficiency.

Generative models for function-valued data, such as time series and solutions to partial differential equations, must learn distributions over infinite-dimensional spaces rather than over finite-dimensional vectors. Functional Flow Matching (FFM) is a recent extension of Flow Matching to this setting, learning a velocity field whose flow transports a Gaussian prior to the data distribution. However, FFM inherits a structural limitation from standard Flow Matching: in each training batch, prior and data samples are paired independently, so the conditional bridge between them must simultaneously traverse the shared global structure of the dataset and instance-specific residuals. This limitation is more consequential in function space than in finite dimensions: directly formulating optimal transport on function spaces is technically delicate, and a flat Euclidean surrogate ignores the function-space geometry that distinguishes function-valued data. We propose kernel Functional Flow Matching (kFFM), which replaces the independent pairing by entropic optimal transport in a kernel-induced Hilbert space via the Hilbert Sinkhorn Divergence, leaving the FFM neural-operator architecture unchanged. Theoretically, we prove the HSD objective is well-posed on Banach ambient spaces with bounded kernels, derive compact-metric approximation bounds against quadratic-cost OT with an explicit kernel-cost mismatch term, and quantify the gap between the function-space objective and the truncated representation computed on a grid, at a rate governed by data regularity. Empirically, kFFM consistently improves MMD-RBF distributional matching over FFM, diffusion, adversarial, and finite-dimensional OT baselines on time-series and PDE benchmarks, with function-space-aware kernels emerging as the strongest choices on every PDE and path-valued benchmarks.

Modern AI agents can plan, reflect, reason, and act over long-horizon digital tasks. Even so, they cannot plan to steer a social interaction in real time. Doing so requires anticipating how latent factors like trust and resistance will evolve under the agent's actions, fast enough to plan within a conversational turn. Generative world models approach this by narrating possible futures, but autoregressive text generation is both too slow for real-time planning and fundamentally lossy. Representation-predictive methods can be superior, but are underexplored for interaction. We build LID-Bench, the first controlled testbed with oracle latent states for interaction dynamics, enabling systematic comparison of generative and representation-predictive world models against known dynamics. A bottleneck decomposition on three generative models spanning 117M--350M parameters reveals that all learn interaction dynamics internally, with text-rendering signal losses of 89--100\% observed by horizon $k{=}5$. Our representation-predictive model (Social-JEPA, 125M params, 500K trainable) bypasses text entirely, forecasting latent dynamics $2.8$--$3.9\times$ more accurately while performing rollouts $1{,}059$--$2{,}314\times$ faster---making real-time planning within conversational turn-taking latencies feasible.

The Linear Representation Hypothesis (LRH) posits that Transformer activations decompose into sparse linear combinations of interpretable directions, with Sparse Autoencoders (SAEs) serving as the predominant method for recovering such decompositions. We propose that idempotency -- the requirement that re-encoding an SAE's output reproduces the same latent representation -- is a desirable property for any faithful dictionary learning method, and demonstrate empirically that most SAEs deviate very far from this ideal. We identify two underlying failure modes: exploding representation norms and unstable active feature sets. Further analysis reveals that even individual SAE latents cannot be reliably reconstructed under re-encoding, and that SAE outputs exhibit significant sensitivity to input scaling. We show that idempotency can be improved via an auxiliary loss term and demonstrate its potential to improve SAE interpretability.

Hypergraphs model higher-order interactions, but realistic hypergraph generation remains difficult because incidence, hyperedge-size heterogeneity, and overlap structure are not faithfully captured by pairwise reductions. We propose HEDGE, a generative model defined directly on relaxed incidence matrices via a structured stochastic diffusion. The forward process combines a hypergraph-specific two-sided heat operator with an Ornstein--Uhlenbeck component, preserving structure-aware noising near the data while yielding an explicit Gaussian terminal law. Conditional on an observed hypergraph, this forward process is linear-Gaussian, so conditional means, covariances, scores, and reverse-drift targets are available in closed form. We therefore learn a permutation-equivariant state-only reverse-drift field in incidence space by regressing onto exact conditional targets, and generate samples by simulating a learned reverse-time SDE from the Gaussian base law. We establish exactness in the ideal state-only setting together with finite-horizon stability guarantees, and empirically show improved hypergraph generation quality relative to strong baselines.

Vector Quantization (VQ) based generative image compression has achieved remarkable perceptual quality. However, existing VQ codecs suffer from two fundamental limitations. First, they lack efficient content-adaptive entropy modeling and rely on static frequencies, leading to low coding efficiency. Second, the inherent conflict between discrete indices and continuous priors prevents true end-to-end joint Rate-Distortion (RD) optimization. To resolve these issues, we propose HyperVQ, a principled framework that establishes a high-performance hyperprior entropy foundation for VQ-based codecs. The core insight of HyperVQ is to shift probability modeling entirely into the continuous embedding space. Instead of directly predicting probabilities for discrete symbols, HyperVQ predicts a high-dimensional continuous multivariate Gaussian distribution for the continuous latents. By treating the discrete codebook entries as fixed "anchors" in this space, we convert the continuous Gaussian density into categorical index probabilities based on relative distances. This elegant formulation provides a powerful, spatially-adaptive entropy engine and renders the cross-entropy rate objective fully differentiable, empowering the network to actively and dynamically optimize the RD trade-off during training. To ensure practicality, we design the lightweight H Block and the Probability Estimation Engine (PEE) to facilitate highly parallel, millisecond-level inference. Experiments demonstrate that HyperVQ acts as a universal module across diverse VQ architectures (single-scale, large-codebook, RVQ), achieving an average bitrate saving of 18.5%, which is 7.28x the saving achieved by conventional Huffman coding. This establishes a robust, RD-controllable foundation for next-generation generative image compression.

As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while prompting can be practical for controllability, it remains fragile due to models' high sensitivity to exact prompt wording and structure. This brittleness has driven interest in activation steering techniques that offer more stable and predictable control over model behavior. However, existing activation steering methods require per-concept optimization, which makes them ill-suited to deployment scenarios where the concept set is large, evolving, or only specified at request time: each new concept incurs at least minutes of optimization on the target model. We propose HyperTransport, a hypernetwork framework that amortizes this cost by mapping embeddings from a pretrained encoder (CLIP in our instantiation) directly to intervention parameters, trained end-to-end using an optimal transport loss. Once trained, HyperTransport produces each new intervention in a single hypernetwork forward pass, 3600–7000x faster than per-concept fitting. On concepts unseen during training, it matches the strongest per-concept baselines at inducing the target concept. By decoupling concept representation from intervention prediction, HyperTransport combines three capabilities that no existing approach offers as a set: amortized steering for open-ended concept sets, continuous interpretable strength control, and cross-modal conditioning where reference images can directly steer text-based generation. We validate HyperTransport on DMD2 and Nitro-1-PixArt across 167 held-out test concepts via CLIP-based metrics, a VLM-as-a-judge evaluation, and a user study. In pairwise comparisons, both human and VLM judges prefer HyperTransport over prompting ~2x as often.

Geometric modeling has become an essential paradigm in AI for Science, where data often inhabit intrinsically curved or structured spaces. However, existing manifold generative models often rely on closed-form or tractable access to global geometric primitives, such as geodesic distances, parallel transport, or spectral decompositions, which are rarely available beyond canonical geometries. To overcome this limitation, we propose Horizontal Diffusion Models (HDMs), a score-based generative framework defined on the orthonormal frame bundle of a Riemannian manifold. Instead of requiring global geometric operations, HDM lifts Euclidean diffusion processes through the Levi-Civita horizontal distribution, using only local metric and connection information while preserving intrinsic manifold geometry. This construction allows standard Euclidean score networks to be lifted into geometry-consistent and gauge-equivariant horizontal vector fields, bypassing the need for manifold-specific neural architectures. We further derive a horizontal KL objective that reduces to a Euclidean score-matching loss and analyze the curvature-dependent cost of the lift through a generalization bound. Experiments on parametric surfaces and scientific datasets demonstrate high-fidelity generation across diverse manifolds with nontrivial geometry and topology.

Generative models for drug design which directly produce synthetic pathways have gained significant popularity due to their ability to constrain the search space to synthetically accessible molecules. However, existing methods have focused primarily on de novo molecular design, and rarely start the generation process from known binders. In this paper, we present HELiX: a template-based GFlowNet for localized exploration of chemical space. HELiX learns to partially decompose a given synthetic trajectory to an intermediate state, and then perform forward synthesis in a manner that preserves synthetic accessibility, leading to diverse, high-reward analog generation. Moreover, we prioritize sample efficiency by incorporating Bayesian optimization into the training procedure. We diagnose problems inherent to training GFlowNets with Bayesian optimization and introduce a greedy acquisition strategy which effectively balances between exploration and exploitation without the need for reward shaping. Finally, we show that local exploration is inherently robust to noisy oracle evaluations, a common problem in drug development when using unreliable proxies for binding affinity.

We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand–object configurations, in the product manifold of hand articulations. While recent unconditional generative priors showed success in modeling human pose, hands are versatile articulated bodies often found in interaction with other objects. This makes it crucial to develop a generative prior which can model the plausible hand poses conditioned on the object under interaction. To this end, our HaNDF proposes a geometry-aware, object-conditioned neural distance field, representing the plausible articulations in the zero-level set of a conditional field. We leverage tools from Riemannian geometry to (i) represent plausible hands in the zero level-set of an object-induced neural field, and (ii) project a given pose onto the this field. Using HaNDF, we can also optimize for a plausible condition, determining the object under interaction. Our extensive evaluations demonstrate that our HaNDF provides a flexible and powerful prior for hand–object interaction, suitable for applications such as pose refinement, reconstruction under occlusion, and physically plausible manipulation synthesis.

Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold'' labels are expensive to collect and limited in quantity. Organizations thus often turn to collecting vast but noisy "silver'' labels from crowdsourced workers or vendor annotators as proxies for gold labels. Because gold remains the evaluation target, naively aggregating noisy silver labels may introduce bias, and estimators built on sparsely observed gold labels may have high variance to resolve the model performance gaps that guide practical decisions. Model evaluation has become an ongoing operational practice rather than a one-time exercise, with evaluation rounds repeating across model versions, releases, and content domains. A natural question is whether the previous historical evaluation data can be used to improve each new round of evaluation. We introduce HERO (History Enhanced RObust model evaluation), a novel framework that uses historical data to reduce both bias and variance in model performance evaluation. HERO calibrates silver labelers' performance learned from historical gold annotations, and stabilizes the resulting estimator by anchoring it to covariate information measured with high precision in the historical data. HERO can be broadly applied across multiple common evaluation tasks, and remains valid when only a subset of historical labelers appears in the current round. We establish conditions under which the bias and variance reductions hold, showcase HERO's performance in simulation studies, and demonstrate its effectiveness on real-world model evaluation benchmarking datasets.

This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach in the adversarial defense domain, we propose a novel smoothing-based defense method, termed Disrupt-and-Rectify Smoothing (DR-Smoothing). Specifically, we integrate a two-stage prompt processing scheme—first disrupting the input prompt, then rectifying it—into the conventional smoothing defense framework. This disrupt-and-rectify approach improves upon previous disrupt-only approaches by restoring out-of-distribution disrupted prompts to an in-distribution form, thereby reducing the risk of unpredictable LLM behavior. In addition, this two-stage scheme offers a distinct advantage in striking a balance between harmlessness and helpfulness in jailbreaking defense. Notably, we present a theoretical analysis for generic smoothing framework, offering a tight bound for the defense success probability and the requirements on the disruption strength. Our approach can defend against both token-level and prompt-level jailbreaking attacks, under both established and adaptive attacking scenarios. Extensive experiments demonstrate that our approach surpasses current state-of-the-art defense methods in terms of both harmlessness and helpfulness.

We propose a novel solution to reward hacking in reinforcement learning. Our setup involves a misspecified reward function corresponding to task completion, and a classifier that flags invalid solutions. As in frontier LLM development, the classifier makes systematic errors. Consequently, reward penalties and data filtering based on the classifier fail to prevent reward hacking. To address this limitation, we introduce GRAFT: Gradient Routing Adapter Fine Tuning. Rather than penalizing or filtering flagged behaviors during training, we constrain (``route'') gradient updates to specific parameters. This has two benefits. First, the model does not learn to exploit classifier errors, i.e., it does not learn to hide the unintended behavior. Second, the unintended behavior can be disabled by ablating the parameters to which they were localized. We demonstrate these benefits in a variety of RL environments where reward penalties result in monitor-subverting policies. Additionally, we show that our method remains effective when we scale model size and task complexity by distilling frontier LLM trajectories into Qwen3-32B. Overall, we show that GRAFT is a promising approach for leveraging unreliable monitors during post-training in environments where true performance is difficult to evaluate.

Flow matching learns a velocity field whose ordinary differential equation transports a simple reference distribution to a data distribution. Existing theory typically controls this velocity through ambient, isotropic Lipschitz regularity, which becomes extremely pessimistic when data concentrate near a low-dimensional manifold. In this regime, standard error bounds can blow up drastically near the terminal time and fail to explain why flow matching should remain stable. We study flow matching for distributions obtained by smoothing a density on a compact manifold with small Gaussian noise. Our first result is a tube-local Lipschitz regularity theorem showing that, on the relevant neighborhood of the manifold, the population flow-matching velocity has a Jacobian bound that grows only at the inverse effective noise scale. This improves substantially over the stronger singular behavior suggested by existing ambient analyses. Our second result gives a directional description of the velocity Jacobian: tangential directions exhibit mild growth, normal directions are strongly contractive, and tangential-normal coupling is lower order. This reveals that the apparent singularity is highly structured rather than uniformly harmful. These results provide geometric foundations for sharper stability guarantees and suggest a path toward intrinsic-dimension generalization bounds for flow matching on manifold-concentrated data.

The success of generative molecular design hinges on a model's steerability toward high-reward samples. Because many molecular properties are intrinsically linked to molecular size, accurately capturing the joint distribution of properties and the number of atoms is essential. However, current diffusion and flow-based models fix the number of atoms, which ultimately limits their ability to navigate this complex relationship. To address this, we introduce Morph, a flexible-size generative model for conditional and unconditional 3D molecular design based on geometric graphs. By dynamically adapting size, Morph can seamlessly integrate existing structural priors, like scaffolds, and significantly enhances property steering. We show that Morph matches current fixed-size state-of-the-art models while offering the benefit of unparalleled sampling flexibility. We demonstrate out-of-distribution generation in regimes where previous models fail, paving the way for enhanced generative modeling for molecular design.

Generative modeling can be formulated as learning a mapping $f$ such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iteratively at inference time, for example, in diffusion/flow-based models. In this paper, we propose *Drifting Models*, a generative modeling framework that evolves the pushforward distribution during training and naturally admits one-step inference. We introduce a drifting field that governs the sample movement and achieves equilibrium when the distributions match. This leads to a training objective that allows the neural network optimizer to evolve the distribution. In experiments, our one-step generator achieves state-of-the-art results on ImageNet 256$\times$256, with FID 1.54 in latent space and 1.61 in pixel space. We hope that our work opens up new opportunities for high-quality one-step generation.