NeurIPS 2026 Papers

Distributed training is essential for scaling LLM training across thousands of GPUs. However, as distributed training requires complex implementations, they are prone to silent bugs, which do not produce explicit error signals but lead to in correct training outcomes. Common debugging practices based on monitoring training loss or gradient norm curves are slow or unable to detect bugs and also do not help localize bugs. We design and implement nnTrace, the first systematic differential testing system for detecting and localizing silent bugs in distributed training. nnTrace aligns intermediate tensors from distributed training with those from a trusted reference implementation. To properly compare the floating-point values in the corresponding tensors, we propose a novel mathematical analysis that provides a guideline for setting tolerances, enabling nnTrace to distinguish bug-induced errors from numerical errors. Experimental results demonstrate that nnTrace effectively detects 11 existing bugs and 3 new bugs in the widely used Megatron-LM framework. nnTrace is effective in various training recipes, including low-precision recipes involving BF16 and FP8. Notably, Megatron-LM has already adopted the method proposed by nnTrace in its development workflow. Our code is available at https://anonymous.4open.science/r/NECK-3C61/.

High-dynamic-range (HDR) formats and displays are becoming increasingly prevalent, yet state-of-the-art image generators (e.g., Stable Diffusion and FLUX) typically remain limited to low-dynamic-range (LDR) output due to the lack of large-scale HDR training data. In this work, we show that existing pretrained diffusion models can be easily adapted to HDR generation without retraining from scratch. A key challenge is that HDR images are natively represented in linear RGB, whose intensity and color statistics differ substantially from those of sRGB-encoded LDR images. This gap, however, can be effectively bridged by converting HDR inputs into perceptually uniform encodings (e.g., using PU21 or PQ). Empirically, we find that LDR-pretrained variational autoencoders (VAEs) reconstruct PU21-encoded HDR inputs with fidelity comparable to LDR data, whereas linear RGB inputs cause severe degradations. Motivated by this finding, we describe an efficient adaptation strategy that freezes the VAE and finetunes only the denoiser via low-rank adaptation in a perceptually uniform space. This results in a unified computational method that supports both text-to-HDR synthesis and single-image RAW-to-HDR reconstruction. Experiments demonstrate that our perceptually encoded adaptation consistently improves perceptual fidelity, text-image alignment, and effective dynamic range, relative to previous techniques.

Single-image 3D methods often trade off pixel alignment and completeness: monocular reconstructors stay registered to visible pixels but stop at the first surface, while image-to-3D generators complete shapes in a canonical frame that is no longer aligned to the input. We introduce methodlong (method), a pixel-aligned multilayer geometry representation that keeps generated 3D in the input camera frame while completing geometry beyond the visible surface. For each input ray, method predicts an ordered stack of camera-space 3D points, where the first entry explains the observed surface and subsequent entries complete occluded surfaces along the same pixel ray. methodnet, a flow-matching diffusion transformer with a frozen 2D foundation encoder, learns this representation from depth-filled dense targets, removing the need for a separate per-layer visibility-mask head. The same objective covers static objects, static scenes, and dynamic clips, with temporal attention added for video. Because the output preserves the input camera frame, intrinsics, and pixel correspondences, it provides a direct 2D-to-3D interface for downstream systems, including text-driven 3D scene editing, geometry-conditioned novel-camera video generation, and training-free integration with existing textured-mesh generators. Across object, scene, and dynamic benchmarks, method improves visible-surface accuracy and complete-geometry quality over strong reconstruction and generation baselines.

Equipping artificial agents with spatial intelligence requires a comprehensive generative prior over the dynamic 3D world. We propose World Motion Models (WMMs) that capture "what was, is, and will be where across time" via sparse SE(3) pose trajectories. WMMs are built on the observation that elements of dynamic scenes can be well-approximated by a set of rigid SE(3) trajectories, a minimal yet expressive primitive for 4D modeling. This representation unifies articulated objects, human bodies, hand-object interactions, piecewise-rigid scene dynamics, camera motion, and even robot states and actions into a single shared space. Given this representation, we cast the joint distribution of these entities as a flexible sequence modeling problem, utilizing flow-matching with per-token noise levels. Coupled with a context token mechanism with non-sequential conditioning, this formulation supports any-to-any marginal conditioning across an arbitrary number of entities and time steps. Tasks such as future prediction, motion infilling, model-predictive control, inverse kinematics, cross-embodiment retargeting, and policy learning all reduce to the application of different masks over the same network. Experiments on diverse 6 applications of 3D vision and robotics demonstrate the versatility and flexibility of WMMs with strong performance.

Oscillations and synchronization are widely believed to play a fundamental role in representation and computation. However, existing machine learning approaches based on synchronization dynamics have largely been confined to specialized settings such as object discovery, with limited evidence of scalability to standard vision benchmarks or logic reasoning tasks. We propose the \emph{Winfree Oscillatory Neural Network} (\emph{WONN}), a dynamical neural architecture based on generalized Winfree dynamics. \emph{WONN} evolves representations on the torus $(S^1)^d$ through structured oscillatory interactions, combining phase-based inductive biases with flexible and hierarchical interaction mechanisms instantiated as either fixed trigonometric mappings or learnable neural networks. We evaluate \emph{WONN} on image recognition and complex reasoning tasks, including CIFAR, ImageNet, Maze-hard, and Sudoku. Across these domains, \emph{WONN} achieves competitive or superior performance with strong parameter efficiency. \textbf{In particular, \emph{WONN} is, to our knowledge, the first synchronization-based oscillatory architecture to scale competitively to ImageNet-1K.} Furthermore, on Maze-hard, \emph{WONN} achieves \textbf{80.1\% accuracy using only 1\% of the parameters of prior state-of-the-art models}. These results suggest that structured oscillatory dynamics provide a scalable and parameter-efficient alternative to conventional neural architectures.

RLHF is widely used to align flow-matching text-to-image models with human preferences, but often leads to severe diversity collapse after fine-tuning. In RL, diversity is often assumed to correlate with policy entropy, motivating entropy regularization. However, we show this intuition breaks in flow models: policy entropy remains constant, even while perceptual diversity collapses. We explain this mismatch both theoretically and empirically: the constant entropy arises from the fixed, pre-defined noise schedule, while the diversity collapse is driven by the mode-seeking nature of policy gradients. As a result, policy entropy fails to prevent the model from converging to a narrow high-reward region in the perceptual space. To this end, we introduce perceptual entropy that captures diversity in a perceptual space and maintains the property of standard entropy. Building upon this insight, we propose two entropy-regularized strategies, Perceptual Entropy Constraint and Perceptual Constraints on Generation Space, to preserve perceptual diversity and improve the quality. Experiments across two base models, neural and rule-based rewards, and three perceptual spaces demonstrate consistent gains in the quality-diversity trade-off; PEC achieves the best overall score of 0.734 (vs.\ baseline's 0.366); a complementary setting of PEC further reaches a diversity average of 0.989 (vs.\ baseline's 0.047). Code will be released.

Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform replay improves over this strong baseline. Across diverse RL settings, we show that the effectiveness of non-uniform replay is governed by three factors: replay volume, the number of replayed transitions per environment step; expected recency, how recent sampled transitions are; and the entropy of the replay sampling distribution. Our main contribution is clarifying when non-uniform replay is beneficial and providing practical guidance for replay design in modern off-policy RL. Namely, we find that non-uniform replay is most beneficial when replay volume is low, and that high-entropy sampling is important even at comparable expected recency. Motivated by these findings, we adopt a simple Truncated Geometric replay that biases sampling toward recent experience while preserving high entropy and incurring negligible computational overhead. Across large-scale parallel simulation, single-task, and multi-task settings, including three modern algorithms evaluated on five RL benchmark suites, this replay sampling strategy improves sample efficiency in low-volume regimes while remaining competitive when replay volume is high.

Practitioners using machine learning on noisy tabular data can often find simpler, more interpretable models with the same accuracy as black box models. In some cases, this is because the noise in the data induces a large Rashomon set, that is, a large set of near-optimal models, which then frequently contains simpler models. However, our understanding of this effect from discrete hypothesis classes --- the Rashomon set's size, the diversity of its predictions, and implicit regularization --- leads to a contradiction in continuous hypothesis spaces, and thus does not directly establish whether noise should necessarily induce simpler or more diverse models. We reconcile the seemingly conflicting effects by showing geometrically that noise drives simplicity through separate mechanisms for linear, logistic, and generalized additive models. We introduce the Rashomon reducibility cost to quantify whether a given model simplification is possible within the Rashomon set, and characterize how this cost changes under general data properties such as label noise. For practitioners working with noisy tabular data, our results suggest that simpler, more interpretable models will be included in the Rashomon set, so that black box models will generally not be needed.

Latent Diffusion Models dominate generative modeling but face two-stage training complexity and the inherent reconstruction limits of pre-trained autoencoders. Pixel-space Flow Matching offers a simplified end-to-end alternative yet often encounters training instabilities when using the standard $v$-prediction objective. Recent research attributes this difficulty to the off-manifold nature of noised targets, prompting a widespread shift toward $x$-prediction. In this paper, we carefully investigate this prevailing assumption through patch-wise Principal Component Analysis (PCA), and we reveal that the failure of $v$-prediction is primarily a bandwidth issue. Specifically, the velocity field in minor components is mostly determined by the raw noisy input, which differs from the dynamics in the principal component space. Deep backbones must propagate this minor information to the final layer to predict the velocity field, which consumes significant model capacity that should be dedicated to principal components. This is particularly critical in pixel space where noise dimensionality is comparable to the hidden dimension. To resolve this, we simply incorporate a term containing the raw noisy input into the final layer. This architectural bypass enables the backbone to focus on high-level semantics while the final layer efficiently reconstructs the minor velocity field. Our method successfully revives $v$-prediction and achieves competitive performance on ImageNet $256\times256$ dataset compared to $x$-prediction baselines.

Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, *e.g.,* band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks. Whether the two representations align is an open question, which we decompose into three sub-questions: *what does the model learn*, *what does the model use*, and *how much can be explained*. We answer them with layer-wise ridge probing, LEACE-style cross-covariance subspace erasure, and a transparent classifier benchmarked against a random-feature baseline. The audit covers three foundation models (CSBrain, CBraMod, LaBraM), five clinical tasks (MDD, Stress, ISRUC-Sleep, TUSL, Siena), and a 6-family 63-feature lexicon. Of the $945$ (model, task, feature) units, $648$ ($68.6\%$) are representation-causal and $199$ ($21.1\%$) are encoded-only. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. Frequency-domain features dominate, but the other five families each contribute substantial causal mass. Confirmed features recover, on average, $79.3\%$ of the foundation model's advantage over the random baseline, with a clean task gradient (MDD $\approx 0.99$ down to Stress $\approx 0.56$): tasks near ceiling are almost fully recovered by the lexicon, while harder tasks leave a non-trivial residual that pinpoints a concrete target for future concept discovery.

The manifold hypothesis suggests that real-world data concentrates on low-dimensional structures, yet navigating meaningful trajectories on latent manifolds remains an open challenge. A common approach is to construct density-aware metrics, but such constructions bundle two distinct questions: (i) where valid data lies and (ii) how we choose to traverse on the manifold. Since different tasks may demand different traversal behaviors even on the same data manifold, specifying these two aspects independently offers additional modeling flexibility. We propose to factor the path objective into a kinetic term governed by a freely chosen geometric prior and a score-based potential, derived from the negative log-density of a pretrained diffusion model, that acts as a soft constraint for manifold adherence. The potential encourages dynamics to evolve along the data support, while the geometric prior remains free to encode task-specific notions of path optimality. To solve the resulting optimization problem, we introduce \textit{geodesic force matching} (GFM), a direct-collocation algorithm that discretizes the trajectory and optimizes all waypoints jointly to satisfy the Euler--Lagrange force balance in a least-squares sense. On synthetic manifolds and 3D shape interpolation, our method produces smooth, on-manifold paths and performs favorably against recent density-aware baselines, most notably in the low-noise regime where those baselines become numerically unstable.

Graphs with a simple spectrum admit cubic-time isomorphism testing, yet we prove that for every natural number $k$, the $k$-Weisfeiler-Leman ($k$-WL) test cannot distinguish all non-isomorphic graphs with a simple spectrum. As the WL hierarchy upper-bounds the distinguishing power of widely-used Graph Neural Networks (GNNs), this incompleteness applies to all such GNNs, ruling out completeness for every $k$-WL-aligned GNN family. To close this gap, we introduce PRiSM (Partition, Refine, Solve, Match), the first provably complete canonicalization of simple-spectrum eigendecompositions. PRiSM obtains the completeness guarantee that prior canonicalizations provably lack, and resolves the open problem of achieving complete expressivity on simple-spectrum graphs. When composed with DeepSets or a Transformer, PRiSM achieves universal approximation on simple-spectrum graphs, justifying the use of canonicalized Laplacian positional encodings. Empirically, PRiSM performs comparably to or outperforms existing spectral canonicalizations on graph regression, classification, and expressivity benchmarks.

Large vision–language models (LVLMs) are inherently opaque, making it difficult to determine whether their outputs are grounded in visual evidence or driven by language-model priors. Existing concept-based interpretability methods are limited to classification settings, rely on proxy models, or require predefined tokens, and thus do not extend LVLMs. We propose Text-Guided Concept Learning (TGCL), a weakly supervised framework for extracting multimodal concept vectors in LVLMs without token supervision. TGCL builds concept-to-image mappings from data and extracts patch-level activations via concept-guided probing. It then formulates concept learning as a contrastive disentanglement problem, isolating concept-specific patches from background patches to produce sparse, stable, and semantically aligned concept vectors. We conduct experiments on four datasets—ImageNet, MSCOCO, CIFAR100, and DTD—and three recent LVLMs. TGCL outperforms recent interpretability methods, achieving up to 4\% higher sparsity, 11\% lower instability, 17\% lower overlap, and 20\% improvement on attribution faithfulness compared to state-of-the-art baselines.

Fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples. Existing methods typically frame this problem as sampling from a reward-tilted distribution, which arises as the solution to a KL-regularized reward-maximization problem. Here we take an alternative approach and introduce an *optimal transport* regularizer built directly from the pre-trained drift; we show that the resulting fine-tuning problem is equivalent to a *deterministic optimal-control problem* on the flow. Assuming access to a pre-trained flow map, we exploit this equivalence to devise a simulation-free reinforcement-learning algorithm for fine-tuning generative flows. We call the resulting framework **Wasserstein-Tilted Flow Maps (WTF)**, the first end-to-end fine-tuning recipe native to flow maps. The output of our approach is itself a fine-tuned flow map, retaining few-step reward-aligned inference at deployment. Numerical experiments at text-to-image scale highlight both the efficiency and the efficacy of our approach. More broadly, our work argues that accelerated samplers such as flow maps are essential infrastructure for efficient post-training, and that the dominant KL-regularized formulation of fine-tuning is only one of many choices worth revisiting.

Compared with deterministic methods that output only point estimates, probabilistic forecasting can characterize both future trends and their uncertainty simultaneously, making it more suitable for decision-making in complex real-world scenarios. In recent years, diffusion models, owing to their powerful generative modeling capability, have been introduced into the field of time series forecasting. However, we find that most existing diffusion-based forecasting methods directly construct the diffusion process in the original future space, which is easily affected by heteroscedastic scale imbalance, causing highly volatile dimensions to dominate the denoising learning and leading to unstable corrections near observation boundaries. Motivated by these issues, we propose VolaRM, a volatility-whitened probabilistic residual modeling framework for long-term time series forecasting. By constructing a conditional probabilistic base distribution, it represents future targets as whitened residuals relative to this base distribution and performs conditional modeling in the normalized residual space, thereby alleviating scale imbalance across variables and forecasting horizons. Meanwhile, a specific gating mechanism is introduced to enhance boundary continuity and the stability of long-term forecasting. Extensive experiments on eight real-world datasets from different application domains demonstrate the effectiveness of VolaRM.

Large vision-language models (VLMs), which process both visual inputs and text queries, incur high inference costs due to the large number of visual tokens, making visual token pruning a natural approach for improving efficiency. Recent work has leveraged query information to guide token selection, often through heuristics derived from attention patterns or token dynamics. However, these approaches do not explicitly exploit the underlying structure of query-vision interactions. We address this gap by observing that the query-vision interaction matrix, naturally induced by the query and key projections already present in transformer attention, is effectively low-rank, with a small number of dominant latent interaction modes capturing query-relevant semantics. Motivated by this observation, we propose Query-Vision Decomposition (QViD), a novel training-free query-aware visual token pruning method that exploits this structure. Extensive experiments on both image and video understanding benchmarks show that QViD consistently improves performance under matched token budgets, with the largest gains appearing under aggressive compression regimes.

Real-world visualization requests are routinely ambiguous, incomplete, or factually incorrect, yet existing Text-to-Visualization (Text-to-Vis) systems assume well-specified inputs and produce charts in a single pass. When queries are imperfect, a system must interact with the user to recover the true intent, but no benchmark or method supports this dynamic process. We introduce VisInteract, a new paradigm that reframes Text-to-Vis as interaction-driven intent recovery, and VisInteract-Bench, to our knowledge, that is the first benchmark for dynamic interactive Text-to-Vis, featuring controlled imperfection injection, a leakage-controlled User Agent for realistic multi-turn feedback, and dual-perspective (code and chart) automated evaluation. On the algorithmic side, we propose Vis-MCTS, a Monte Carlo Tree Search (MCTS) enhanced method, introducing improvements over classical MCTS, that Progressive Widening to tame the unbounded tool-argument space in tree search, cross-rollout information sharing so clarifications and critiques benefit the entire search tree, and Dimension-Aware Reward Decomposition that routes scalar user feedback along data-fidelity, visual-design, and intent-alignment dimensions to resolve credit assignment across heterogeneous actions. Extensive Experiments across two LLM backbones show that Vis-MCTS consistently outperforms all Text-to-Vis baselines, improving end-to-end task success by 13.40\%--16.27\% over the strongest interactive baseline and by more than 5\times over non-interactive ones.

Protein language models (pLMs) have shown strong potential for zero-shot prediction of missense variant effects, yet systematic benchmarking on viral proteins remains limited, a critical gap given the need for proactive tools that can anticipate emerging mutations ahead of experimental validation. Here we introduce ViroGym, a comprehensive benchmark evaluating pLMs across three tasks: 79 deep mutational scanning (DMS) assays covering eukaryotic viruses with 552,065 mutated sequences across 7 phenotypic readouts, 21 influenza neutralisation tasks, and a real-world pandemic prediction task for SARS-CoV-2. We benchmark well-established pLMs on fitness landscapes, antigenic diversity, and pandemic forecasting, and find that the ProGen2 family consistently achieves the strongest performance across all three tasks. Crucially, DMS and neutralisation performance reliably identifies models that generalise to real-world emergence, even though the mutation sets they surface barely overlap, revealing that complementary in vitro benchmarks capture the evolutionary constraints needed for real-world mutation forecasting.

Learning semantic representations for 3D Gaussian Splatting has recently emerged as a promising direction for 3D scene understanding. However, existing 2D-to-3D semantic lifting approaches rely primarily on 2D segmentation supervision and lack object-level priors, causing the learned representation to be driven by local mask evidence rather than holistic object structure. In this paper, we present VLSplat, a vision-language guided approach for object-centric semantic lifting in 3D Gaussian Splatting. Our method augments mask-based lifting with object-level semantic priors derived from vision-language models and refines Gaussian representations using an object-group scene graph. The refinement is performed through two language-guided modules: intra-group refinement suppresses semantic noise within object groups, while inter-group acquisition expands object support regions by incorporating structurally relevant candidates from neighboring groups. Extensive experiments on indoor scene datasets demonstrate that VLSplat improves semantic consistency and structural completeness. Our code and models are available at https://vlsplat.github.io/