NeurIPS 2026 Papers

Transformers achieve strong accuracy but incur high compute and memory cost. Structured pruning reduces inference cost, but most methods rely on retraining or multi-stage optimization, which limits post-training deployment. We propose CORP, a closed-form one-shot structured pruning method that removes MLP dimensions and attention substructures using only unlabeled calibration data without labels, gradients, or fine-tuning. CORP formulates structured pruning as a representation recovery problem. It models removed components as affine functions of retained components and derives closed-form ridge regression solutions that fold compensation into model weights. This minimizes the expected representation error under the calibration distribution. Experiments on ImageNet with DeiT reveal strong redundancy in both MLP and attention representations. Without compensation, one-shot structured pruning causes severe accuracy loss. With CORP, models retain high accuracy under aggressive sparsity. On DeiT-Huge, CORP achieves 83.27% Top-1 accuracy after pruning 50% of both MLP and attention structures.

Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD) strategies leading to low-rank approximations of the original model weights. However, the underlying single shared subspace modeling can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from costly iterative dictionary and coefficient updates. We propose **COMPOT** (**C**alibration-**O**ptimized **M**atrix **P**rocrustes **O**rthogonalization for **T**ransformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, reducing significantly the overall optimization cost. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality–compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression. Code will be released upon acceptance.

Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. *We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance.* We propose **COVER** (**C**overage-**O**riented **V**iewpoint curation with **E**RP **R**ange-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build **CM-EVS** (**C**overage-curated **M**etric **E**RP **V**iew **S**et), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth along ERP rays, calibrated world-to-camera pose, and provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage–conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.

Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. The code will be made public.

We present Correlated Latent Uncertainty Estimation (CLUE), a single-pass deep uncertainty quantification framework that combines efficient amortized neural prediction with Bayesian-style information propagation across inputs. CLUE introduces a kernel-based prior, allowing cross-input dependence over latent variables while preserving test-time inference efficiency. CLUE requires neither posterior sampling nor ensembles, and avoids matrix inversion during inference, yet recovers posterior contraction behavior analogous to conventional Bayesian models. In the white-noise limit of the kernel, CLUE reduces to standard evidential deep learning (EDL). This limiting case reveals standard EDL as amortized variational inference with an independent latent structure, providing a probabilistic explanation for several pathologies of EDL identified in prior work. Empirically, CLUE exhibits consistent Bayesian-like uncertainty contraction and improved uncertainty quality across synthetic and real-world benchmarks, while maintaining competitive predictive accuracy and fast single-pass inference.

While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations. To address this limitation, extensive research has focused on enhancing model grounding capabilities through techniques such as Reinforcement Learning. However, rather than compelling agents to adapt to human-centric interfaces, we propose constructing interaction interfaces specifically optimized for agents. This paper introduces Component Interface for Agent (CI4A), a semantic encapsulation mechanism that abstracts the complex interaction logic of UI components into a set of unified tool primitives accessible to agents. We implemented CI4A within Ant Design, an industrial-grade front-end framework, covering 23 categories of commonly used UI components. Furthermore, we developed a hybrid agent featuring an action space that dynamically updates according to the page state, enabling flexible invocation of available CI4A tools. Leveraging the CI4A-integrated Ant Design, we refactored and upgraded the WebArena benchmark to evaluate existing SoTA methods. Experimental results demonstrate that the CI4A-based agent significantly outperforms existing approaches, achieving a new SoTA task success rate of 86.3\%, alongside substantial improvements in execution efficiency.

Spectral token mixers based on Fourier transforms provide an efficient way to model global interactions in visual feature maps. Existing designs often either apply filter-wise spectral responses along fixed channel axes, or learn adaptive frequency-indexed channel mixing without explicitly aligning the channel directions used across frequencies. We propose CHASM, a Cross-frequency Harmonized Axis-Separable Mixer, as a structured middle ground. CHASM separates what should be shared from what should remain frequency-specific: all frequencies share a learned channel eigenbasis, while each frequency retains its own positive spectral gains. The shared basis makes channel directions comparable across the spectrum, whereas the positive gains preserve local spectral adaptivity. CHASM applies this structured operator separably along the height and width axes and is used as a drop-in replacement mixer inside existing backbones. We provide a structural characterization of the shared-basis operator family and evaluate CHASM through controlled same-backbone comparisons. Across accelerated MRI reconstruction, undersampled MRI segmentation, and natural-image reconstruction, CHASM consistently improves over same-backbone spectral-mixer baselines. Ablations show that removing the shared-basis constraint weakens performance, and randomizing coherent sampling geometry substantially reduces the gain, supporting cross-frequency harmonization as a useful inductive bias for spectral token operators.

Accurate counts of fish populations are essential for monitoring ecosystems and making decisions about conservation, harvest limits, and river management. In many field deployments, these counts are obtained from sonar videos that experts must inspect manually, making large-scale monitoring slow, expensive, and difficult to reproduce. Automated counting systems have been proposed as a scalable alternative, but so far this promise has been difficult to realize in deployment. Developing evaluations that better reflect real world deployment conditions will close this gap, and drive improvements to counting methods that translate to reality. To achieve this, we propose Caltech Fish Counting 2026 (CFC26), a new dataset with improved evaluation protocols that simulate deployment to current and new rivers, testing in-distribution and out-of-distribution performance. We expand the CFC22 dataset to eleven deployment locations across nine Pacific salmon river systems from Alaska to Northern California, capturing a broader geographical and ecological range of data. We additionally introduce two new evaluation metrics for counting, nMNE and nMANE, that mirror the operational workflow that experts use and help surface errors that frame-level detection metrics do not capture. Using these metrics, we reveal previously-concealed directional bias in upstream vs downstream counts, which would have direct and significant ecological implications if undetected.

Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution “optical biopsies” of human skin {\em in vivo} by acquiring en-face images at successive depths, forming a sparse $z$-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution ($0.5\mu m$) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning ($3\mu m$), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach CD-RCM: a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to $200 \mu m$. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM’s depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.

Set-level visual difference discovery is increasingly important for dataset auditing and for understanding model behavior under distribution shift, yet manually inspecting thousands of images is impractical. This motivates the emerging task of *set difference captioning*, i.e., describing concepts that are more often true for one image set than another. Different from existing approaches which heavily rely on large language models (LLMs), incurring substantial computational cost in terms of time and tokens, we introduce **CCDiff**, a lightweight, statistically grounded, and training-free framework for set difference captioning that significantly reduces LLM dependence during core difference discovery, achieving over **$2\times$** speedup and reducing token usage to zero. CCDiff operates in three stages: constructing a shared pool of candidate concepts from domain vocabularies or lightweight captioning models, filtering candidates to obtain set-specific concept pools, and performing inverse canonical correlation analysis (CCA) across sets to identify low-correlation directions that correspond to set differences. Candidate concepts are then ranked using a CCDiff score that balances inverse correlation with within-set representativeness. We evaluate CCDiff on VisDiffBench and further validate it on additional domains including MetaShift and MIMIC-CXR. Across benchmarks and real-world settings, CCDiff matches or exceeds prior LLM-based approaches while being substantially more efficient and scalable.

While Speculative Decoding (SD) has become an essential lossless acceleration technique for Large Language Models, its direct application to Multimodal Large Language Models (MLLMs) is hindered by the intricate visual dependencies present in cross-modal generation. Existing SD methods enforce a uniform global alignment between the draft and target models, which proves ineffective in multimodal settings due to a structured distribution bias: deviations of the draft model are systematically concentrated on a sparse subset of tokens that demand fine-grained visual comprehension. To overcome this limitation, we reformulate the training of multimodal draft models as an acceptance-oriented critical-token alignment problem and introduce CATS, a novel two-stage training framework. CATS employs two complementary selection mechanisms to pinpoint critical tokens:hard tokens that exhibit high rejection probabilities, and vision-critical tokens whose prediction fundamentally depends on visual semantics. Following an initial global alignment phase, CATS performs sparse, targeted refinement exclusively on the union of these identified token sets. Extensive experiments across three representative benchmarks using multiple LLaVA and Qwen2.5-VL target models demonstrate that our approach consistently improves the token acceptance rate over strong baselines such as EAGLE-2 and supervised fine-tuning. Ultimately, CATS substantially accelerates MLLM inference, achieving speedups of up to 3.05×.

Many studies argue that the success of sharpness-aware minimization (SAM) is due to the implicit regularization of the gradient norm or the eigenvalues of the Hessian. However, they cannot fully explain why the flatness-based measures do not always correlate well with generalization. For instance, we can easily construct a counterexample by only optimizing with hard examples. In this paper, we propose to resolve such a contradiction from the perspective of anti-overfitting. First, we show that the sharpness at the mini-batch level approximately equals the sum of the gradient norm and the Kullback–Leibler (KL) divergence between the predictive distributions evaluated at the current point and its adversary. Second, we show that the \emph{gradient consistency}, which approximately quantifies the inner product between the mini-batch gradient and the per-example gradient, decreases consistently during training. This result explains why SAM is less likely to suffer from overfitting. At last, building on these insights, we further introduce CASAM, an algorithm that reweights each example according to gradient consistency to enhance the generalization performance. Extensive experiments on popular benchmarks such as ImageNet-1K and Clothing1M corroborate its efficacy.

Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which intermediate SQL decision caused success or failure. Token-level dense supervision is also ill-suited: SQL tokens do not align with complete semantic decisions, can penalize execution-equivalent queries, and are difficult to label reliably at scale. We therefore propose CAPER, which automatically derives clause-level supervision via counterfactual intervention on the SQL abstract syntax tree (AST), enabling root-cause error localization for reward modeling; the resulting data is used to train CAPER-9B, a lightweight Clause-PRM that provides clause-boundary feedback for policy optimization and candidate verification. Experiments on BIRD and Spider show that clause-aligned supervision not only improves execution accuracy, achieving up to a 15.3\% relative EX improvement over GPT-5.4, but also strengthens failure-localization capability, reaching 84.53\% accuracy and 90.60\% MRR on held-out failures.

Finite-shot quantum tomography often leaves a posterior over many density matrices compatible with the same measurement record, but fast reconstruction methods collapse this uncertainty to a single state. We frame this setting as amortized posterior sampling and introduce BuresTomFlow, a measurement-conditioned Flow Matching sampler for full-rank density matrices. The method transports base density matrices along Bures-Wasserstein paths on the positive matrix cone, projects the path back to the trace-one state space, and trains the velocity field with a Bures-aligned tangent loss. Thus the sampler is trained in the same fidelity-based geometry used to judge posterior quality. We evaluate BuresTomFlow in dense four-qubit simulations with count, shadow, and mixed measurement records. Against Cholesky, log-density, and generic Riemannian Flow Matching controls at comparable learned-sampler runtime, BuresTomFlow improves Bures distance, credible-ball coverage, and held-out observable prediction. In the main comparison across multiple seeds, mean Bures distance decreases from $0.618$ for the Cholesky baseline to $0.367$ for BuresTomFlow, and mean coverage error decreases from $0.203$ to $0.100$. These results show that the value of Bures geometry is not merely enforcing physical states, but producing calibrated posterior uncertainty when finite-record tomography cannot be reduced to one reconstruction.

Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. One recent focus is the generalization of Euclidean fully connected (FC) and convolutional layers to non-Euclidean geometries. However, previous approaches typically focus on a few selected manifolds and rely on specific properties of the target manifold. In contrast, this work proposes a framework for constructing FC and convolutional layers over computationally tractable Riemannian spaces. This framework incorporates several previous FC layers across different geometries as special cases and is instantiated on ten representative manifolds, including three hyperbolic models, five geometries of the symmetric positive definite (SPD) manifold, and two Grassmannian perspectives. Experiments on different manifolds demonstrate the effectiveness and applicability of our approach.

A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too difficult to solve exactly for complex systems. Such systems are commonly modeled using simulators, which impose computational approximations. Meanwhile, experimental measurements more faithfully represent the real-world, but experimental data typically consists of observations that only partially reflect the system’s full underlying state. We propose a data-driven distribution alignment framework that bridges this simulation-to-experiment gap by pre-training a generative model on fully observed (but imperfect) simulation data, then aligning it with partial (but real) observations of experimental data. While our method is domain-agnostic, we ground our approach in the physical sciences by introducing Adversarial Distribution Alignment (ADA). This method aligns a generative model of atomic positions—initially trained on a simulated Boltzmann distribution—with the distribution of experimental observations. We prove that our method recovers the target observable distribution, even with multiple, potentially correlated observables. We also empirically validate our framework on synthetic, molecular, and experimental protein data, demonstrating that it can align generative models with diverse observables.

Hybrid large language models (LLMs) integrating state space models (SSMs) and full attention face a critical caching bottleneck due to mismatched caching granularities. While full attention allows flexible, token-level cache reuse, SSMs are always restricted to coarse-grained block- or request-level reuse. Consequently, an SSM layer's cache miss can easily invalidates successful fine-grained hits in adjacent attention layers, disrupting the entire reuse pipeline. To resolve this, chunk-level position-independent cache (PIC) reuse is highly desirable to align the caching granularity across all layers. However, enabling PIC reuse for history-dependent SSMs remains fundamentally challenging. To bridge this gap, we propose HyPIC, the first unified framework achieving PIC reuse for hybrid architectures. We introduce residual state propagation and online decay correction, which efficiently adapt offline SSM chunk caches to new contexts by explicitly recomputing minimal boundary tokens. Furthermore, we design a fast parallel recomputation pipeline exploiting the linear superposition property of SSMs to eliminate sequential bottlenecks. Experiments on diverse hybrid models and datasets demonstrate that HyPIC achieves a 2$\times$ speedup in time-to-first-token (TTFT) while maintaining highly competitive accuracy.

Bridging the gap between simulation and reality remains a fundamental challenge for end-to-end autonomous driving. Existing approaches primarily focus on appearance-level features. This often leads to suboptimal transfer, where visually aligned models still produce inconsistent or unsafe behaviors in the real world. In this paper, we propose a unified domain adaptation framework that jointly aligns perception and decision processes through geometry-aware and vector-space representations. At the perception level, we introduce a Geometry-Aware Perception Alignment (GAPA) module that enforces cross-domain consistency in both explicit geometry and implicit structural representations extracted from latent features. This reduces domain discrepancy by targeting scene structure rather than appearance. At the decision level, we propose a Latent Vector Space Guidance Decision Alignment (LVDA) module that aligns vector-conditioned policy distributions. By modeling driving decisions as conditioned on structured representations of map topology and multi-agent interactions, we minimize discrepancies between source and target action distributions via adversarial and structural alignment. To further enhance stability, we introduce a progressive adversarial transfer strategy that improves cross-domain feature alignment and training stability. Extensive experiments on a newly partitioned nuScenes dataset demonstrate that Bridge-AD achieves excellent performance, effectively narrowing the gap between simulation and real-world autonomous driving.

Generative models are often required to serve many downstream conditional tasks. In the training-free setting, a pretrained unconditional model is reused as a prior and combined with a task-specific likelihood to sample from the corresponding posterior, without retraining the generative model. While such conditional samplers have been developed for diffusion and flow generative models, training-free conditional sampling for Schrödinger bridge (SB) generative models remains unexplored, despite the growing appeal of SBs as flexible stochastic transport models between general source and target distributions. We propose BridgeTwist, a training-free conditional sampler for pretrained SB generative models, formulated as a tempered twisted sequential Monte Carlo scheme. At each step, the sampler uses a closed-form plug-in twist surrogate, which combines the velocity and score fields exposed by SB pretraining into an explicit endpoint predictor. Since this surrogate is less reliable near the source, we use a time-varying temperature to temper the twist, downweighting its influence at early steps. Telescoping importance weights then cancel all intermediate twist factors, yielding an asymptotically exact conditional sampler without any auxiliary twist network or pilot rollouts. Empirically, BridgeTwist consistently outperforms training-free baselines on class-conditional sampling and inpainting on MNIST and CIFAR-10, and on text-to-image generation on CelebA-HQ.