NeurIPS 2026 论文列表

Neural Processes (NPs) are flexible meta-learning models capable of uncertainty quantification across diverse applications. However, the standard NP framework assumes access to noise-free inputs. This is unrealistic in many practical settings, where sensor noise or measurement errors are unavoidable. This discrepancy between model assumptions and real-world data can significantly degrade prediction quality and uncertainty estimates. We introduce Denoising Neural Processes (DnNPs), a principled extension of the NP framework that explicitly models input uncertainty. We train DnNPs via variational inference. DnNPs jointly learn to denoise corrupted inputs and perform meta-learning, making them robust to noisy training data. We demonstrate DnNPs' effectiveness on both synthetic and real-world datasets, showing consistent improvements in prediction and uncertainty quantification compared to standard NPs. On PM$_{2.5}$ spatial interpolation from real-world air quality sensors, DnNPs reduces RMSE by up to 43% and NLL by up to 80% relative to standard NPs. We also provide theoretical grounding: we show that the Bayes optimal predictor under noisy inputs belongs to the DnNPs model class, and that plug-in models with fixed observation variance incur an irreducible KL divergence gap that DnNPs provably avoids.

Distributed online convex optimization (D-OCO) is a powerful paradigm for modeling distributed scenarios with streaming data. However, the communication cost between local learners and the central server is substantial in large-scale applications. To alleviate this bottleneck, we initiate the study of D-OCO with compressed communication. Firstly, to quantify the compression impact, we establish the $\Omega(\delta^{-1/2}\sqrt{T})$ and $\Omega(\delta^{-1}\log{T})$ lower bounds for convex and strongly convex loss functions, respectively, where $\delta \in (0,1]$ is the compression ratio. Secondly, we propose an optimal algorithm, which enjoys regret bounds of $O(\delta^{-1/2}\sqrt{T})$ and $O(\delta^{-1} \log T)$ for convex and strongly convex loss functions, respectively. Our method innovatively incorporates the error feedback mechanism into the Follow-the-Regularized-Leader framework to address the coupling between the compression error and the projection error. Furthermore, we employ the online compression strategy to mitigate the accumulated error arising from the bidirectional compression. Our online method has great generality, and can be extended to the offline stochastic setting via online-to-batch conversion. We establish convergence rates of $O(\delta^{-1/2}T^{-1/2})$ and $O(\delta^{-1} T^{-1})$ for convex and strongly convex loss functions, respectively, providing the first guarantees for distributed non-smooth optimization with compressed communication and domain constraints.

Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can generalize effectively to new downstream tasks. After fine-tuning on only a few trajectories from a target domain, they often achieve strong accuracy in low-data regimes. However, these models are typically large and computationally intensive, limiting their usefulness as fast surrogates for numerical solvers. We propose Teacher Rollout Extension (TREX), a knowledge distillation framework that transfers the predictive capability of a pretrained foundation model into a compact and efficient student. Starting from a fine-tuned teacher, TREX augments limited downstream data by generating long synthetic trajectories through teacher rollouts with periodic noise injection. This procedure samples from the teacher-induced rollout distribution without requiring explicit knowledge of the initial-condition distribution, while exposing the student to long-horizon states and local recovery behavior around states encountered during autoregressive prediction. The student can further incorporate task-specific inductive biases, such as equivariance, that the teacher does not necessarily enforce. We evaluate TREX on multiple PDE benchmarks. The resulting students match or even surpass the teacher’s accuracy while reducing the number of parameters by several orders of magnitude and achieving more than an order-of-magnitude speedup in inference.

The robust delivery of power is a non-negotiable prerequisite for the operational reliability of modern chips. Even a single unanticipated IR-drop violation can cause system-level crashes, making IR-drop a strict criterion in chip signoff. However, current methodologies ranging from physics-based simulators to recent learning-based surrogates remain bottlenecked by an inherent coupling between spatial topology and temporal evolution. This paradigm is intrinsically unscalable when billion-gate spatial complexity converges with the extensive workloads of full-testbench verification. In this work, we reformulate dynamic IR-drop assessment as a disentangled multimodal learning problem and introduce Layout Waveform Fusor (LWFusor), which decouples invariant spatially static chip layouts from transient switching waveforms and fuses them through physics-aligned end-to-end learning. By projecting activity sequences directly into the spatial potential domain, the proposed approach departs from traditional time-stepping bottlenecks and makes long-horizon power integrity analysis computationally efficient. Experiments on industrial-scale designs demonstrate orders-of-magnitude acceleration while maintaining high fidelity in hotspot capture, highlighting a new trajectory for power integrity sign-off in large-scale EDA workflows.

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.

We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach to the XY model, a fundamental continuous-spin model in condensed matter physics, we show that our technique addresses the shortfalls of the Markov chain Monte Carlo (MCMC) in generalization to varying system sizes. More specifically, we show that training a temperature-conditioned diffusion model on smaller-size XY model lattices enables the generation of accurate samples in larger lattice sizes. By tracking physically important observables of the model, such as spin correlations, our experiments demonstrate that diffusion sampling followed by few MCMC steps reduces the thermalization time by an order of magnitude relative to the standard MCMC with random initialization. Our study provides valuable insight as to how generative models can be used to study continuous-state condensed matter systems at scale.

In machine learning applications, privacy requirements during inference or deployment time could change constantly due to varying policies, regulations, or user experience. In this work, we aim to generate a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training steps, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post processing techniques, namely random selection and linear combination, to output final private models satisfying any target privacy parameter. We provide privacy accounting of these approaches from the lens of R\'enyi DP and privacy loss distributions for general problems. In a case study on private mean estimation, we precisely characterize the privacy/utility results and theoretically establish the superiority of linear combination over random selection. Empirically, we validate our approaches and analyses on several models and both synthetic and real-world datasets.

Optimizing functionals over the space of probability measures is now ubiquitous in machine learning. A widely used approach is to perform the optimization directly over the Wasserstein space, but many objective functionals of practical interest are non-convex along Wasserstein geodesics, making the analysis of standard first-order methods challenging. In this work, we study a class of objectives over the Wasserstein space that admit a difference-of-convex (DC) decomposition and we lift the classical convex-concave procedure (CCCP) to this setting. Under smoothness and strong convexity assumptions on the convex components of the decomposition, we prove almost stationarity along the iterates of the resulting algorithm. Our main focus is on the Maximum Mean Discrepancy (MMD) and the Energy Distance (ED) functionals, for which we develop explicit Wasserstein DC decompositions, and establish local convergence of the scheme under mild assumptions. Empirically, we show that well-chosen DC decompositions yield faster and more stable convergence than Wasserstein gradient descent on these MMD objectives.

Compact medical multimodal models are the most realistic path to clinical deployment, yet they often produce fluent answers that are not actually grounded in the image. We show that this failure has a clear internal signature. In five compact medical backbones and across six medical VQA benchmarks, visual tokens lose their spatial diversity shortly after entering the language backbone, their residual updates are an order of magnitude smaller than text residuals, and attention on them freezes on content free regions of the image. Zeroing the visual residual stream barely changes the output, while zeroing the text residual stream destroys it. We give a short analytical account that links the collapse of visual similarity to the residual dominance ratio, and we propose VGrip, a set of three lightweight losses that act on the three failure points without modifying the backbone architecture or the inference procedure. We also introduce the Visual Dependency Score, a simple and benchmark agnostic measure of how much a model actually uses the image. On Qwen2.5-VL-7B, VGrip lifts accuracy by 4.7 points on average across the six benchmarks and more than doubles the Visual Dependency Score over a strong LoRA baseline, with gains that are stable from two to eight billion parameters.

Fine-scale-faithful neural simulation under fixed storage budgets remains challenging. Many existing methods reduce high-frequency error by improving architectures, training objectives, or rollout strategies. However, under budgeted coarsen-quantize-decode pipelines, fine detail can already be lost when the carried state is constructed. In the canonical periodic incompressible Navier--Stokes setting, we show that primitive and derived fields undergo systematically different retained-band distortions under the same operator. Motivated by this observation, we formulate **Derived-Field Optimization (DerivOpt)**, a general state-design framework that chooses which physical fields are carried and how storage budget is allocated across them under a calibrated channel model. Across the full time-dependent forward subset of PDEBench, DerivOpt not only improves pooled mean rollout nRMSE, but also delivers a decisive advantage in fine-scale fidelity over a broad set of strong baselines. More importantly, the gains are already visible at input time, before rollout learning begins. This indicates that the carried state is often the dominant bottleneck under tight storage budgets. These results suggest a broader conclusion: in budgeted neural simulation, carried-state design should be treated as a first-class design axis alongside architecture, loss, and rollout strategy.

Derivative-informed training improves neural operators by directly supervising their input--output sensitivities, but existing methods rely on offline tangent solves and stored derivative labels. We propose sketched tangent consistency loss (sTCL), an on-the-fly drop-in derivative regularizer for neural-operator training that improves learned Jacobians without changing the architecture or generating offline sensitivity data. At each training step, sTCL samples a small number of perturbation directions in the input function space, computes the corresponding surrogate Jacobian--vector products using forward-mode automatic differentiation, and penalizes the residual of the forward sensitivity equation. This equation is obtained by differentiating the governing PDE with respect to the input perturbation direction, so derivative supervision is obtained directly from the physics rather than from stored tangent labels. We further show that sketching makes online derivative-informed training computationally feasible, while conditioning is the key to making it effective. Random directional sketching keeps the derivative penalty at the same order of cost as the standard data-fitting loss, making sTCL compatible with stochastic neural-network training as a lightweight loss term. However, raw forward-sensitivity residual penalties can fail for stiff, ill-conditioned, or indefinite tangent operators. To address this, we introduce lightweight operator-aware preconditioners selected by a simple tangent-operator decision rule. Across four PDE benchmarks---Helmholtz, nonlinear diffusion--reaction, Burgers, and two-dimensional Navier--Stokes---operator-conditioned sTCL achieves accuracy comparable to offline DIFNO while eliminating the offline derivative-data generation stage. These results show that on-the-fly derivative-informed training need not merely amortize offline tangent-solve cost into training; with appropriate sketching and conditioning, sTCL provides an attractive drop-in path to derivative-informed neural operators.

Reinforcement learning (RL) has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments. Existing approaches either rely on training data with ground truth annotations or require advanced proprietary language models (LMs) to synthesize environments that keep fixed once created. In this work, we propose TRUSTEE, a cost-friendly method for training tool calling agents with dynamic environments fully simulated by free open-source LMs that can be as small as 8B, including task generation, user simulation, tool simulation and trajectory evaluation, paired with an adaptive curriculum learning mechanism that controls task difficulty during training. Our empirical results show that TRUSTEE outperforms baselines which require extra external resources in most cases. These confirm that, with a sufficiently sophisticated design, even simulated environments with a local 8B LM as the backbone could set a strong baseline for tool learning. We hope our proposed paradigm could democratize tool learning and inspire future research on environment scaling with limited resources.

Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, and handle user barge-ins. Yet these improvements in conversational dynamics often come with weaker reasoning and instruction-following abilities, revealing a potential tension between interactive dynamics and intelligence capability. In this paper, we argue that such an **intelligence--dynamics** trade-off is not fundamental: conversational dynamics can instead be learned as a separate real-time decision policy from human dialogue data. To this end, we propose DuplexPO, a reinforcement learning (RL) framework that decouples when to speak from what to say. It preserves the semantic response capability of an instruction-tuned assistant, while optimizing its temporal interaction behavior over selected high-impact windows from long human conversations. To quantitatively optimize these dynamics, we formulate the Factorized Conversational Dynamics Reward (FCDR) to enable fine-grained temporal credit assignment for turn initiation, backchanneling, yielding, and regularized participation. The policy is then optimized with a GRPO-style objective. Experiments show that DuplexPO substantially improves full-duplex behaviors, including timely backchannels, smooth turn-taking, and barge-in handling, while maintaining strong reasoning and instruction-following performance. Moreover, improvements in dynamics-oriented metrics are reflected in better user experience, suggesting that optimizing conversational timing as a standalone objective can promote more natural full-duplex interaction.

Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses, however, rely primarily on subspace embedding guarantees, which do *not* precisely characterize the statistical bias of *nonlinear* random oblique projections induced by sampling, which arises ubiquitously in subsampled least squares and fast low-rank approximation methods. Because (pseudo)inversion is *nonlinear*, these random oblique projections can be systematically *biased* even when the underlying sketch is unbiased, thereby introducing hidden bias into downstream least squares and low-rank approximation solutions. In this work, we develop a unified non-asymptotic theory for random oblique projections in high dimensions. We show that standard random sampling schemes generally induce a systematic statistical *bias* overlooked by classical subspace embedding-style analyses, and we propose a principled debiasing framework to correct it. We illustrate the power of the theory through two canonical applications. For subsampled least squares, we obtain sharp bias--variance characterizations, reveal previously unrecognized statistical *suboptimality* in widely used sampling schemes, and identify when debiasing yields provable improvements. For fast CUR decomposition, we develop a debiased approach with improved approximation accuracy. Numerical experiments further validate our theoretical findings.

Dynamic scene representation and rendering in the visible spectrum has been extensively studied. Compared to visible-light imaging, thermal infrared sensing offers all-weather observation and strong penetration capability, enabling perception in low-visibility environments. However, dynamic reconstruction in thermal infrared scenes is challenged due to atmospheric transport effects and interference from low-emissivity background regions, which often leads to motion blur and details loss in rendered results. To address these issues, we propose DTGS, a physics-driven dynamic 3D scene reconstruction approach. DTGS factorizes dynamic thermal scenes into radiometric and geometric components, modeling radiometric changes via rendering that integrates atmospheric radiative transfer, while capturing geometric deformations through Adaptive Temporal Gaussian basis. In addition, we design a thermal-radiance-weighted structural similarity loss to suppress gradient interference from low-radiance noisy regions. Furthermore, to demonstrate the effectiveness of our method, a large-scale dataset for this field named Dynamic\_LTR is created. Experimental results demonstrate that our method achieves a 2.7 dB improvement in PSNR, a twofold speedup in rendering, and a threefold reduction in training time. The code and datasets will be available after acceptance.

Accurate forecasting of Earth's dynamic systems is vital in geophysics, where neural operators offer a powerful data-driven paradigm. Standard operators often treat the Earth as a flat Euclidean plane, introducing geometric artifacts. In contrast, Spherical Neural Operators (SNOs) resolve this mismatch by operating within the spherical spectral space, naturally adapting to spherical geometries. However, for computational efficiency, existing SNOs typically rely on the rotation equivariance assumption. This assumption simplifies the transitions of physical states into isolated spectral modal evolutions, thereby over-idealizing the Earth as a homogeneous system. To solve this problem, we no longer rely on the rotation equivariance assumption that isolates spectral modal evolution, but directly derive an explicit modal interaction kernel and propose Diagonal Spherical Neural Operators named DIAGNO. DIAGNO explicitly parameterizes cross-modal interactions through spectral modal decomposition, thereby preserving the inherent heterogeneity of Earth dynamics. Furthermore, leveraging the unique diagonal structure of the spherical spectrum, we design inter- and intra-diagonal interaction mechanisms to natively capture essential zonal and meridional dynamics, respecting intrinsic geophysical regularities. Extensive experiments across simulated fluid dynamics and real-world Earth system reanalysis (including atmosphere and ocean) datasets demonstrate DIAGNO’s superior capability, consistently achieving state-of-the-art performance in multi-step forecasting. Beyond this, we validate DIAGNO’s practical analytical value in professional geophysical domains by assessing its fidelity in capturing climatological anomalies and maintaining physical consistency.

Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substantial representation gap between state-of-the-art crystal generative models and pretrained universal machine learning interatomic potentials (MLIPs) via energy probing, and show this gap can be closed by a simple training-time alignment. We propose Crystal REPresentation Alignment (CrystalREPA), a plug-and-play framework that aligns the atom-wise hidden states of generative encoders with frozen MLIP representations through an element-aware contrastive objective, transferring stability-aware atomistic priors with marginal training overhead and no additional inference cost. Across three generative frameworks, ten MLIP teachers, and two benchmark datasets, CrystalREPA consistently improves the thermodynamic stability, structural validity, and structural fidelity of generated crystals. Equally important, we find that an MLIP's transfer effectiveness is poorly predicted by its accuracy on standard leaderboards (e.g., Matbench Discovery) but strongly predicted by the distinguishability of its atom-wise representation space, yielding a practical, accuracy-independent criterion for selecting MLIP teachers for generative transfer.

Deep learning has accelerated automated atomic model building from cryo-EM maps, yet progress remains limited by the lack of large, high-quality datasets and standardized evaluation benchmarks. We address this gap by introducing CryoAtlas, a rigorously curated dataset and unified benchmark for density-guided atomic model building. By integrating and filtering EMDB and PDB entries (up to March 2026), CryoAtlas provides 17,809 map--model--sequence entries, including a curated 16,657-entry training split. From this resource, we construct a 618-target benchmark with a shared evaluation protocol that jointly assesses structural accuracy, model-to-map fit, and stereochemical validity. Using this framework, we benchmark state-of-the-art learning-based methods against Phenix and AlphaFold~3, comprehensively analyzing the impact of map resolution, sequence length, protein category and sequence identity on modeling accuracy. As a case study, retraining the ModelAngelo CNN component on CryoAtlas improves performance on the benchmark. CryoAtlas provides an open, reproducible foundation for future cryo-EM atomic model building research.

We investigate the problem of learning neural operators for coupled multi-physics PDEs, where multiple physical processes co-evolve and influence one another via bidirectional coupling. For example, in subsurface CO$_2$ storage, fluid flow, heat transfer, and geo-mechanical deformation evolve in a tightly coupled manner. Prior work, including neural operators and generative PDE solvers, mostly focuses on single-physics settings or relies on iterative inference, limiting their effectiveness in coupled PDEs. This paper introduces coupled-flow, a one-step generative neural operator that revisits coupled operator learning as solving a distributional transport problem. It parameterizes process-specific average velocity fields conditioned on the full coupled state, enabling cross-process interactions to emerge naturally from the transport dynamics. We further establish a Wasserstein-2 generalization bound that ties the prediction error of coupled-flow to its training losses through the regularity of the induced flow. Experiments on a wide variety of multi-physics benchmarks demonstrate that coupled-flow achieves significant performance improvements over coupled neural-operator and generative PDE baselines.