NeurIPS 2026 论文列表

Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent stored updates from clients, even when they are absent from the current training round, thereby emulating their presence. Furthermore, we propose FedAdaVR-Quant, which stores client updates in quantised form, significantly reducing the memory requirements (by 50%, 75%, and 87.5%) of FedAdaVR while maintaining highly competitive model performance. We analyse the convergence behaviour of FedAdaVR under general nonconvex conditions and prove that our proposed algorithm can eliminate partial client participation error. Extensive experiments conducted on multiple datasets, under both independent and identically distributed (IID) and non-IID settings, demonstrate that FedAdaVR consistently outperforms state-of-the-art baseline methods.

Federated Multi-view Clustering (FMVC) enables privacy-preserving cross-view fusion from distributed multi-view data, where each client holds one view of samples and participates in the server's fusion without exposing raw data. In practice, existing FMVC methods face two key challenges: (1) the collected samples often show unaligned correspondence across views, especially under distributed collection; and (2) the gap between higher data privacy and lower computation cost remains hard to reconcile. To address these issues, we propose an Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering (**Fed-AGA**), which employs a coarse-to-fine alignment strategy on lightweight anchor graphs to achieve misalignment-robust cross-view fusion while bridging the gap between data privacy and computation cost. Specifically, in the coarse alignment stage, we introduce an anchor-based graph generation module to generate an anchor graph on each client and upload them to the server for category-wise alignment by a designed topology-aware alignment module, which distills topology components as category-exclusive signatures to guide and achieve ideal category-wise alignment. In the next fine alignment and fusion stage, we design an attention-based alignment module to encourage each sample to pay attention to highly similar samples for sample-wise alignment, and the fine-aligned results are contrastively fused into a cross-view consistent anchor graph for clustering. Extensive experiments on multiple datasets demonstrate that Fed-AGA achieves state-of-the-art performance among related methods, while theoretical analysis offers theoretical motivation for two key components.

Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail. Yet, this intuition is mostly empirical and qualitative. We introduce \emph{feature information dynamics}, an information-theoretic framework for localizing when a feature is generated during diffusion. Using the I-MMSE identity, we connect the rate of feature mutual information change to a gap between optimal unconditional and feature-conditional denoising losses, yielding practical estimators for feature information density. We further develop a chained decomposition that separates shared from incremental information in a feature hierarchy. We use this framework first to quantitatively confirm spectral autoregression in pixel diffusion, and then to extend the analysis beyond frequency: under a class$\to$mask$\to$Canny conditioning chain, the per-feature information densities differ across pixel, SDVAE, VAVAE, and RAE, exposing fundamental differences between these representations.

Can a large language model be \emph{trained} to tolerate silent hardware faults, and if so, \emph{where} in the networkshould the consistency signal be enforced? We answer both questions and find that the second one matters more than the first. Injecting tile-level GEMM faults during training and adding a KL consistency term on the output distribution reduces fault-induced perplexity degradation from $12.9\%$ to $0.21\%$ on GPT-2 Small---a $60\times$ reduction (two-seed mean). The placement of the consistency target is decisive: a direct hidden-only objective at the fault site can worsen degradation to $32.7\%$, and fair bounded hidden controls with corrupted CE improve substantially but still trail output KL in an 80k-step screen ($1.74$--$1.76\%$ vs.\ $0.84\%$). A conditional constraint hierarchy and gradient-alignment probes explain why output-level consistency is more permissive and easier to optimize than direct early hidden-state consistency, without requiring that every hidden-alignment formulation fail. The output-over-hidden advantage persists at 8B scale, the GPT-2 ordering reproduces on held-out corpora, and generic robustness methods---SAM, R-Drop, and SAF---fail to close the gap. Pairing the trained model with simulated algorithm-based fault detection yields a bit-flip-to-erasure interface with a $1.10\times$ perplexity ratio, where unprotected inference produces NaN.

In this paper, we study federated optimization for solving stochastic variational inequalities (VIs), a problem that has attracted growing attention in recent years. Despite substantial progress, a significant gap remains between existing convergence rates and the state-of-the-art bounds known for federated convex optimization. In this work, we address this limitation by establishing a series of improved convergence rates. First, we show that, for general smooth and monotone variational inequalities, the classical Local Extra SGD algorithm admits tighter guarantees under a refined analysis. Next, we identify an inherent limitation of Local Extra SGD, which can lead to excessive client drift. Motivated by this observation, we propose a new algorithm, the Local Inexact Proximal Point Algorithm with Extra Step (LIPPAX), and show that it mitigates client drift and achieves improved guarantees in several regimes, including bounded Hessian, bounded operator, and low-variance settings. Finally, we extend our results to federated composite variational inequalities and establish improved convergence guarantees.

We study clustering algorithm for dynamic evolving graphs $\{ G_t\}$, in which new edges (and potentially new vertices) are added into a graph, and the underlying cluster structure of the graph gradually changes over time. By designing a hierarchical data structure for dynamically maintaining the cluster structure of $\{ G_t\}$, we prove that the cluster structure of $G_T$ can be maintained with $O(d)$ amortised update time and $\widetilde{O}\left(n_T^{1/d}\right)$ amortised query time, for an arbitrary large constant $d$. This significantly improves the previous state-of-the-art (Laenen and Sun, ICML~'24), which requires $O(1)$ update time and $O(n_T/\log n_T)$ amortised query time. We further demonstrate this improvement with experiments on both synthetic and real-world datasets.

Diffusion large language models promise parallel token generation, yet inference remains bottlenecked by deciding which masked tokens can be safely committed together. Fast-dLLM addressed this with KV caching and confidence-guided parallel decoding, but its decoding theory uses a homogeneous high-confidence assumption that effectively reduces each candidate set to its weakest selected token. We argue that this leaves speed on the table because real decoding steps exhibit heterogeneous confidence profiles. We propose **Fast-dLLM++**, a training-free extension that introduces *Fr\'{e}chet profile decoding*: selecting parallel commit sets from the full sorted confidence profile rather than a single worst-case confidence. The resulting rule is a heterogeneous-confidence generalization of Fast-dLLM's factor selector and it recovers the previous rule exactly in the equal-confidence case and adds a provable *heterogeneity bonus* when the selected tokens have uneven confidences. Fast-dLLM++ leaves the model, diffusion process, and cache implementation entirely unchanged, making it a drop-in replacement for existing Fast-dLLM decoding. Experiments on GSM8K, MATH, HumanEval, and MBPP with the LLaDA-8B model show that the theoretical improvement translates directly into empirical gains: profile-aware selection improves the accuracy-throughput frontier by exploiting safe parallelism that weakest-token rules miss, achieving up to 37\% higher throughput at comparable accuracy. Our anonymous code release is at https://anonymous.4open.science/r/fast-dllm-plus-plus/.

3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream forecasting and planning. Existing methods usually exhibit different accuracy-latency characteristics. Performance-oriented models provide stronger geometric and semantic predictions, while deployment-friendly models provide faster responses. This motivates EvoOcc, a fast-slow evolutionary occupancy prediction framework that jointly exploits their complementary strengths to improve the accuracy-latency balance without changing the upstream architectures. EvoOcc models evolutionary occupancy prediction as a continuous time controlled dynamical system, enabling flexible adaptation to irregular prediction arrival of both fast and slow system. The framework further decomposes the evolution process into dual state evolution where ego state evolution for the deterministic coordinate shifts and scene state evolution for the genuine geometric and semantic scene changes. Extensive experiments on three different fast-slow system pairs and irregular prediction arrivals show that EvoOcc consistently improves over fast system baselines while maintaining much lower latency than the slow system, indicating a favorable accuracy-latency balance and stable behavior under practical inference situations.

Linear attention has emerged as a cornerstone for efficient long-context architectures, as evidenced by its integration into state-of-the-art open-source models including Qwen3.5/3.6, Kimi Linear, and RWKV-7. Models that incorporate linear attention layers with the so-called Delta-Rule involve the inversion of triangular matrices as a core sub-routine. This operation often forms a performance bottleneck, and, due to its high-sensitivity to numerical errors, it can significantly deteriorate end-to-end model accuracy if it is not carefully implemented. This work provides a systematic analysis of both direct and iterative triangular inversion algorithms, targeting methods that are rich in matrix products, and, therefore, have the potential to efficiently utilize modern hardware. To that end, our analysis covers a broad spectrum of mathematical and practical aspects, with a heavy focus on numerical stability, computational complexity, and, ultimately, hardware efficiency and practical considerations. We provide a rigorous experimental evaluation to verify these properties in practical scenarios, and in low-precision floating-point representations, highlighting the strengths and limitations of each method. Performance benchmarks on NPUs reveal up to $4.3\times$ speed-up against the state-of-the-art implementations of SGLang for triangular matrix inversion, leading to significant performance improvements on the entire layer level, while maintaining full end-to-end model accuracy.

Structure learning for sparse graphical models in high dimensions requires both computational scalability and selection consistency. Classical criteria such as (Extended) Bayesian Information Criterion can provide consistency, but rely on model-specific likelihood and complexity calibration. In comparison, cross-validation avoids such analytic calibration and is therefore attractive for graph learning. However, cross-validation is computationally infeasible and theoretically inconsistent in structure learning. In this work, we resolve the tension by developing a scalable graph-recovery procedure built on approximate cross-validation. We demonstrate its effectiveness in learning large graphical models both theoretically and empirically, and establish selection consistency under mild signal strength assumptions.

The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional generative evaluation paradigm prohibitively expensive, therefore making the latency of LLM's in-training downstream performance evaluation unbearable. However, simple metrics like training loss (perplexity) are not always correlated with downstream performance, as sometimes their trends diverge from the actual task outcomes. This dilemma calls for a method that is computationally efficient and sufficiently accurate in measuring model capabilities. To address this challenge, we introduce a new in-training evaluation paradigm that uses a lightweight probe for monitoring downstream performance. The probes take the internal representations of LLM checkpoints (during training) as input and directly predict the checkpoint's performance on downstream tasks measured by \emph{success probability} (i.e., pass@1). We design several probe architectures, validating their effectiveness using the OLMo3-7B's checkpoints across a diverse set of downstream tasks. The probes can accurately predict a checkpoint's performance (with avg. AUROC$>$0.75), have decent generalizability across checkpoints (earlier predicts later), and reduce the computation latency from $\sim$1 hr (using conventional generative evaluation method) to $\sim$3 min. In sum, this work presents a practical and scalable in-training downstream evaluation paradigm, enabling a more agile, informed, and efficient LLM development process.

Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids. Traditional CSP is accurate but relies on exhaustive search, costing several CPU-years per molecule. Generative models such as OXtal dramatically reduce this cost by sampling stable organic crystal structures directly. However, OXtal forgoes explicit lattice parametrization in favour of modelling large crops of the bulk material with expensive triangle layers, which can incur a computational cost of minutes per molecule. In this paper, we reduce this to seconds with Clari, a large-scale flow matching model that generates redundancy-free unit cells and replaces triangle layers with pure pair-bias attention. Clari requires only atom types and bonds as input and does not need an RDKit-sanitizable input molecule, which expands its applicability to challenging chemistries such as fullerenes, metal complexes, and atom clusters. We further ablate key design choices such as auxiliary losses, timestep distributions, noise priors, and self-conditioning. Because Clari also models explicit hydrogens, it supports inference-time scaling via direct energy ranking, without any decoration or relaxation step. On OXtal's aggregated test set, we generate 1000 crystals and select the best 30 ranked by energy, surpassing OXtal's solve rate while obtaining a speedup of $5$-$8\times$. We also introduce a new test split of diverse and complex molecules for future benchmarking. Our contributions enable CSP within seconds, making large-scale virtual screening of organic solids practical.

In physics-informed machine learning, a target function $u^* $ is learned from noisy value observations $y_i=u^* (x_i)+ \varepsilon_i$, together with differential information, given either by noisy observations $d_j=(Du^* )(z_j)+\xi_j$ or by a known physical constraint $Du^* =v$ . We consider the setting where $D$ is a linear differential operator and analyze a physics-informed kernel estimator $\hat u$ combining $n$ value observations and $m$ differential observations. In this context, we ask how much can differential information improve predictions, and how does this improvement depend quantitatively on $n$ , $m$, and $D$. We prove finite-sample bounds, supported by numerical simulations, revealing a two-regime structure for the prediction error. When $m$ is limited, the rate depends jointly on $n$ and $m$; when $m$ exceeds a problem-dependent threshold, the rate saturates and matches the oracle rate obtained when the perfect constraint $D \hat u = Du^* $ is imposed. Examples are discussed for Sobolev spaces which are reproducing kernel Hilbert spaces and include partial Laplacian constraints on the torus and gradient observations on bounded domains. These examples illustrate the range of possible learning rate improvements - from the standard nonparametric $n^{-1/4}$ to the parametric rate $n^{-1/2}$. Finally, we derive physically consistent rates in a stronger norm that jointly controls the errors in $\hat u$ and $D\hat u$.

Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off between performance degradation and computational overhead. We propose a novel gating-based KV cache eviction method for frozen-weight LLMs that achieves high compression ratios with negligible computational cost. Our approach introduces lightweight sink-attention gating modules to identify and retain critical KV pairs, and integrates seamlessly into both the prefill and decoding stages. The proposed gate training algorithm relies on forward passes of an LLM, avoiding expensive backpropagation, while achieving strong task generalization through a task-agnostic reconstruction objective. Extensive experiments across the Qwen2.5-1M, Qwen3, and Gemma3 families show that our method maintains near-lossless performance while evicting up to 70\% of the KV cache. The results are consistent across a wide range of tasks, including long-context understanding, code comprehension, and mathematical reasoning, demonstrating the generality of our approach.

In multiclass softmax cross-entropy, the full generalized Gauss-Newton (GGN) curvature couples all output logits through the softmax covariance, making second-order updates harder to scale as the number of classes grows. We show that the standard multiclass GGN can be decomposed exactly into a rank-one true-vs-rest term and a positive semidefinite within-competitor covariance term. Fast Gauss-Newton (FGN) retains the first term and drops the second, yielding a positive semidefinite under-approximation of the multiclass GGN that is exact for binary classification. The derivation uses an exact true-vs-rest scalar-margin representation of softmax cross-entropy: the loss and gradient are unchanged, and the approximation enters only at the curvature level. Exploiting the FGN curvature structure, the damped update can be written as an equivalent whitened row-space system with one row per mini-batch example. We solve this system matrix-free by conjugate gradient using Jacobian-vector and vector-Jacobian products of the scalar margin map. Targeted mechanism experiments and a fixed-feature head evaluation support the predictions from the decomposition: FGN stays closest to the full softmax GGN when competitor mass is concentrated or damping is large, and deviates as the dropped within-competitor covariance grows.

For two $d$-dimensional point sets $A,B$ of size $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A,B)=\sum_{a \in A} \min_{b \in B} \mathsf{dist}(a, b)$, where $\mathsf{dist}$ is some underlying distance measure. We present efficient algorithms for approximating the Chamfer distance under the $\ell_p$ norm for $1 < p \le 2$. For the general case $1 < p < 2$, we utilize lopsided $\ell_p$ to $\ell_1$ embeddings with weak guarantees. We show that they are sufficient for preserving the Chamfer distance. In the high-dimensional regime, we use Fast Matrix Multiplication techniques to speed up the lopsided embeddings. These lead to $\mathcal{O}(nd\cdot \mathrm{poly}(\log \log n, \log(1/\varepsilon))/\varepsilon^2)$ total time. For the specific Euclidean case ($p=2$), we leverage the Fast Johnson-Lindenstrauss Transform based on Toeplitz matrices and re-analyze the previous $\ell_1$-specific Chamfer algorithm in $\ell_2$. These achieve a runtime matching the recent state-of-the-art $\ell_1$ result of $\mathcal{O}(nd(\log \log n + \log(1/\varepsilon))/\varepsilon^2)$, improving upon the previous $\mathcal{O}(nd \log n/\varepsilon^2)$ bound for $\ell_2$. We also give additional results for the angular distance and upper and lower bounds in the streaming setting.

The Procrustes-Wasserstein problem asks to match the vectors in two unlabeled point clouds $X$ and $Y$, which are related via a latent orthogonal transformation. This problem appears in unsupervised representation alignment, domain adaptation, and geometric matching, but its computational theory remains limited. We study a planted Gaussian model in which $Y$ is obtained from $X$ via an unknown relabeling, an unknown orthogonal transformation, and additive noise. Departing from the isotropic setting considered in prior work, we assume that the covariance of $X$ is anisotropic, with a power-law spectrum. While closer to real-world data, this structure also enables a directions-first approach: Estimate the dominant eigenspaces of both point clouds, then recover the matching. We prove that a polynomial-time version of this procedure exactly recovers the true relabeling with high probability under explicit scaling conditions. To our knowledge, this is the first computational recovery guarantee for Procrustes-Wasserstein with nontrivial observational noise and dimensions beyond $\log n$, where $n$ is the number of points. Experiments on synthetic data and 3D shapes support the theory, showing that anisotropy favors directions-first methods, can lower runtime and improve recovery.

Diverse applications on signed networks, ranging from leader-follower opinion dynamics to graph semi-supervised learning, fundamentally reduce to solving the discrete Dirichlet boundary value problem. The computational bottleneck of this problem lies in evaluating the entries of the label propagation operator $Q = -L_{U,U}^{-1}L_{U,L}$. While existing approximation methods based on absorbing random walks attempt to address this complexity, they often suffer from high variance and limited precision. To overcome these limitations, we propose MultiTreeQ, a novel and efficient framework designed to approximate the operator's entries with high accuracy. Specifically, MultiTreeQ is constructed based on the Signed Multi-Rooted Spanning Tree and leverages Rao-Blackwellized variance reduction to significantly improve estimation precision. Extensive experiments on real-world networks confirm that MultiTreeQ substantially outperforms existing baselines, scaling to massive graphs with up to 10 million nodes while delivering remarkable improvements in computational efficiency and precision. Our code is available at https://anonymous.4open.science/r/label-propagation-operator-2059/.

In quantum systems, physical quantities of interest can often be expressed as expectation values of a probability distribution, which allows their estimation by Markov Chain Monte Carlo (MCMC) methods. This approach, an instance of Quantum Monte Carlo (QMC), plays a central role in obtaining predictions of system properties relevant to high energy or condensed matter physics. The use of Hamiltonian dynamics to design a Markov kernel, known as Hybrid or Hamiltonian Monte Carlo (HMC), is widely used in quantum applications. In this context, it is standard to use an HMC kernel that is ``adjusted'' with the Metropolis-Hastings (MH) criterion, so that estimates of expectations converge to their true value in the limit of infinite sample size. However, recent work in computational statistics suggests that an \emph{unadjusted} HMC kernel can be significantly more efficient, while maintaining an asymptotic bias which is small relative to the error arising from the variance of the finite sample size. We adapt this approach to the QMC setting, focusing on a challenging and high-dimensional \emph{spin-fermion} model of a quantum phase transition. We find that unadjusted methods outperform adjusted HMC in terms of effective sample size per gradient call by around an order of magnitude, while retaining accurate results.