NeurIPS 2026 论文列表

For graph node classification, calibrated class probabilities are need when confidence scores, usually the maximum predicted class probability, are used to rank prediction, defer uncertain nodes to human review or control risk. Existing post-hoc calibrators either apply one global temperature or use graph-awared modules without a derived strctural form. We study node-level calibration for graph node-classification problem under structural heterogeneity. Our first results show that a logit-only temperature rule is insufficient when nodes with identical logits but different local homophily require different optimal temperatures. We then analyze a population-concentration contextual stochastic block model with Gaussian features and a one-layer linear GCN. Under equidistant class means, the Bayes posterior over class-template scores is a temperature-scaled softmax whose inverse temperature is governed by homophily-dependent signal strength. In the positive-signal homophilic regime, the resulting temperature decreases approximately with normalized local homophily. This law motivates Homophily-aware Temperature Scaling (HoTS), a simple post-hoc calibrator that assigns each node a positive scalar temperature from entropy-based logit concentration and estimated local homophily. HoTS has three parameters, preserving class, and learn the strength of the structural correction from calibraion data. Across 18 node-classification benchmarks, two GNN backbones, and eight calibration baselines, HoTS achieves the best mean Expected Calibration Error (ECE) of $4.66\%$, the best average rank, and the strongest confidence ranking in selective classification.

Deployed approaches for AI text detection often rely on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable to three types of distribution shifts that occur continually post-deployment, and for which labeled data is often unavailable: adversarial humanization, new LLMs being released, and temporal drift in human writing. Simultaneously, existing approaches do not leverage a key signal of LLM usage: inference-time homogeneity. We propose a test-time adaptation (TTA) approach, using semi-supervised learning, that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time. Empirically, we find that state-of-the-art supervised detectors systematically fail when they encounter distribution shifts in AI-generated and human writing, both adversarial and natural, while test-time adaptation with semi-supervised learning is largely robust; e.g., the commercial model Pangram detects just 24.1\% of our adversarial AI-generated text, compared to 90.5\% for our test-time approach. We establish that test-time adaptation is a promising framework for AI text detection in the wild.

We present a new operator-theoretic representation learning framework for offline reinforcement learning that recovers the directed temporal geometry of a controlled Markov process from hitting time observations. While prior art often produces symmetric distances or fails to satisfy the triangle inequality, our framework learns a Hilbert-space displacement geometry where expected hitting times are realized as linear functionals of latent displacements. We prove that this representation exists under latent linear closure and is uniquely identifiable up to a bounded linear isomorphism. For finite-dimensional implementations, we show that global hitting-time error is bounded by one-step transition error amplified by the environment's transient spectral radius. Furthermore, we provide finite-sample guarantees accounting for approximation, statistical complexity, and trajectory-label mismatch. Derived from this theory, we curate Isomorphic Embedding Learning (IEL) as a new goal-agnostic foundation policy learning algorithm that anchors a HILP-style consistency objective with explicit hitting-time regression to ensure that the learned geometry reflects actual decision-time progress. This asymmetric and compositional structure enables robust graph-based multi-stage planning for long-horizon navigation. Our experiments demonstrate that IEL improves the state of the art of learning foundation policy policies from offline maze locomotion data.

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

Existing conformal prediction methods for time-to-event outcomes leverage only baseline covariates, producing prediction intervals that are insufficiently informative to facilitate decision making. We propose History-Aware Prediction Sets (HAPS), a conformal framework that constructs prediction sets for individual event times using covariate histories observed up to a decision time, targeting coverage among individuals who have survived to this time. HAPS handles right censoring adjusted for time-varying confounders via inverse probability of censoring weighting. When the censoring weights are consistently estimated, it achieves PAAC (probably asymptotically approximately correct) coverage among survivors. We further propose two doubly robust extensions of HAPS to weaken reliance on consistent estimation of the censoring distribution. In simulations, HAPS and its extensions reduce median prediction interval length by up to 75\% relative to baseline comparators while maintaining close to nominal coverage. On two public benchmark data sets, HAPS reduces the median interval length by up to 60\% for predictions at year 5, compared to the baseline comparators.

The deployment of large language models (LLMs) in hiring pipelines is growing, yet existing evaluations generally overlook an important aspect of this development: the compliance of LLMs with applicable legal rules. To fill this gap, we introduce HireWatch, a benchmark that assesses whether LLM hiring assistants comply with U.S. employment discrimination law (e.g., the Civil Rights Act of 1964). We construct a 200-item bank consisting of interview questions that are either lawful or unlawful under U.S. federal statute, regulation, and enforcement guidance. We evaluate the propensity of 18 proprietary and open-source LLMs to select lawful vs. unlawful questions from this bank across ~100,000 trials under a novel seven-tier contextual escalation ladder, crossing occupational details with organizational pressure. Our findings reveal that LLMs' compliance with employment discrimination law is brittle. While LLMs ordinarily comply with legal rules under different job contexts, they readily violate the law when subject to institutional pressure (e.g., an unlawful manager instruction or company policy). Meanwhile, although the inclusion of explicit legal reminders in prompts can improve legal compliance, it does not altogether eliminate violations of employment discrimination law.

Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5--8 more tokens than necessary, applying verbose reasoning uniformly regardless of problem difficulty. We propose Hint Tuning, a data-efficient approach that teaches models to calibrate reasoning depth. Our key insight: the corresponding instruct model serves as an ideal difficulty probe. By testing what the instruct model can solve with varying guidance, we automatically construct training data across three states: No-Hint (direct answer), Sparse-Hint (minimal prefix), and Full-Hint (complete reasoning). This converts the abstract challenge of difficulty labeling into a measurable consistency check between the instruct and reasoning models. With only 1K self-annotated samples, Hint Tuning achieves 24--66% token reduction (31.5% average) across mainstream reasoning models (Qwen3-Thinking, DeepSeek-R1-Distill) at multiple scales (4B--32B) while maintaining competitive accuracy on five benchmarks. Unlike methods requiring massive distillation datasets or expensive RL, we achieve superior efficiency through simple alignment with the instruct model's capabilities.

High-fidelity 3D generative modeling underpins game asset creation, medical imaging, and VR/AR environmental design, where geometric structure directly influences perception and decision-making. Diffusion models represent a powerful paradigm for point cloud generation, yet existing approaches rely heavily on local voxel or patch features and often miss global structural organization. Recent topology-aware methods show that global conditioning can improve generation quality, yet persistent-homology pipelines add substantial preprocessing complexity. We introduce HilbertGen-3D, a topology-aware conditional diffusion framework based on Hilbert–Multifractal Conditioning (HMC). HMC computes multi-scale voxel measures, serializes them via a 3D Hilbert curve to expose structured spatial dependencies, and estimates the generalized fractal dimensions from scale-dependent partition functions. We encode these signals as global embeddings and multi-scale tokens, and inject them into a diffusion denoiser through an efficient bottleneck fusion module. On ShapeNet dataset, HilbertGen-3D reduces the 1-Nearest Neighbor Accuracy by up to 8.74 points and improves coverage by up to +21.45 over prior topology-aware methods. This shows that combining local density cues with global Hilbert–multifractal structure provides a simple, efficient, and effective recipe for high-fidelity and diverse 3D generation.

Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, these methods often suffer from serious control instability and robustness issues when applied in real-world applications such as robotics and autonomous driving, posing notable challenges for their practical deployment. We argue that this instability issue stems largely from their limitations on solely supervising and optimizing zeroth-order actions (i.e., the action labels), failing to account for higher-order action dynamics and temporal consistency. In this paper, we show that simultaneously supervising both zeroth- and first-order actions can dramatically enhance policies' performance and control robustness. To achieve this, we introduce a novel and elegant loss scheme supported by formal theoretical guarantees that can equip any off-the-shelf policy model (e.g., deterministic, stochastic, or flow policies) with the capability for higher-order action supervision, without requiring any structural modifications. Moreover, our proposed method can serve as a lightweight plug-and-play module that seamlessly integrates with a broad spectrum of existing offline RL frameworks. Extensive evaluations on OGBench and D4RL demonstrate that our approach yields substantial performance and robustness improvements across a wide range of continuous control environments. Notably, our method can also enhance policies' out-of-distribution (OOD) generalization capability in the challenging low-data regime, making it an ideal tool in tackling many real-world control problems.

Understanding the behavior of stochastic gradient methods is a central problem in modern machine learning. Recent work has highlighted diagonal linear networks as a simplified yet expressive setting for analyzing the optimization and generalization properties of neural models. In this work, we show that in the high-dimensional regime, stochastic gradient descent on diagonal linear networks is well-approximated by continuous dynamics governed by a stochastic differential equation (SDE), which explicitly decouples the drift from the gradient noise. We further derive a deterministic partial differential equation whose solution propagates the relevant state of the iterates and characterizes the time evolution of a broad class of observable statistics, including the risk, curvature, and other metrics for optimality. To the best of our knowledge, this is the first deterministic high-dimensional description of SGD for a genuinely nonlinearly parametrized model. Finally, we show that, under a suitable parametrization, the stochastic dynamics are globally well posed and converge exponentially fast to zero risk with high probability, yielding a fully explicit non-asymptotic description of their long-time behavior. Numerical simulations corroborate our theoretical findings.

Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied. We develop a direct, data-adaptive test statistic for assessing conditional independence in the graph structure of stationary Gaussian time series. We establish a finite-sample, Berry--Esseen type Gaussian approximation bound for the statistic, which applies to both short-memory and long-memory time series. The testing procedure is fully data-adaptive using block bootstrap method, on which we provide a finite-sample validity result including in the ultra-high-dimensional scenario, and can be extended to comparing graphical structures in two-sample tests. We also develop a consistency-empowered correction to the statistic and show that such tests attain asymptotic consistency in both size and power. Our proposed method is applied to a real-world fMRI data to understand functional connectivities within brain in different periods.

Recently, a series of works have started studying variations of concepts from learning theory for product spaces, which can be collected under the name high-arity learning theory. In this work, we consider a high-arity variant of sample compression schemes and we prove that the existence of a high-arity sample compression scheme of non-trivial quality implies high-arity PAC learnability.

High-dimensional robotic reinforcement learning (RL) is often bottlenecked by inefficient exploration in large, redundant actuator spaces. We introduce Devyn (Developing synergies), a lightweight framework that learns a structured low-dimensional action parameterization directly during policy optimization. Unlike methods that require precomputed synergies or structured exploration in the full action space, Devyn starts from a randomly initialized latent-to-action map and shapes it online through task interaction. The policy acts strictly in a reduced latent space, while a learnable synergy matrix decodes latent actions into actuator commands. To make this evolving decoder stable and useful for policy learning, Devyn combines slow decoder evolution with structural regularization, reducing latent-action semantic drift while encouraging compact coordination patterns. Across diverse continuous-control benchmarks, including high-dimensional humanoid and overactuated musculoskeletal systems, Devyn improves upon standard SAC and achieves competitive or better performance than recent methods designed for high-dimensional robotic control. Ablations show that the gains do not come from a generic latent-action bottleneck alone, but rather from the slow, structured evolution of the synergy matrix. Our results suggest that learning the action parameterization itself can be a simple and effective route to scaling RL for high-dimensional robots.

Conditional independence (CI) testing is a fundamental problem in statistics. However, classical nonparametric CI tests suffer from the curse of dimensionality, as nonparametric estimation degrades rapidly in high dimensions. This paper proposes RP-CIT, a random-projection-aggregated CI test that remains effective in the high-dimensional regime. The central idea is to project $X$ and $Y$ onto random one-dimensional subspaces before applying a univariate base CI test. This projection (i) preserves conditional independence under the null hypothesis and (ii) reduces the original high-dimensional problem to a collection of univariate CI subproblems, thereby avoiding high-dimensional nonparametric estimation. Since a single projection may miss the dependence signal, we aggregate the $p$-values from $k$ independent projections via a Bonferroni-max rule, which asymptotically controls the Type~I error at the nominal level and yields a limiting Type~II error bound that decreases exponentially in $k$ under a detectable-projection condition. The paper also develops a top-$r$ aggregation extension for alternatives whose signal is spread across multiple projection directions. More broadly, the random-projection aggregation framework can be combined with other scalar base tests; as one example, we use a projected GCM base test to accommodate higher-dimensional conditioning variables. Experiments on synthetic benchmarks, image-valued CI tasks, and Norman Perturb-seq module skeleton learning illustrate the calibration, power, and practical utility of the framework.

The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and then synchronize across workers. Additionally, recent work suggests that the one-worker version of one of these algorithms, DiLoCo, shows promising results as a (synchronous) optimizer. Motivated by these studies we present an analysis of LA-DiLoCo, a simple member of the DiLoCo family, on a high-dimensional linear regression problem. We show that the one-worker variant, LA, provides a different tradeoff between signal and noise than SGD, which is beneficial in many scenarios. We also show that the multi-worker version generates more noise than the single worker version, but that this additional noise generation can be ameliorated by appropriate choice of hyperparameters. We conclude with an analysis of SLA --- LA with momentum --- and show that stacking two momentum operators gives an opportunity for acceleration via a non-linear transformation of the ``effective'' Hessian spectrum, which is maximized for Nesterov momentum. Altogether our results show that two-phase optimizers represent a fruitful new paradigm for understanding and improving training algorithms.

We study risk control for online policy learning, where an agent adaptively collects data and aims to learn a new policy where the risk is below a tolerance threshold at every round with a high probability. Such policy updates and data collection induce feedback covariate shift (FCS): the observed data depend on the history through the evolving policies and data-collection process. FCS brings two substantive problems for risk control: 1) efficient risk evaluation for each candidate policy and 2) valid policy selection with high probability risk control. To address these, we propose an efficient permutation-based weighted risk evaluator based on the Sinkhorn algorithm and select policies whose estimated risk upper confidence bounds (UCB) fall below the target threshold, with the UCB obtained through a proposed Gaussian bootstrap method. Theoretically, we prove that our risk evaluator's worst-case variance is no greater than that of the standard importance-weighted evaluator. Also, our policy control method yields an asymptotically valid simultaneous risk bound and provides asymptotic high-probability risk control for the selected policy. Empirically, our method maintains the target risk level, achieves lower risk than baseline methods, has lower variance in risk estimation, and enables reliable policy selection with high-probability risk control.

Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant approach to private deep learning, but it pays compute and privacy budget \emph{per trained model}: each new architecture, ensemble member, or downstream fine-tuning round incurs additional privacy cost. We propose \textsc{SPS} (Summarize--Privatize--Synthesize) and its enhanced variant \textsc{SPS+}, dataset-distillation algorithms that release a private synthetic dataset by privatizing intermediate activation statistics from a public pretrained model. Once released, the dataset is post-processed freely: any downstream architecture, ensemble, federated aggregation, or continual update incurs \emph{zero} additional privacy cost. In particular, a single \textsc{SPS+} dataset distilled from a Wide ResNet-22-8 transfers zero-shot to architectures with different inductive biases (Vision Transformers, Swin Transformers, and ConvNeXt), all without re-incurring privacy. Empirically, \textsc{SPS+} is competitive with state-of-the-art DP-SGD across $\epsilon \in \{1,2,4,8\}$ on CIFAR-10 and CIFAR-100, with a notable advantage on CIFAR-100 under strict privacy, achieving a $5.8\%$ gain at $\epsilon{=}1$ over compute-matched DP-SGD. \textsc{SPS+} additionally outperforms prior generation-based DP methods by a large margin and supports private federated and continual learning out of the box.

Adapting pretrained diffusion models to downstream objectives such as inverse problems often requires expensive test-time guidance or optimization. We propose a principled framework for generating high-quality reward-aligned samples at substantially reduced inference cost. Our approach formulates test-time adaptation as a hierarchical variational model, where control is amortized into a lightweight yet expressive stochastic policy. This formulation naturally supports few-step diffusion sampling: large step sizes enable fast inference, while the learned policy maintains sample quality by providing structured per-step control. The resulting fully amortized sampler achieves a strong quality--speed tradeoff, matching or exceeding recent test-time scaling baselines while requiring significantly less compute. For example, on $4\times$ super-resolution, our method achieves better perceptual quality with more than 5$\times$ faster inference compared to the best-performing baseline. We further extend our approach to a semi-amortized regime that combines cheap amortized proposals with limited test-time optimization, achieving state-of-the-art perceptual quality across several challenging inverse problems.

Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable features via multi-modal pre-training, making them well-suited for CIL. However, real-world visual and linguistic concepts are inherently hierarchical: a textual concept like ''dog'' subsumes fine-grained categories such as ''Labrador'' and ''Golden Retriever,'' and each category entails its images. But existing CLIP-based CIL methods fail to explicitly capture this inherent hierarchy, leading to fine-grained class features drift during incremental updates and ultimately to catastrophic forgetting. To address this challenge, we propose HASTEN (Hierarchical Semantic Tree Anchoring), a hierarchy-aware framework that uses semantic structure to stabilize CLIP-based CIL. Rather than treating classes as isolated labels, HASTEN leverages external hierarchical knowledge as structured supervision to organize visual and textual features in hyperbolic space, helping maintain parent-child relations as new tasks arrive and mitigating feature drift. Since the shared hyperbolic mapper is updated across tasks, we further stabilize it by constraining its updates to a null space induced by prior-task features, reducing interference with previous mappings while retaining adaptability to new classes. Extensive experiments on nine benchmarks show that HASTEN consistently outperforms existing methods while reducing feature drift and catastrophic forgetting.