NeurIPS 2026 论文列表

Existing semi-supervised adversarial training (SSAT) methods typically employ a teacher-student framework where a teacher model provides supervisory signals for unlabeled data. However, they rely on static supervision---either hard pseudo-labels or soft labels with fixed temperature---which maintains constant learning difficulty throughout training. This rigidity fails to accommodate the student's evolving capability, especially when the teacher's performance plateaus. To address this, we reframe SSAT as knowledge distillation from a Bayes teacher and formalize a stage-dependent bias-variance tradeoff. This analysis reveals the existence of an optimal temperature: low temperatures reduce bias, while higher temperatures control the variance term in the robust generalization bound. Guided by this insight, we propose BayesAT, a Bayes-guided progressive distillation framework that jointly employs a low-to-high temperature warming schedule and confidence-adaptive sample reweighting. BayesAT allows the student to first learn from sharp, high-confidence supervision to establish reliable decision boundaries, then progressively from smoother distributions that encourage exploration of inter-class relationships. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-200 demonstrate that BayesAT consistently improves robust accuracy while maintaining natural accuracy over existing SSAT methods, highlighting the importance of dynamic, theory-driven supervision for effective knowledge transfer in semi-supervised adversarial training.

Modern transformation-aware self-supervised representations are routinely scored under protocols that grant the evaluator paired views, augmentation labels, or teacher anchors --- none of which the deployed predictor ever sees. We audit the gap that opens once these privileges are removed and only a single query remains. The finding is a systematic evaluation illusion: on 3DIEBench-Honest, honest single-query rotation errors are typically $2{-}3\times$ as large as native transformation scores, consistently across comparators and matched-utility thresholds. To make this gap measurable, we introduce a deployment-honest evaluation contract specifying a one-query test interface, matched semantic utility, a strongest-eligible-neighbor headline with tail summaries, and explicit privilege accounting. Theory and experiments are in service of the contract: lower-bound analysis grounds the contract at the interface level, while a positive control supplies a non-vacuous upper certificate under declared privileges. We exercise the contract on a generated law, on 3DIEBench-Honest, and on PTB-XL. The operational conclusion is that native transformation-aware success is not, on its own, a single-query deployment certificate.

Recent audio embedding models learn general-purpose representations that transfer across clip- and frame-level tasks, yet they operate almost exclusively on single-channel signals and are blind to spatial structure. Spatial audio models capture that structure but rely on clip-level supervision or contrastive objectives, yielding embeddings unsuitable for frame-level tasks like sound event detection and localization (SELD). We argue this dichotomy is not fundamental: patch-level spatial self-supervision can jointly learn acoustic and spatial features at both temporal granularities. We introduce AudioSphere, a multi-channel masked autoencoder trained on Ambisonics recordings that reconstructs both spectral content and directional cues via a joint spatiotemporal masking strategy that masks both time-frequency patches and intensity vectors. AudioSphere supports frame-level predictions while producing strong clip-level representations via aggregation. Alongside the model, we release RealSELD, a standardized benchmark for evaluating frozen backbones on real-world multi-channel SELD. AudioSphere enables, for the first time, SELD with a frozen self-supervised backbone, while matching or exceeding prior general-purpose audio embedding models on the HEAR benchmark.

Unlearnable Examples (UEs) aim to protect data from unauthorized model training by injecting imperceptible perturbations that degrade model generalization. Existing methods typically rely on well-trained surrogate models to generate such perturbations, implicitly assuming that stronger surrogates yield better degradation. In this work, we challenge this widely adopted assumption and show that it is fundamentally suboptimal. We provide theoretical insights by establishing an upper bound on the target model's test loss, showing that perturbation effectiveness is closely related to the surrogate’s sensitivity to input perturbations. Specifically, a less robust surrogate model leads to stronger poisoning performance. Motivated by this analysis, we propose a simple yet effective stability metric to select optimal surrogate models for single-level methods. We further extend this principle to bi-level optimization frameworks. Beyond revealing the bi-level method's implicit reliance on non-robust surrogates, we substantially boost its poisoning performance by the stability-based surrogate selection as well. Experiments on five datasets and four representative UE methods show that our selected surrogates reduce the target model's test accuracy by an average of 18\% compared with well-trained surrogates. Notably, this is the first work to reduce CIFAR-10 test accuracy to 10\% under a perturbation budget of 1/255, whereas prior UE methods typically rely on 8/255.

Operator learning has been highly successful for continuous mappings between infinite-dimensional spaces, such as PDE solution operators. However, many operators of interest—including differential operators—are discontinuous or set-valued, and lie outside classical approximation frameworks. We propose a paradigm shift by formulating approximation via graph convergence (Painlevé–Kuratowski convergence), which is well-suited for closed operators. We show that uniform and $L^p$ approximation are fundamentally inadequate in this setting. Focusing on maximally monotone operators, we prove that any such operator can be approximated in the sense of local graph convergence by continuous encoder–decoder architectures, and further construct structure-preserving approximations that retain maximal monotonicity via resolvent-based parameterizations.

Pre-training neural operators on diverse PDE datasets has emerged as a promising paradigm for building general-purpose surrogate models in scientific machine learning. However, the **inherent complexity** and **structural diversity** of PDE solution operators make multi-PDE pre-training fundamentally difficult. Existing approaches address this mainly by enlarging model capacity, while the target solution operators themselves remain unchanged. Inspired by classical numerical analysis---where a problem-tailored *operator transformation* reformulates a complex PDE into a numerically easier equivalent---we propose to similarly reformulate the target solution operators, turning a heterogeneous set of complex, structurally divergent operators into a simpler and better-aligned equivalents. Since different PDEs admit different simplifications, the transformation must be *adaptive and input-dependent*, so that a single neural operator can approximate the entire family jointly. We instantiate this idea as AOT-POT (**A**daptive **O**perator-**T**ransformation for **P**re-training **O**perator **T**ransformer), which realizes such a transformation by expanding the hidden representation into multiple parallel streams, aggregating and redistributing them with input-dependent weights before and after each sub-layer, and mixing streams through Sinkhorn-projected doubly stochastic matrices for stable training. Together, these mechanisms reformulate the diverse, complex solution operators into a simpler equivalents that a single architecture can approximate jointly. Empirically, AOT-POT achieves state-of-the-art results on 12 PDE benchmarks with only 3% additional parameters, reducing the relative L2 error by up to 77.6% (40.9% on average). Fine-tuning further reduces L2 error by up to 92% on in-domain PDEs and 89% on out-of-domain PDEs, confirming that adaptive operator transformation is an orthogonal and effective axis for advancing PDE foundation models, beyond merely scaling model capacity.