NeurIPS 2026 Papers

Foundation models (FMs) have shown great promise in medical imaging, but most FMs are trained on unimodal data within isolated domains, such as brain MRI alone. Human aging and disease arise through coordinated biological processes across organs, therefore motivating multimodal FMs that learn whole-body representations. A key challenge, however, is that real-world multimodal biomedical data are often missing not at random, which can reduce power, limit generalizability, and introduce bias. We propose Pan-FM, a pan-organ foundation model pre-trained on imaging from seven organs (Brain, Heart, Adipose, Liver, Kidney, Spleen, and Pancreas) under realistic missing-organ scenarios. Pan-FM uses a unified backbone that handles organ missingness during both training and inference, and is pre-trained with masking-based self-distillation. We find that naive multimodal pre-training leads to dominant-organ shortcut learning bias, with the model over-relying on dominant organs such as adipose and heart. To address this, we introduce Saliency-Guided Masking (SGM), which uses the model attention distribution to adaptively mask dominant organs during pre-training, thus encouraging more balanced cross-organ, whole-body learning. Notably, SGM introduces negligible computational overhead and can be seamlessly integrated into existing self-supervised learning frameworks to improve multi-organ representation learning. On the UK Biobank, Pan-FM achieves stronger prediction across 13 disease categories and 14 single disease entities than single-organ and multi-organ baselines, with improved robustness under missing-organ settings. Pan-FM serves as a scalable solution to realistic modality-missingness in multimodal learning in system neuroscience and as a step toward more generalizable whole-body FMs.

High-resolution weather prediction is important for resolving local atmospheric patterns missed by coarse global forecasts. Large pretrained weather backbones have improved global forecasting, but adapting them to produce future high-resolution fields from recent coarse states remains challenging and costly. We formulate this setting as Predictive Spatiotemporal Weather Downscaling (\textbf{PSWD}), where the target is a future high-resolution trajectory rather than a same-time refined field. To make this structure explicit, we propose a framework that decomposes prediction into spatial and temporal stages using a shared pretrained backbone, fixed numerical scaffolds, and lightweight residual heads. For backbone adaptation, we introduce \textbf{PROLA} (PRincipal-Orthogonal Low-rank Adaptation), which splits a fixed low-rank budget between pretrained principal directions and their orthogonal complement. PROLA further rescales rank-1 optimizer updates using gradient signals, improving adaptation without increasing trainable rank. Experiments in the PSWD setting show that PROLA outperforms representative low-rank adaptation baselines under matched trainable-parameter budgets. These results support PSWD as a challenging setting for weather backbone adaptation and PROLA as an effective method for this setting.

Parameter-efficient fine-tuning (PEFT) methods such as low-rank adaptation have become essential for adapting large language models to downstream tasks. Existing approaches often apply low-rank updates uniformly across all layers, ignoring the structural coupling inherent in transformer architectures. We observe that transformers contain \emph{weight pairs} whose interactions govern model behavior including query-key products, value-output products, and feedforward networks. We propose PRincipal Interaction Subspace Matching (PRISM), a principled framework that exploits this coupling through spectral parameterization. For each weight pair, we parameterize adaptations using the spectral basis of the paired matrix, ensuring that updates occur in the subspace where the paired matrix has maximal effect. We validate our approach through controlled experiments on synthetic models, demonstrating faster convergence. Empirical evaluations on arithmetic reasoning, code generation, and vision tasks show that PRISM outperforms existing PEFT methods.

We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at $I(S^*; Y_{1:T})=0$. This privacy regime bounds the membership-inference attack (MIA) posterior success rate at the prior, an MIA-resistance level the DP framework matches only at $\varepsilon=0$ and infinite noise. All DP-ZO comparisons below are matched at the MIA posterior level. The key insight is that PAC Privacy charges mutual information only when the release depends on which candidate subset is the secret. Sign-quantizing subset-aggregated zeroth-order gradients creates frequent unanimity, steps at which every candidate subset agrees on the update direction; at these steps the released sign costs zero conditional mutual information. We propose two variants that span the privacy-utility trade-off: PACZero-MI (budgeted MI via exact calibration on the binary release) and PACZero-ZPL ($I=0$ via a uniform coin flip on disagreement steps). We evaluate on SST-2 and SQuAD with OPT-1.3B and OPT-6.7B in both LoRA and full-parameter tracks. On SST-2 OPT-1.3B full fine-tuning at $I=0$, PACZero-ZPL reaches ${88.99\pm0.91}$, within $2.1$pp of the non-private MeZO baseline ($91.1$ FT). No prior method produces usable utility in the high-privacy regime $\varepsilon<1$, and PACZero-ZPL obtains competitive SST-2 accuracy and nontrivial SQuAD F1 across OPT-1.3B and OPT-6.7B at $I=0$.

Pretrained vision transformers provide strong semantic representations, but their token embeddings are not designed to transform predictably under image rotations. This limits their direct adaptation to domains such as satellite imagery, histopathology, microscopy, and other scientific imaging settings, where in-plane orientation is often arbitrary and no canonical pose exists. Existing equivariant adaptation methods for pretrained models typically rely on frame averaging or canonicalization, which can be effective on synthetically rotated natural images but do not teach the adapted model an internal rotation-aware representation. We introduce OrbitLoRA, a lightweight orbit-aware adapter for frozen visual foundation models. OrbitLoRA combines standard LoRA with a typed residual branch that maintains scalar and vector fields over ViT patch tokens. The typed branch is initialized from local steerable image features and harmonic coordinate fields, updated at selected transformer depths, and injected into the frozen token stream through zero-initialized residual gates. During finetuning, paired rotated views supervise the typed state through a transport-and-rotation consistency loss, while the semantic output is trained to remain invariant for the downstream task. This design preserves the rich features of pretrained ViTs while adding a learnable rotation-symmetry prior. Across classification and segmentation tasks, and across DINOv2-, CLIP-, SAM-, and LLaVA-style backbones, OrbitLoRA matches strong LoRA-with-augmentation baselines in high-data regimes and substantially improves adaptation in low-data regimes, where learning the symmetry prior is most valuable.

Offline-to-online reinforcement learning (O2O RL) aims to improve offline RL agents with limited online interaction. Prior work has shown that, in conservative value-regularized methods such as CQL, pessimistic offline critics can underestimate values and hinder online fine-tuning. We show that this issue is more general: critic underestimation also appears in explicit policy-constraint methods such as TD3+BC, and can persist or become more severe through bootstrapped Bellman updates during fine-tuning. To address this problem, we propose Optimistic Q-value Adaptation (OQA), a framework that corrects underestimation at the Bellman-backup level. OQA constructs an additional offline-pretrained actor--critic pair and uses it together with the original pair to form a controlled optimistic backup that mitigates inherited pessimism during fine-tuning. Unlike trajectory-return calibration methods, OQA does not rely on return lower bounds and is not tied to CQL-style regularization. Across D4RL MuJoCo locomotion, Maze2D, AntMaze, and Adroit benchmarks, OQA instantiations based on TD3+BC and CQL consistently outperform the corresponding backbones and strong O2O RL baselines.

Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However, the largest wearable datasets are not publicly available for research, and leading wearable foundation models trained on such datasets are rarely open-weight or come with reproducible training code. To accelerate open science in wearable health, we release **OpenMyHeartCounts (OpenMHC)**, the largest and most comprehensive open-access wearable health dataset to-date, alongside open-source implementations of recent wearable foundation models. \openmhc{}, derived from over a decade of data collected through the My Heart Counts study app, includes >60 million hours of wearable data across 19 sensor channels (e.g., step count, heart rate, sleep, workouts) and up to 169 linked variables, including health, lifestyle, mood, and behavior from 11,894 consenting participants. Furthermore, we introduce a unified, open benchmark that enables standardized comparison of wearable health models across three tracks: health and behavior downstream prediction, multivariate data imputation, and time-series forecasting. We benchmark classical methods alongside recent wearable and multivariate time series foundation models. By open-sourcing data, code, and model weights at this unprecedented scale, we aim to democratize wearable health AI research and enable the community to drive open progress in this domain.

Online change-point detection is a fundamental problem in sequential monitoring, where the goal is to detect distributional changes in a time series as quickly as possible while controlling false alarms. We propose a general framework for online change-point detection that leverages foundation probabilistic forecasters, such as Chronos-2, which can capture complex temporal structure including trend, seasonality, heteroskedasticity, and nonlinear dependence. Our approach transforms each incoming observation relative to its predictive quantile forecast into an approximately standard Gaussian monitoring statistic, which can then be used in classical sequential detection procedures such as CUSUM and MOSUM. To reduce adaptation of the forecaster to post-change observations, we introduce a buffer between the forecasting context window and the forecast target. Because this design can induce serial dependence in the monitoring statistics, we develop a parametric bootstrap procedure to calibrate critical thresholds and control false alarms in practice. Extensive simulations across data-generating processes involving non-normality, trend, seasonality, heteroskedasticity, and autoregressive dependence show that the proposed method achieves competitive detection performance relative to benchmark procedures, while requiring no explicit specification of a parametric pre-change model.

Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages. The sequential nature of EHR, analogous to natural language, has motivated the use of next-token prediction to train prior EHR Foundation Models (FMs) over events. However, this training fails to capture the full structure of EHR. When a given event occurs must be captured, but the event value (abnormal lab) also modulates the likelihood of other clinical events. Most existing EHR FMs do not jointly model this likelihood and are unable to capture the full observation process, impacting downstream capabilities. We propose ORA, a marked time-to-event pretraining objective that jointly models event timing and associated measurements. Across multiple datasets, downstream tasks, and model architectures, this objective consistently yields more generalizable representations than next-token prediction and pretraining losses that ignore continuous measurements. Importantly, the proposed objective yields improvements beyond traditional classification evaluation, including better regression and time-to-event prediction. Beyond introducing a new family of FMs, our ablations suggest a broader takeaway: pretraining objectives that account for EHR structure are critical for expanding downstream capabilities and generalizability.

Universal visual anomaly detection (AD) aims to identify anomalous images and segment anomalous regions towards open and dynamic scenarios, typically adhering to zero- and few-shot paradigms without dataset-specific fine-tuning. Recently, the field has seen significant progress driven by the integration of vision-language foundation models. However, we observe that current methods often struggle with laborious prompt engineering, elaborate adaptation modules, and complex training strategies that ultimately constrain their flexibility and generalizability. In this paper, we rethink the fundamental mechanism of vision-language models for AD and present UniADet, an embarrassingly simple, effective and general framework for Universal vision Anomaly Detection. Our approach is built on two key insights: First, we reveal that the primary function of the language encoder is merely to derive decision weights and we demonstrate that it is unnecessary, as these weights can be learned more directly and efficiently. Second, to resolve learning conflicts arising from disparate feature manifolds, we introduce a systematic decoupling strategy that learns independent weights across both distinct tasks (classification vs. segmentation) and hierarchical features. UniADet is highly simple and efficient, learning only decoupled weights and requiring 0.02M learnable parameters. It is inherently versatile, adapting seamlessly to various foundation models (\eg, CLIP, DINOv2, and DINOv3). Extensive evaluations of 14 real-world benchmarks in the industrial and medical domains demonstrate that UniADet not only exceeds state-of-the-art zero/few-shot methods by a substantial margin, but also outperforms full-shot AD methods for the first time. This empirical evidence reveals the profound potential of language-free frameworks to redefine the boundaries of visual anomaly detection. The code and models will be made publicly available.

In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mechanisms. When a parameter mask and data subset jointly determine restricted fine-tuning, this separation incurs redundant overhead and makes coordinated selection difficult. We cast parameter and data selection as two bilevel selection problems under a common validation objective and derive a shared local response-surrogate scoring rule. Under first- and second-order validation-improvement approximations, parameter importance and data utility emerge as column-wise and row-wise aggregations of a single gradient interaction matrix, yielding a closed-form row--column correspondence for co-extracting both signals. Building on this structure, we propose $\textbf{DualSFT}$ ($\textbf{Dual}$-$\textbf{S}$election $\textbf{F}$ine-$\textbf{T}$uning), a one-shot dual-scoring algorithm that produces a parameter mask and data subset from shared gradient statistics. On 3B$-$9B LLMs, single-axis DualSFT variants strengthen target-task performance and stability--plasticity trade-offs within their comparison groups, while full DualSFT yields a more favorable joint-constrained trade-off than sequential hybrid baselines under matched budgets.

In autonomous driving, multi-agent collaboration enhances individual perception capabilities through information sharing. However, in real-world applications, differences in models and training data among heterogeneous agents inevitably lead to domain gaps between shared features, bringing challenges to collaboration. To address this, existing methods typically rely on a one-to-one adaptation paradigm, necessitating specific training or fine-tuning for every new agent. This results in a large accumulation of adapters on the vehicle, which is unsustainable for resource-constrained edge devices. To overcome these limitations, we propose UniMTO, a Unified adaptation framework that enables Many-To-One adaptation across heterogeneous agents using a single, fixed adapter. Specifically, it treats domain adaptation as a pattern transformation task, disentangling domain-invariant content and domain-specific pattern from features, and achieving source-to-target pattern transformation through recombination based on a Mixture-of-Experts (MoE) mechanism. Extensive experiments on V2V4Real and V2X-Real demonstrate that UniMTO achieves superior generalization, and exhibits strong zero-shot adaptation capabilities, outperforming all state-of-the-art methods, enabling many-to-one domain adaptation with a single, fixed adapter.

We study how to design online optimization loops for molecular optimization via adaptation of pre-trained generative models. At test time, we aim to utilize limited oracle feedback to steer generation toward top-molecules achieving high rewards. This creates a design problem coupled across several dimensions, including which candidates receive oracle evaluations, how observed rewards are utilized for model update, and how to overcome pre-trained model biases–to effectively explore over complex design spaces. Despite recent algorithmic progress on each individual component, it remains unclear how they interact in practice, within real-world online adaptation loops. For instance, top-$K$ optimization objectives may make adaptation overly greedy and thereby reduce exploration, while model-debiasing techniques may be unnecessary when exploration is already induced by exploratory acquisition functions, e.g., Thompson sampling. To address this, we conduct a controlled study on discrete-diffusion molecular optimization, finding that well-designed components remain beneficial when combined, indicating that they tackle complementary issues. Together, these components yield an online fine-tuning recipe that outperforms offline fine-tuning and search-augmented baselines across several small-molecule binding affinity and protein fitness optimization tasks, under equal oracle-call budgets and GPU-hour accounting.

Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored to different training steps. OATS leverages valuable training samples as principled guiding signals and dynamically generates high-quality synthetic data conditioned on them. We further design a diffusion-based framework to produce realistic time series. Experiments on TSFMs demonstrate that OATS consistently outperforms regular training and yields substantial performance gains over static data augmentation baselines across six validation datasets and two TSFM architectures. The code is available at the link https://anonymous.4open.science/r/OATS-536E.

RNA function arises from a hierarchical folding process in which primary sequences form secondary structures that govern biological activity. While foundation models have proven effective for learning transferable RNA representations, most existing approaches rely on primary sequence alone or incorporate structural information only superficially during pre-training. Three problems limit current approaches: experimentally determined secondary structures are scarce, computational pseudo-labels are noisy, and no existing model integrates structure at multiple levels across masking, architecture, and supervision. Here, we present \textbf{N\"uwa.RNA}, a pre-trained foundation model built on a \emph{structure-driven multi-level pre-training framework} that integrates secondary structure at all three stages of pre-training simultaneously. N\"uwa.RNA introduces dual-granularity masking that combines single-token masking with structure motif masking, treating entire structural elements as indivisible units; a noisy structural attention bias that injects curriculum-corrupted contact maps into every Transformer layer to model pseudo-label uncertainty; and a structure denoising objective that recovers clean base-pairing states from corrupted input, completing a closed denoising loop. Pre-trained on a large-scale non-coding RNA corpus, N\"uwa.RNA achieves state-of-the-art performance on 10 of 13 BEACON benchmark tasks, with up to $+$7.83 F1 improvement on structure prediction, and attains the highest zero-shot secondary structure prediction accuracy among single-sequence RNA foundation models.

Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-observation tasks. Yet the published work about these models does not give reviewers or users enough information to tell which model fits a given task. We argue that nobody knows what the current state of the art is in geospatial foundation models. The methods may be useful, but the GFM literature does not standardize evaluations, training and testing protocols, released weights, or pretraining controls well enough for anyone to compare or rank them. In a 152-paper audit, we find 46 cross-paper disagreements of at least 10 points for the same model, benchmark, and protocol; 94/126 papers with extractable pretraining data use a configuration no other paper uses; and 39\% of GFM papers release no model weights. This lack of community standards can be solved. We propose six concrete expectations: named-license weight release, shared core evaluations, copied-versus-rerun baseline annotations, variance reporting, one shared evaluation harness, and data-vs-architecture-vs-algorithm controls. These gaps are a coordination failure, not a fault of any individual lab; the authors of this paper, like many others in the GFM community, have contributed to them. Rather than just critiquing the community, we aim to provide concrete steps toward a shared understanding of how to innovate GFMs.

Epilepsy affects approximately $1$% of the global population, where seizure detection and reliable forecasting are critical for patient safety and quality of life. While emerging foundation models increasingly recast EEG analysis as a language modeling task, most remain confined to single-modality inputs and narrow domain generalization. In this work, we present NeuroNTP, a modular multimodal foundation model built on an xLSTM backbone and pretrained on one of the largest retrospective epilepsy datasets ever collected, comprising $150,000$ hours of neural (EEG) and physiological (ECG, SpO$_2$) signals paired with rich clinical text. NeuroNTP employs a flexible, montage-geometry grounded tokenizer and optimizes a composite objective, synergizing causal next-token prediction with seizure-specific auxiliary tasks to enable unified sequence modeling across heterogeneous modalities. This approach allows the model to capture long-range temporal dependencies and cross-modal interactions that unimodal baselines ignore. In extensive experiments, we show that NeuroNTP outperforms supervised baselines by a substantial margin on held-out clinical cohorts. Furthermore, the model generalizes to public EEG benchmarks such as CHB-MIT and Siena Scalp EEG, achieving state-of-the-art performance under standard evaluation settings. We release code and model weights to facilitate further research.

Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG), but it is less clear how effective they are in this particular field. Published evaluations differ in datasets, in the EEG-specific preprocessing that might influence reported results, and in the reported metrics, frequently obscuring the clinical relevance in EEG. We introduce NeuroAtlas, the largest EEG benchmark to date: 42 datasets and $\sim$260k hours covering clinical EEG (epilepsy, sleep medicine, brain age estimation) and brain-computer interfaces, and include multiple datasets per task along with bespoke clinical evaluation metrics. Besides evaluating EEG-FMs with respect to supervised baselines, we present results from generic time-series FMs. We report three findings. First, EEG-specific FMs do not consistently outperform time-series FMs, which have neither EEG-focused architectures nor been pretrained on EEG. Second, standard machine learning metrics are insufficient to assess clinical utility: thus, we thoroughly evaluate more appropriate measures such as the quality of event-level decision-making, hypnogram-derived features, and the brain-age gap in the domains of epilepsy, sleep, and brain age, respectively. Third, model rankings and performance can vary substantially within domains. We conclude that pretrained models perform largely on par, with only narrow advantages for a few, and that current models do not yet deliver on the promise of an out-of-the-box unified EEG model. NeuroAtlas exposes this gap and provides the datasets and metrics for the next generation of unified EEG FMs.

Foundation Models for Electroencephalography (EEG) are trained with masked reconstruction, contrastive alignment, and language-model interfaces, yet their learned representations remain difficult to inspect: raw EEG lacks canonical token semantics, and models may preserve waveform detail while discarding physiologically meaningful structure. We introduce Neuro-KE (Neuro-Knowledge Engine), a knowledge interface that computes EEG descriptors without additional human annotation and renders them as paradigm-specific supervision for semantic grounding. Rather than using handcrafted features as standalone classifiers, Neuro-KE converts the same domain knowledge into numeric prediction targets, feature-state text anchors, and feature-grounded instruction answers. Across three interfaces, Neuro-KE improves aggregate grounding and transfer metrics, with task-dependent exceptions: it improves balanced accuracy (BAcc) by 2.60 points over reconstruction-only masked pretraining across 12 datasets and three backbones, improves six-dataset EEG--text validation BAcc by 2.23 points over label-only anchors, and improves EEG-MLLM feature-grounded generation to ROUGE-L F1 0.607 and BERTScore F1 0.929. These results suggest that established EEG descriptors can serve as reusable grounding signals for inspecting and supervising EEG foundation-model interfaces.