NeurIPS 2026 Papers

Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive representations for long-horizon sequence data, especially continuous sequences like time series, remains an open challenge. While fixed-size patching has improved scalability and performance, discovering variable-sized, data-driven patches end-to-end often forces models to rely on soft discretization, specific backbones, or heuristic rules. In this work, we propose Reinforcement Patching (ReinPatch), a novel framework that jointly optimizes a sequence patching policy and its downstream sequence backbone model with reinforcement learning. By formulating patch boundary placement as a discrete decision process optimized via Group Relative Policy Gradient (GRPG), ReinPatch bypasses the need for continuous relaxations and performs dynamic patching policy optimization in a natural manner. Moreover, our method allows strict enforcement of a desired compression rate, freeing the downstream backbone to scale efficiently, and natively supports multi-level hierarchical modeling. We evaluate ReinPatch on time-series forecasting datasets, where it demonstrates compelling performance compared to state-of-the-art data-driven patching strategies. Furthermore, our detached design allows the patching module to be extracted as a standalone foundation patcher, providing the community with visual and empirical insights into the segmentation behaviors preferred by a purely performance-driven neural patching strategy.

Federated graph clustering (FGC) aims to partition distributed graphs into different groups while preserving data privacy. Existing FGC methods focus on spatial-domain information aggregation, while how to use expressive spectral-domain signals of graphs for FGC still remains unexplored. Main challenges are twofold: (1) Collecting and integrating semantic signals that fully express the critical patterns of graphs; (2) Obtaining spectral consensus while preserving local properties of clients. To address these challenges, we propose dynamic spectral federated graph-level clustering (DySFGC). DySFGC designs learnable polynomial filters to capture abundant graph signals and employs spectral contrastive learning and reconstruction to explore the latent semantic information. Besides, DySFGC develops dynamic spectral consensus, which updates the consensus via frequency-specific channels and conducts the dynamic updating process by evaluating the discrepancies between the server and clients. Extensive experiments on fifteen datasets demonstrate the superiority of DySFGC over eleven baselines.

Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain is constrained by the excessive cost of visual token processing. We identify that current efficient strategies based on token reduction are unreliable to select object-centric spatial evidence. To overcome this, we propose SmartRes, a framework that performs efficiency optimization in the pixel space via dynamic resolution routing. SmartRes first encodes a low-resolution view for global context and uses a lightweight router to activate high-resolution patches in object-centric regions and constructs an order‑preserving visual sequence. To further enable robust routing under severe foreground‑background imbalance, we introduce a margin-regularized routing objective that increases foreground-background logit separation and improves foreground recall. Experiments on Ego4D and EgoIntention show that SmartRes reduces visual tokens by up to 67\% while retaining 86.4\% of full-resolution performance, and achieves up to $1.66\times$ faster inference than state-of-the-art token reduction methods with higher accuracy. Furthermore, strong performance on small object grounding indicates the effectiveness of SmartRes towards egocentric applications. Code will be publicly available.

Federated Domain Generalization (FDG) aims to learn a global model robust to heterogeneous domain shifts without sharing raw data, a critical challenge in multi-center medical imaging. Most existing methods implicitly assume static inter-domain discrepancies, overlooking the fact that client representations evolve continuously during local optimization. We identify this phenomenon as representation dynamics, which refers to the temporal evolution of latent features induced by training updates rather than shifts in underlying data distributions. Such dynamics often lead to unstable aggregation, particularly in heterogeneous medical imaging scenarios. To address this issue, we propose Dynamic Knowledge Tracked FDG (DKT-FDG), a framework that explicitly tracks these dynamics by leveraging flow matching to model continuous representation trajectories. By aligning representation evolution instead of static snapshots, DKT-FDG improves robustness to domain shifts. Comprehensive experiments on multiple multi-center medical benchmarks demonstrate consistent improvements over state-of-the-art FDG methods, highlighting the importance of modeling representation dynamics in federated learning.

Virtual spatial transcriptomics aims to predict spatial gene expression from histopathology images. However, gene expression is not governed by morphology alone; regulatory relationships are structured, context-dependent, and shaped by the local tissue microenvironment. Existing visual-to-expression models and fixed-prior graph methods struggle to align molecular dependencies with spatially varying histological contexts. We propose DragH2ST, a dual-stage retrieval-augmented dynamic regulatory graph learning framework that constructs task-specific regulatory manifolds and rewires them at the spot level according to local histology. During training, DragH2ST retrieves and integrates heterogeneous biomedical knowledge to build a task-specific regulatory prior, guiding gene representations toward biologically plausible and context-adaptive regulatory structures. To capture microenvironment-dependent regulation, context-gated graph rewiring performs spot-level soft rewiring of gene-regulatory edges conditioned on histology-derived context, enabling adaptive graph reasoning without dense graph reconstruction. At inference, DragH2ST retrieves morphologically similar historical spot prototypes as case-level references, providing evidence-based support for interpretation and biological plausibility assessment. Experiments on multiple benchmarks show that DragH2ST improves prediction accuracy, preserves gene-gene regulatory topology, and captures spatially heterogeneous expression patterns.

Recent DETR-based tiny object detectors adopt dynamic-query mechanisms to handle density imbalance. However, existing designs entangle two effects: how many and which queries are decoded. This entanglement obscures what actually drives the gains, thereby hindering principled dynamic-query design; additional learned predictors or heuristic filtering steps also introduce extra overhead and inevitable decision errors, which can undermine practicality and final gains. In this paper, we disentangle these effects and show that dynamic query primarily acts as budgeting---allocating decoder query capacity according to object density. Motivated by this view, we formulate a monotonic and conservative budgeting principle and propose a budgeting-based DETR (BUTR), a streamlined framework that implements dynamic query by using a lightweight, training-free budgeter to estimate an input-dependent query budget $K(x)$ from encoder confidences. BUTR applies standard Top-$K(x)$ query initialization, removing heuristic filtering, learned components, and complex selection logic. On AI-TOD-v2 and VisDrone, BUTR shows clearer density-aligned query allocation behavior than prior dynamic-query detectors and improves AP by more than 0.5, while using about half as many queries with fewer parameters and GFLOPs, requiring over 2$\times$ less training time, and delivering 4$\times$ faster inference. Additional COCO results further indicate its applicability across a broad spectrum of detection scenarios.

The quadratic attention cost of Vision Transformers (ViTs) forces a sharp compromise between discretization and rollout horizon, and binds tightest in the fine-scale PDE regime where shocks, reaction fronts, and material interfaces occupy a small, time-varying fraction of the domain. Further, conventional neural surrogates do not adapt to localized features dynamically. We propose WAMRViT, a ViT that tokenizes its input as a balanced quadtree under a wavelet-inspired refinement criterion, encodes position and refinement level jointly via a 3D rotary positional embedding with a soft level axis, and regrids in cell space at inference to remain stable over long rollouts. A multi-scale variant additionally keeps each leaf at its native source resolution and defers the per-level resolution differential to the model. Unlike prior adaptive-tokenization work, the tokenizer imposes no a priori token count; we evaluate under fully adaptive topology over long autoregressive rollouts; and WAMRViT is the first machine-learning surrogate to natively tokenize multi-level Adaptive Mesh Refinement (AMR) data. We demonstrate that on uniform-grid datasets, WAMRViT exceeds a finest-patch uniform backbone on the region of interest while using a fraction of the tokens, with a structural interpolation cost on the global metric that the multi-scale variant closes at the initial rollout steps. On a complex AMR combustion problem whose extremely fine scale features uniform-grid baselines cannot represent natively and must project onto a coarser grid, WAMRViT operates directly on the adaptive cells and substantially reduces finest-level error at matched parameters. Code: https://anonymous.4open.science/r/wamrvit-review-74E1

Online Convex Optimization with memory and time-varying constraints (COCO-M) is a recently-introduced framework that captures the dynamics of stateful online learning and non-stochastic control with budget constraints. Despite its expressive power, COCO-M remains largely understudied. In this work, we tackle this problem in its full scale, providing an optimistic algorithm that incorporates untrusted predictions to tame universal dynamic regret and cumulative constraint violation. Our analysis leverages a potent reduction from memory to delay and treats state-dependent gradients as delayed feedback. We establish the first bounds that adapt to both environment non-stationarity (path length $P_T$) and prediction accuracy, while lifting previous restrictive assumptions such as functional separability. En route to these results, we derive findings of independent interest for the memory-to-delay reduction, and for delayed OCO with time-varying constraints. The proposed framework ensures sublinear worst-case guarantees that improve with prediction accuracy, reaching \(\mathcal O(1)\) regret under perfect predictions, with cumulative constraint violation (CCV) $\mathcal O(\sqrt{T})$ for unknown $P_T$ and $\mathcal O(\log T)$ for known $P_T$, while recovering the optimal COCO rates when memory disappears.

Model merging combines fine-tuned models into a unified one without joint training or access to original data. Dynamic merging improves flexibility by selectively activating task-relevant parameters, but existing methods either maintain a full shared model with tiny experts or allocate excessive capacity to experts, leading to suboptimal accuracy–efficiency trade-offs. We propose **DiDi-Merging**, a slim dynamic merging framework that uses **differentiable rank allocation** to balance shared and per-task expert parameters. The method casts parameter budgeting as differentiable rank optimization over low-rank modules, supervised data-free by the original task vectors, and applies a brief refinement stage to recover task fidelity. Across vision, language, and multimodal benchmarks, DiDi-Merging matches prior dynamic baselines at **1.24×** the parameters of a single fine-tuned model and surpasses them at **1.4×**, substantially more compact than methods requiring ≥2× storage.

Quantifying uncertainty in time series forecasting is particularly demanding because sequential data exhibit temporal dependence and are prone to distributional changes. Conformal inference has emerged as a powerful uncertainty quantification approach through the construction of prediction sets. Recent advances have introduced online conformal methods that adaptively adjust prediction thresholds through feedback mechanisms. However, the existing feedback mechanism typically relies solely on miscoverage indicators (actual feedback)—whether the true label falls within the interval at each time step—while overlooking the empirical prediction threshold (estimated feedback) that is derived from the oracle conformal method. In this paper, we propose $\textit{Dynamic Dual-feedback Conformal Inference}$ (DDCI), which incorporates a dual-feedback mechanism consisting of $\textit{actual feedback}$ and $\textit{estimated feedback}$. The former drives the primary adjustment of the intervals based on true observations, while the latter dampens excessive expansions or contractions by leveraging empirical thresholds from conformal inference during updates. By balancing these two signals, DDCI achieves more stable and narrower prediction intervals in sequential settings while adhering to the target coverage rate in practice.

Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual capability expansion across sequential tasks. In such scenarios, Multimodal Continual Instruction Tuning (MCIT) aims to acquire new capabilities while limiting catastrophic forgetting. Existing methods mainly follow a module-composition paradigm: they maintain task-level prompts or LoRA experts and dynamically route or aggregate a subset of them at inference. However, samples within the same task can still differ substantially in visual scenes, question intents, and reasoning demands. This motivates instance-level adaptation to individual query-image pairs rather than only selecting or combining task-level modules. To this end, we propose DRAPE (Dynamic Cross-Modal Prompt Generation), a prompt-learning framework that synthesizes continuous instance-specific soft prompts for MCIT. Instead of selecting prompts from a fixed pool, DRAPE derives prompt queries from the textual instruction and cross-attends to visual patch features, producing query-image conditioned prompts that are prepended to the frozen LLM. To mitigate forgetting during sequential updates, DRAPE applies null-space gradient projection to the shared projector and uses CLIP-based prototype routing for task-label-free generator selection at inference. Extensive experiments on MCIT benchmarks show that DRAPE achieves state-of-the-art performance among representative prompt-based and LoRA-based continual-learning baselines.

Transformers have become the dominant architecture for large language models, largely due to the scalability and flexibility of attention, feed-forward layers, residual connections, and normalization. This paper introduces dynamic short convolutions as an additional neural network primitive for improving Transformer architectures. Unlike static short convolutions, dynamic convolutions use input-dependent filters, preserving the locality bias of convolution while increasing expressivity. Motivating experiments show that applying dynamic short convolutions to key, query, and value representations improves performance on challenging associative recall tasks compared with static convolutional variants. Across language-modeling experiments ranging from 150M to 2B parameters, dynamic convolutions consistently outperform standard Transformers and Transformers augmented with static short convolutions. Scaling-law analysis indicates a 1.33$\times$ compute advantage over parameter-matched Transformers, while a custom Triton kernel enables efficient execution with minimal end-to-end slowdown. These results suggest that dynamic short convolutions are a scalable, hardware-efficient, and expressive primitive for advancing Transformer-based language models.

Mobile and wearable sensing offer a scalable pathway to longitudinal mental health monitoring, yet accurate prediction remains limited by two fundamental forms of distribution shift in human behavior. The first arises across users, where similar sensor patterns can carry different clinical meanings, a phenomenon we call inter-subject heterogeneity. The second arises within a single user, where behavioral baselines themselves evolve over time, producing intra-subject behavioral drift. In this domain, ground-truth labels come from Ecological Momentary Assessment (EMA), brief self-reports collected in everyday life that are intrusive and inherently sparse. As a result, approaches that address these distribution shifts by updating model parameters are difficult to apply. To address this gap, we propose DyCon (Dynamic Context Modeling), a dual-loop framework that personalizes by building and evolving a separate natural-language personal context for each user, without updating any model parameters. Within each session, a momentary refinement loop restructures this personal context to resolve inter-subject heterogeneity. Across sessions, a longitudinal update loop integrates only validated insights to track intra-subject behavioral drift. Extensive experiments on four public benchmarks (GLOBEM, PMData, StudentLife, LifeSnaps) show that DyCon consistently outperforms baseline methods, particularly under sparse supervision and behavioral drift. Our results suggest that evolving an explicit personal context offers a practical pathway for longitudinal personalization in real-world health monitoring.

A buyer who repeatedly participates in auctions for related items often faces a dynamic value problem: what they win today changes what tomorrow's items are worth. Despite the empirical importance of such effects, especially in display advertising, the algorithmic literature on repeated auctions has overwhelmingly focused on the learning problem—treating each opportunity as a contextual bandit—while sidestepping the planning problem induced by the dependence of current value on past wins. We introduce DynamAuction, a synthetic environment for single-buyer sequential bidding. Each won auction contributes reward through a kernel conditioned on a carry-over context that aggregates the bidder's past wins via a commutative monoid. The environment supports first- and second-price auctions, non-homogeneous Poisson arrivals, delayed feedback, saturation, and recency effects. We also introduce a class of Model Predictive Control algorithms built on top of the myopic-optimal valuation—the optimal bid in a second-price auction if no further opportunity were to follow. This class of myopic-optimal MPC consistently outperforms off-the-shelf deep RL baselines (PPO, SAC, TD3). We release DynamAuction to support RL research on repeated auctions.

Video generation must account for two sources of motion, one induced by the observer's camera path and the other caused by scene dynamics. An ideal camera-controlled video model should account for both motions: let users move the camera while evolving the scene dynamics. While current models handle camera-induced motion well in static settings, they struggle for dynamic scenes: objects are static, move incorrectly, or degrade in generation quality. We introduce DynaTokens, a lightweight set of learnable scene-specific tokens that teach dynamics to an existing camera-controlled world model. Our method is motivated by a simple asymmetry between the two sources of motion: whereas camera motion affects the generated view globally, object dynamics are spatially localized. Through cross-attention, DynaTokens trains the learnable tokens from a few example trajectories for a scene while keeping the base model frozen, and enables dynamics under new query camera paths. DynaTokens achieves a better simultaneous dynamics-camera tradeoff on VBench2 and WorldScore evaluations than LoRA, block finetuning, and specialized trainable-layer baselines. Analyses of token attention, ablations, and motion temporality suggest that matching the trainable interface to the structure of the learning target is important for effective adaptation.

Unsupervised video object-centric learning requires temporally consistent representations for downstream tasks. Existing methods mainly build temporal consistency on the assumption that adjacent frames share the same object composition. However, this local assumption is insufficient for real videos. Some objects remain semantically consistent over much longer temporal spans, while object composition may also change as objects appear or disappear. Fixed-slot recurrent Slot Attention architectures struggle to accommodate such visibility changes, limiting their ability to learn stronger temporal consistency beyond adjacent frames. We propose Dynamic Prototypical Contrast (DynaProto) to address these challenges. DynaProto introduces a dynamic recurrent Slot Attention architecture that determines whether each slot should be activated at each frame, thereby adapting to changing object visibility over longer temporal scopes. In addition, DynaProto introduces a prototypical contrastive objective that provides stronger clip-level temporal consistency supervision. Together, these two components enable DynaProto to learn more stable slot binding over extended temporal ranges. Extensive experiments show that DynaProto learns more temporally consistent slot representations and substantially outperforms prior methods on object discovery and object property prediction on real-world datasets, including an 11.0\% mBO improvement on YouTube-VIS.

Long-horizon forecasting of dynamical time series remains a fundamental challenge in machine learning, as accurate predictions require learning the underlying dynamics. While recent time-series foundation models leveraging in-context learning (ICL) have shown promise, we show that they fail to genuinely capture the governing dynamics. By rewriting the context of a time series as a collection of lagged input-output pairs, we frame dynamical time-series forecasting as a posterior inference problem over the space of structural causal models (SCMs), arguing that the underlying dynamic structure is a natural inductive bias. We propose DynaPFN, which uses pretrained tabular Prior-Fitted Networks (PFNs) to approximate this posterior through ICL, augmented with a library of dynamical features that permit simpler representations of complex systems. Even though the pretrained models have never seen time series data, DynaPFN outperforms existing zero-shot forecasting baselines on established benchmarks of chaotic time series and real-world datasets, demonstrating that incorporating structural priors over system dynamics is a valid approach for robust long-horizon forecasting.

Imaging of dynamic cellular responses to perturbations requires live-cell measurements, posing a challenge for fluorescence-based assays that can visualize molecular phenotypes by specific labeling but often affect cell health. Virtual staining helps by predicting fluorescence-like signals from label-free microscopy. Although many models have been developed for this task, their performance on volumetric time-lapse data remains poorly characterized. We address this gap by introducing DynaCell, an evaluation framework for 3D time-lapse virtual staining of live cells, comprising a new paired label-free and fluorescence 3D time-lapse imaging dataset, a suite of baseline models, and a three-tier metric panel measuring pixel-level fidelity, organelle segmentation performance, and single-cell phenotypic similarity. Using DynaCell, we study model behavior across 2 cell types and microscopes, 4 organelles, and 3 perturbation states. We find that regression baselines better predict the spatial localization of target organelles and are more robust to label-free input distribution shifts, while the generative baseline better captures population-level phenotype distribution across cells and organelles. These differences expose trade-offs between preservation of structural and phenotypic fidelity that are obscured by single-metric evaluation, and suggest how virtual staining can support downstream tasks such as localization, segmentation, tracking, and phenotypic profiling. DynaCell provides reusable data, code, checkpoints, and documentation for assessing virtual stains for live-cell biological measurements.

Safety is a key problem in reinforcement learning, especially during exploration. While safety filters are promising to address this issue, they are ill-suited for high-dimensional systems with unknown dynamics. We propose Dyna-style Safety Augmented Reinforcement Learning (Dyna-SAuR), a novel algorithm that learns a control policy as well as a scalable safety filter requiring minimal domain knowledge by leveraging a learned uncertainty-aware dynamics model. A novel filter learning formulation enforces to avoid failures and areas of high model uncertainty. As joint deployment of the control policy and filter allow to safely collect data and improve the dynamics model, certain areas grow and filter conservatism decreases over time. Dyna-SAuR reduces failures compared to state-of-the-art methods by two orders of magnitude on goal-reaching CartPole and MuJoCo Walker.