NeurIPS 2026 Papers

Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive models tend to underrepresent the co-occurrence structure of tail events (i.e., diseases, symptoms), reducing the fidelity and faithfulness of generated data for rare subpopulations. To this end, we propose ADAPCLA framework, which enables generative models to adaptively fit and generate EHR data through a data distribution-aware training strategy; this is achieved by internalizing data knowledge parameters by simulated annealing training. It also supports training-free adaptation to a diverse clinical population for generation through zero-shot distribution control. Moreover, our theoretical analysis characterizes rare-code logit updates through the label-wise empirical NTK and derives a prior-internalization bound for how annealing speed and NTK conditioning affect retained prior signals. Experiments on real-world data show that ADAPCLA achieves consistent gains in tail plausibility, downstream utility, and zero-shot control; in particular, it improves TailPairSeen over HALO by 114.2\% on MIMIC-III and 65.1\% on MIMIC-IV, outperforms GPT-style generation by 3.5\% F1 for zero-shot cross-population adaptation.

Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as it can model holistic 3D spaces by encoding geometric occupancy along with semantic categories. However, existing occupancy prediction methods struggle to meet practical deployment requirements, such as adapting to varying computing budgets, sensor setups, and observation views. In this paper, we propose a point-based Adaptive 3D Occupancy Prediction method, called AdaOcc, tailored for embodied scenarios. To accommodate heterogeneous sensor inputs, AdaOcc uses an adaptive geometry-guided dual-branch encoder that can support RGB images in various numbers of views with (estimated) depth maps or LiDAR scans. AdaOcc represents occupied regions via sparse semantic points trained with a progressive query learning strategy, allowing the prediction computational budget to be flexibly adjusted through query point numbers and decoder layers. To facilitate high-fidelity geometric modeling for lightweight point-based occupancy learning, we further propose a novel containment loss that regularizes predicted points to reside within valid occupied regions. Extensive experiments show that our method achieves a new state-of-the-art on Occ-ScanNet with considerable performance improvements over previous methods. Moreover, our framework demonstrates strong practical applicability as an adaptive 3D perception module in real-world embodied systems.

Large agentic models can now operate in complex digital games by pretraining on web-scale data, but gameplay trajectories often contain highly redundant actions. Single-action agents can exploit this redundancy spuriously by copying recent actions, leading to causal confusion. Fixed-length action chunks reduce this ambiguity by predicting multiple future actions at once, but their open-loop execution accumulates errors because the agent cannot adapt to new observations during the chunk. The agent should therefore learn when to act for longer without feedback and when to stop early for a new observation. We introduce AdaMAP (Adaptive Multi-Action Prediction), an adaptive multi-action prediction framework that directly generates variable-horizon action chunks, interleaves actions with grounded visual dreams that provide verifiable future constraints, and optimizes the resulting policy with offline imitation learning followed by online MAP-PPO. Across Minecraft and VizDoom, AdaMAP attains the best or tied-best result in five of six task categories and improves average Minecraft success by 3.56 percentage points over the fixed-horizon RL baseline. The learned horizons vary systematically across tasks and games, suggesting that adaptive multi-action prediction discovers useful temporal abstractions.

While recent multimodal recommender systems have demonstrated the effectiveness of incorporating visual and textual information to improve downstream performance, most existing methods rely on static modality fusion, assuming that the relative importance of textual and visual signals remains stable across recommendation scenarios. This design may not fully account for an important variation across recommendation requests: some queries require fine-grained visual cues, whereas others are better served by textual or functional semantics, in which case indiscriminate modality fusion brings in uninformative cues and impairs recommendation quality. To address this, we propose AdaM-Rec, an LLM-based framework for adaptive modality routing in multimodal recommendation, which enables dynamic calibration of reliance on textual and multimodal evidence for user-specific queries. Built on structured natural-language representations of items and user preferences, it estimates modality reliability using proxy recall tasks. Specifically, it generates pseudo-queries that match the granularity of the actual query while pointing to the user's positively interacted items as verifiable proxy targets, evaluating which modality yields better recall performance in analogous scenarios and optimizing the routing strategy in an agentic manner. It then performs routed recall with optimized strategy, enriches results with collaborative items, and ranks candidates by their relevance to both the query and user preferences. Experiments demonstrate that AdaM-Rec delivers strong performance against state-of-the-art baselines, highlighting the effectiveness and broader potential of adaptive control over modality reliance in multimodal recommendation. Code will be publicly available.

Large Reasoning Models (LRMs) rely on extensive Chain-of-Thought generation but suffer from memory bottlenecks due to linear Key-Value (KV) cache growth. While low-bit quantization offers a potential solution, we identify that current static quantization strategies suffer from severe degradation on complex reasoning tasks at extreme bit-widths. Notably, this accuracy drop is accompanied by increased generation length, which ironically limits the intended efficiency gains. We attribute this performance collapse to the fact that static quantization methods are insufficiently flexible to meed dynamic demands of precise long-range retrieval. Utilizing Average Attention Distance (AAD) to quantify retrieval patterns, we find that the attention patterns in shallow layers exhibit strong locality, whereas that in deep layers demonstrate intensive long-range dependencies. Furthermore, deep layers have fluctuating AAD values across token positions. Based on above findings, we propose AdaKVQ, an adaptive mixed-precision quantization framework that modulates the bit-width allocation guided by the dynamic demands of long-range context retrieval in each layer. Extensive experiments on complex reasoning benchmarks with Llama-3.1 and Qwen2.5 demonstrate that AdaKVQ consistently outperforms state-of-the-art baselines, achieving performance comparable to full-precision models.

Large language models face substantial computational challenges in long-sequence inference due to the quadratic cost of self-attention, with the prefill stage being particularly bottlenecked. Static sparse attention reduces compute via predefined masks but can miss task-critical evidence, while dynamic sparsification is more flexible yet can be brittle across tasks, leading to either insufficient coverage or distractor-induced noise. We propose AdaCal, an Adaptive Calibration framework for robust sparse attention that synergizes coarse, experience-driven task dispatch with head-wise physical profiling. By deriving entropy, concentration, and mid-zone coverage metrics from a lightweight probe pattern, AdaCal dynamically governs head activation and augmentation policies, allocating additional computation strictly on demand. This design is realized through task-conditional policies that effectively suppress unnecessary expansion for structure-dominant inputs while enhancing long-range coverage for retrieval-intensive tasks. Experiments show that AdaCal improves robustness over strong static and dynamic baselines, with clearer gains in long-context settings beyond 32K tokens.

Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process. While multimodal large language models (MLLMs) excel at general video understanding, their ability to perform specialized editorial tasks remains unexplored. We introduce \textbf{AdShot}, a benchmark for evaluating MLLMs on shot selection for ad clipping. It contains 823 professionally edited ad pairs (30-sec sources and 15-sec edits) across 194 brands and 17 industries, annotated with shot-level boundaries and content features (action, emotion, information, imagery). We evaluate 13 state-of-the-art video-only MLLMs and 2 audio-visual MLLMs (7B–72B parameters) on shot selection accuracy (Precision, Recall, F1), duration constraints (15s), and content preservation. We find: (1) model architecture matters more than scale, with smaller models often matching or exceeding larger ones; performance is strongest for target ads with 6–10 shots; (2) models exhibit a mean absolute duration error of 5.39s, with most tending to overshoot the 15s target, though they can still substantially reduce human editing effort; (3) all models exhibit systematic content biases, favoring information-rich and emotion-focused shots while under-selecting action and imagery. We publicly release our benchmark to facilitate research at the intersection of multimodal AI and computational advertising.

Large language model (LLM)-native advertising has emerged as a viable monetization channel as LLM service providers seek sustainable revenue streams. However, integrating native ads into LLM responses introduces two largely understudied challenges: (1) controlling ad intensity---how prominently advertising content appears---and (2) ad labeling---identifying which segments of the generated text constitute advertising. We propose AdKnob, a framework jointly addressing both challenges. For ad intensity control, we introduce new ad control tokens without pretrained semantic associations and align the model via direct preference optimization to generate ads at each desired level. For ad labeling, we propose an attention rollout-based attribution mechanism that identifies ad segments by tracing their attribution to input ad-related tokens, requiring no additional inference cost. We further construct MI-KnobSet, the first multi-level ad intensity preference dataset for LLM-native ads. Experiments across six LLMs and human evaluations show that AdKnob achieves uniformly spaced ad intensities where baselines collapse to a narrow range, achieves the highest ad labeling accuracy while being over 500$\times$ faster than the strongest baseline, and is preferred over baselines by human evaluators up to 89.6\% of the time. Code and data will be publicly available.

In training Mixture-of-Experts models, sharded data parallelism splits each expert’s parameters across GPUs. Before each layer runs, GPUs must use Allgather to rebuild the full weight matrix. This communication can take a large part of each training iteration. Prior work reduces this cost with lossy compression, but lossy methods can affect accuracy. We propose Adaptive Exponent Reuse Allgather, or AdERA, a lossless Allgather compression method for sharded MoE training. Our key observation is that, after a short warmup phase, most weight exponents stay the same across iterations. AdERA stores these exponents locally and sends only the sign and mantissa when the exponent has not changed. The receiver then rebuilds the exact original weight by combining the cached exponent with the received sign and mantissa. Because parameter matrices have different shapes across layers, AdERA compresses only when compression saves time. It also overlaps compression with Allgather communication and computation. Our real experiments and large-scale trace-driven simulator show that, compared with the lossless baseline, AdERA achieves a 3.70× speedup on 16 GPUs and is projected to reach 4.28× on 128 GPUs, while preserving bitwise-exact parameter reconstruction. Compared with the lossy baseline, AdERA achieves a 3.93× speedup on 8 GPUs and is projected to reach 4.42× on 128 GPUs. The source code is publicly available.

Reference-to-video (R2V) generation has advanced significantly and is now a promising paradigm for controllable video synthesis. Nevertheless, in advertisement video generation, where accurate camera control and precise product presentation are essential, existing approaches still face two fundamental challenges. First, current models adhere weakly to high-level semantic camera instructions, yielding stochastic or inconsistent camera motion. Second, they struggle to preserve strict cross-frame appearance consistency, often leading to identity ambiguity artifacts such as logo drifting, texture distortion, and geometric distortion. Although certain image animation methods incorporate explicit motion conditions (e.g., trajectories or motion fields) to improve controllability, these conditions are costly to annotate, thereby constraining their practical applicability. To address these limitations, we propose a camera-aware R2V framework named AdDirector, which adaptively exploits anchored guidance from the reference image and propagates it across spatial, temporal, and scale dimensions. Specifically, AdDirector introduces Spatio-Temporal-Scale (STS) modulation, a camera-conditioned modulation mechanism that first selects multi-scale reference features, and then regulates feature injection throughout the R2V generative process spatio-temporally conditioned on camera types. This design allows our model to adaptively balance global structural coherence and fine-grained detail preservation. Furthermore, AdDirector proposes Temporal Anchoring Regularization (TAR), a boundary-conditioned feature-level regularization scheme. TAR leverages latent trajectories extracted from a base R2V model as temporal anchors, thereby reducing feature drift and improving cross-frame appearance consistency. These two modules together transform static reference conditioning into structured anchored guidance, and experiments show that our method substantially improves camera controllability, appearance consistency, and spatio-temporal coherence for advertisement video generation.

Human players in cooperative games often rely on conventions: reusable patterns of play that coordinate well when used by the whole team, but can fail otherwise. Evaluating agents in this setting is difficult because human demonstration datasets are usually unlabeled: trajectories do not identify which player, group, or convention produced them. As a result, standard proxy-based evaluations can miss important population diversity. In particular, proxy bots produced by regularised self-play around a pooled behavioural cloning policy can remain concentrated around a single dominant mode of play. We introduce a pipeline for recovering and evaluating this hidden convention diversity from unlabeled demonstrations. Our method first learns latent convention structure from the dataset, then selects a compact set of distinct but plausible conventions, and finally reconstructs each selected convention into a high-performing playable proxy using KL-regularised PPO. A key practical challenge is that KL-regularised PPO is sensitive to the KL penalty, which is often tuned using online interaction. We provide an empirical offline calibration procedure for choosing this KL penalty in the PPO+KL reconstruction settings studied here. On Hanabi, we find that the BC-derived proxy suite is highly concentrated, and that the relative ordering of agents changes when evaluation uses the latent proxy suite instead.

Policy Dual Averaging (PDA) offers a principled Policy Mirror Descent (PMD) framework that more naturally admits value function approximation than standard PMD, enabling the use of approximate advantage (or Q-) functions while retaining strong convergence guarantees. However, applying PDA in continuous state and action spaces remains computationally challenging, since action selection involves solving an optimization sub-problem at each decision step. In this paper, we propose actor-accelerated PDA, which uses a learned policy network to approximate the solution of the optimization sub-problems, significantly reducing runtime. We provide a theoretical analysis that quantifies how actor approximation error impacts the convergence of PDA under suitable assumptions. We then evaluate its performance on several benchmarks in robotics, control, and operations research problems. Actor-accelerated PDA achieves superior performance compared to popular on-policy baselines such as Proximal Policy Optimization (PPO). Overall, our results take a significant step toward bridging the gap between the theoretical advantages of PDA and its practical deployment in continuous-action problems with function approximation.

Do language models make decisions under uncertainty like humans do? And if so, what role does extended reasoning play in the underlying decision process? We answer this question by introducing an active probabilistic reasoning task that cleanly separates sampling (actively acquiring evidence) from inference (integrating evidence towards a decision). Benchmarking humans and a broad set of contemporary LLMs against optimal reference policies reveals a consistent pattern: extended reasoning is the key determinant of strong performance, driving large gains in inference, while yielding only modest improvements in active sampling. To explain these differences, we fit a behavioral model that captures systematic deviations from optimal Bayesian behavior through interpretable parameter families, placing humans and models in a shared low-dimensional cognitive space. The resulting fits show how reasoning shifts models toward human-like regimes of evidence accumulation and belief-to-choice mapping, and yield testable predictions about the latent dynamics that might drive each decision. Probing residual-stream activations of an open-weight reasoning model, we find that the geometry of internal representations tracks these predicted dynamics, linking behavior to representational correlates of the fitted latent dynamics.

Many high-throughput scientific experiments produce distributional outputs, requiring conditional generative models to interpret the data, and to predict outcomes at new inputs. Acquiring experimental data at new conditions (e.g., for a new antigen or a new cell type) is often expensive, motivating the need for automated experimental design methods. However, existing active learning frameworks are limited as they assume scalar or vector-valued outputs. We first show that the Wasserstein test risk of a conditional generative task decomposes into a mean term and a shape term, demonstrating why mean-based active learning is blind to distributional shape. To capture both, we propose the transport Neural Tangent Kernel (tNTK), a tractable kernel that measures how sensitive a generative model's predicted distribution is with respect to its parameters. The tNTK recovers the standard NTK in the deterministic limit and upper-bounds the posterior Fréchet variance of the prediction under small parameter perturbations. It admits a closed form for affine Gaussian transport and tractable Monte Carlo estimators otherwise. Treating the tNTK as a Gaussian process covariance function, we greedily select inputs that minimize the total GP posterior variance across the pool, yielding a distribution-aware active learning strategy. We evaluate this strategy across four classes of conditional generative models, including conditional flow matching and conditional Monge gap, on synthetic distributions, intent-labeled sentences, and single-cell perturbation responses. In these experiments, the tNTK outperforms the baseline NTK computed from the conditional mean, demonstrating the value of distribution-aware active learning for generative-modeling tasks.

We consider the active learning problem where the goal is to learn an unknown function with low error under a Boltzmann distribution induced by the function itself. This self-induced weighting arises naturally in problems such as molecular energy modeling and free energy estimation, yet poses unique challenges as the target distribution is unknown and its partition function is intractable. We propose an acquisition function, AB-SID-iVAR, based on Gaussian Process surrogates that approximates the intractable Bayesian target distribution in closed form while avoiding partition function estimation, applicable to both discrete and continuous input domains. We also analyze a Thompson sampling alternative (TS-SID-iVAR) as a higher variance Monte Carlo variant. Despite the unknown target, under mild conditions, we establish that the terminal prediction error vanishes with high probability, and provide a tighter average-case guarantee. We demonstrate superior performance over existing active learning approaches on synthetic benchmarks and real-world modeling tasks.

Generative compressed sensing uses the range of a pretrained generator as a nonlinear model for recovering structured signals from limited measurements. We study a conditional version of this problem for image recovery from subsampled Fourier measurements using prompt-conditioned generative models. Our framework separates two roles of conditioning: the prompt used to design the sampling distribution and the prompt used to define the recovery model. For ReLU and Lipschitz conditional generators, we prove stable recovery bounds showing that prompt-matched Christoffel sampling retains the same Christoffel complexity constant as existing near-optimal generative compressed sensing theory, while prompt mismatch incurs an explicit compatibility penalty. Experiments with Stable Diffusion show that prompts meaningfully reshape Christoffel sampling distributions and influence image recovery. Overall, our results suggest that prompts should be treated as design variables with distinct effects on sensing, approximation, and recovery.

Most active learning (AL) methods estimate sample value through empirical criteria such as prediction ambiguity, diversity, or distributional coverage. We revisit AL from a spectral-geometric perspective by regarding the feature span of the current labeled set as the modeled subspace and its orthogonal complement as an operational residual subspace. The projected residual component of an unlabeled sample then reflects its relation to representation components weakly expressed by the labeled-feature spectrum. Based on this formulation, we propose a spectrally driven nullspace regulation framework that unifies spectrum-induced subspace decomposition, cross-cycle residual persistence estimation, and coverage-aware acquisition in the residual-coordinate space. This yields an acquisition criterion that favors samples whose residual components are both persistent across cycles and complementary to already selected samples. Experiments on visual benchmarks show competitive or improved sample efficiency over strong baselines. Further analyses support the proposed view by showing that residual components shrink anisotropically and that acquisition rankings vary substantially across backbones, indicating that sample value is conditioned on the current representation state rather than being purely data-intrinsic.

Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In generative discovery, however, one aims to sample valid new-to-nature designs, assigned negligible probability under, and thus inaccessible to, standard models fitted to the observed data. To overcome this limitation, we depart from data distribution matching and view a generative model through its generable set: the region it covers with non-negligible probability. This allows to introduce a new learning principle for out-of-distribution flow modeling: enlarging a model’s generable set to increase coverage of the valid design space. We propose Active Flow Expansion (ActFlow), a continued pre-training method that employs verifier feedback to expand a pre-trained model over new valid regions by iteratively adapting to synthetic data generated through active exploration in the learned flow representation. Theoretically, we establish to our knowledge first-of-their-kind statistical learning guarantees for out-of-distribution flow modeling, analyzing generable set expansion as a local-to-global reachability process over a learned representation. Empirically, we assess ActFlow with suitable out-of-distribution generative modeling metrics across small organic molecules, mid-sized drug-like molecules, therapeutic peptides, and protein sequence design tasks. Results show that ActFlow expands valid coverage far beyond the region modeled by the initial pre-trained model, significantly outperforming widely adopted synthetic flow pre-training methods.

Neural subgraph retrieval (NSR) models degrade under query distribution shifts, and retraining for each new distribution requires collecting fresh ground-truth labels, each of which entails an NP-hard subgraph-matching instance across a large corpus—making repeated label collection computationally prohibitive. We introduce ACROSS, an active corpus selection framework that, under a fixed solver budget, selects a small corpus subset per out-of-distribution query whose labels suffice for effective NSR training. We prove that allocating the entire budget to retrieving positives is optimal given extreme class imbalance. To enable efficient positive retrieval across arbitrary query distributions, we linearize corpus embeddings around a base model, apply randomized projections to eliminate hinge nonlinearities, and train distribution-shift-tolerant representations via adversarial parameter perturbation. The resulting corpus index is built once and reused across all distributions. Experiments on four molecular graph datasets show ACROSS consistently outperforms six baselines in downstream retrieval accuracy.