NeurIPS 2026 Papers

Large Language Models (LLMs) have demonstrated remarkable capabilities in Information Extraction (IE) tasks; however, their performance in zero-shot Named Entity Recognition (NER) remains suboptimal. We argue that this limitation stems not from intrinsic defects within the models, but rather from the failure of existing instruction paradigms to effectively elicit their latent capabilities. Drawing inspiration from cognitive science, we propose an instruction optimization framework termed Instruction Semantic Elaboration (ISE), which fully elicits models' latent NER capabilities by mimicking the semantic elaboration process—specifically by contrasting related concepts, decomposing constituent elements, and illustrating with concrete examples. We evaluate our framework under a zero-shot setting across four domain-specific datasets. Results demonstrate that ISE effectively and stably unlocks the intrinsic NER capabilities of LLMs, achieving a 10.99\% improvement in F1 score over initial instructions and outperforming strong baselines by 6.89\%. Furthermore, the framework generalizes well across model architectures and parameter scales, while remaining robust to the quality of initial instructions. This study offers a novel perspective on activating NER capabilities in LLMs, facilitating their efficient and stable deployment in real-world scenarios.

Despite the critical need for sustainable deep learning, model energy efficiency remains largely neglected in Hardware-Aware Neural Architecture Search (HW-NAS). Discovering energy-efficient models is severely bottlenecked by costly on-device measurements and the brittleness of training-free accuracy proxies, which often yield unbalanced "glass cannon" architectures. We propose Energy-Efficient Neural Architecture Search (EENAS), a strictly zero-shot HW-NAS framework. EENAS introduces a PCA-based anomaly router that dispatches tasks to decision trees or tabular transformers, achieving <10\% MAPE zero-shot energy prediction on entirely unseen GPUs. Simultaneously, we robustly estimate accuracy by ensembling training-free proxies via a novel Log Z-score aggregation. Combining these predictors unlocks Absolute Hardware Bounding, discovering Pareto-optimal architectures for strict energy budgets entirely offline in under 30 minutes. Evaluated on ImageNet-1K, our models achieve 40\% less energy consumption at comparable accuracy, or a 2.5\% accuracy improvement under the same energy budget compared to efficient baselines.

Recent flow matching (FM) methods improve the few-shot adaptation of vision-language models, by modeling cross-modal alignment as a continuous multi-step flow. In this paper, we argue that existing FM methods are inherently constrained by incompatible geometric priors on pre-trained cross-modal features, resulting in suboptimal adaptation performance. We first analyze these methods from a polar decomposition perspective (\ie, radial and angular sub-manifolds). Under this new geometric view, we identify three overlooked limitations in them: \textit{1) Angular dynamics distortion}: The radial-angular coupling induces non-uniform speed on the angular sub-manifold, leading to regression training difficulty and extra truncation errors. \textit{2) Radial dynamics neglect}: Feature normalization discards modality confidence, failing to distinguish out-of-distribution and in-distribution data, and abandoning crucial radial dynamics. \textit{3) Context-agnostic unconditional flow}: Dataset-specific information loss during pre-trained cross-modal feature extraction remains unrecovered. To resolve these issues, we propose \textbf{warped product flow matching (WP-FM)}, a unified Riemannian framework that reformulates alignment on a warped product manifold. Within this framework, we derive \textbf{direct product flow matching (DP-FM)} by introducing a constant-warping metric, which yields a decoupled cylindrical manifold (\ie, direct product manifold). DP-FM enables independent radial evolution and constant-speed angular geodesic transport, effectively eliminating angular dynamics distortion while preserving radial consistency. Meanwhile, we incorporate classifier-free guidance by conditioning the flow on the pre-trained VLMs' hidden states to inject missing dataset-specific information. Extensive results across 11 benchmarks have demonstrated that DP-FM achieves a new state-of-the-art for multi-step few-shot adaptation.

Coresets are a fundamental tool for scaling clustering algorithms, but standard constructions assume full access to the pairwise distances. In many modern settings, access to distances is costly and thus limited, and algorithms must instead rely on inexpensive comparison queries. We study coreset construction in the Rank-Measure (RM) model, where the algorithm can make noisy comparison queries to a quadruplet oracle alongside a small number of distance queries. In this model, we present the first algorithm to construct an $\epsilon$-coreset for $(k,z)$-clustering in Euclidean space. It matches the coreset-size bounds known in the classical model, while using only polylogarithmically many distance queries in the input size. Our approach is based on a uniform-sampling framework [Chen, SICOMP 2009], which decomposes the input into geometric rings and samples uniformly within each ring. We implement this approach using primarily noisy comparison queries, and then compress the result using a standard full-access coreset construction. In contrast, prior work in oracle-based clustering achieved only constant-factor guarantees. We complement our theoretical findings with experiments that demonstrate strong empirical performance against full-access baselines while using significantly fewer distance computations.

Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy of interest is a recursive geometric object with fine-scale refinements that continue beyond the levels directly observed. We introduce classification fields: infinite-depth hierarchical cluster structures on $\mathbb{R}^d$ generated by a local parent-to-child refinement rule. A classification field generator maps each parent centre to an ordered, bounded, and separated tuple of child residuals. Together with a root and a scale factor, this rule recursively generates cluster centres, Voronoi cells, and a metric DAG encoding the hierarchy. Given only a finite prefix of such a hierarchy, we learn a classification field predictor that approximates the generator and can be rolled out to unseen depths. We prove exponential truncation convergence in the completed cell metric and ReLU realizability with width $(O(\varepsilon^{-\gamma})$ and depth $\widetilde O(\varepsilon^{-3\gamma/2})$, where $\gamma=\log K/(-\log s)$, up to finite-window aspect-ratio factors. The approximation holds at the level of the induced compact metric structures, measured in the completed cell-metric Hausdorff distance. Experimental validation on matched CFG-generated hierarchies, IFS fractals, and image-induced recursive clustering hierarchies shows that learned predictors preserve ordered child slots, unordered geometry, and hierarchy-level path metrics under recursive rollout. These results support the claim that finite hierarchical observations can reveal local refinement rules capable of generating substantially deeper classification fields.

Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single scene; it requires progressing through evolving events, viewpoint shifts, and discrete shot boundaries. Existing autoregressive models often struggle in this setting. Trained primarily for short-horizon continuation, they treat long sequences as extended single shots, inevitably suffering from motion stagnation and semantic drift during long rollouts. To bridge this gap, we introduce \textbf{CausalCine}, an interactive autoregressive framework that transforms multi-shot video generation into an online directing process. CausalCine generates causally across shot changes, accepts dynamic prompts on the fly, and reuses context without regenerating previous shots. To achieve this, we first train a causal base model on native multi-shot sequences to learn complex shot transitions prior to acceleration. We then propose Content-Aware Memory Routing (CAMR), which dynamically retrieves historical KV entries according to attention-based relevance scores rather than temporal proximity, preserving cross-shot coherence under bounded active memory. Finally, we distill the causal base model into a few-step generator for real-time interactive generation. Extensive experiments demonstrate that CausalCine significantly outperforms autoregressive baselines and approaches the capability of bidirectional models while unlocking the streaming interactivity of causal generation.

Transformers achieve strong accuracy but incur high compute and memory cost. Structured pruning reduces inference cost, but most methods rely on retraining or multi-stage optimization, which limits post-training deployment. We propose CORP, a closed-form one-shot structured pruning method that removes MLP dimensions and attention substructures using only unlabeled calibration data without labels, gradients, or fine-tuning. CORP formulates structured pruning as a representation recovery problem. It models removed components as affine functions of retained components and derives closed-form ridge regression solutions that fold compensation into model weights. This minimizes the expected representation error under the calibration distribution. Experiments on ImageNet with DeiT reveal strong redundancy in both MLP and attention representations. Without compensation, one-shot structured pruning causes severe accuracy loss. With CORP, models retain high accuracy under aggressive sparsity. On DeiT-Huge, CORP achieves 83.27% Top-1 accuracy after pruning 50% of both MLP and attention structures.

This paper considers a practical few-shot anomaly detection (FSAD) setting, termed **discriminative FSAD**, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce **IDEAL**, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormality as deviations from normality. IDEAL decomposes the learning process into two novel components: 1) a Normal Variation Eraser to suppress nuisance normal variations that may lead to noisy deviations from normality, thereby highlighting anomaly-relevant deviation representations; 2) an Intrinsic Deviation Encoder to decompose these denoised deviation representations into intrinsic deviation vectors capturing the most discriminative orthogonal deviation directions. At inference, IDEAL scores query-to-normal deviations preserved after projection onto the learned intrinsic deviation vectors, enabling generalization for both seen and unseen anomalies. Extensive experiments on eight real-world datasets show that IDEAL generalizes effectively to unseen anomalies and consistently outperforms existing state-of-the-art FSAD methods.

Few-shot medical image segmentation has shown great potential in reducing annotation costs for clinical applications. Recently, many few-shot methods have explored the Segment Anything Model (SAM) for training-free medical image segmentation via prompt engineering. However, existing approaches mainly focus on locating positive prompts from support images while overlooking informative background cues, making them prone to over-segmentation in anatomically similar regions. Moreover, simply introducing negative prompts cannot effectively suppress boundary leakage, and may even interfere with SAM’s mask decoding process, resulting in worse performance than using positive prompts alone. To address this limitation, we propose a training-free support-query framework, termed Boundary-Aware Prompt Mining, which introduces boundary-aware negative prompting for SAM-based few-shot medical image segmentation. Specifically, we introduce a Prototype-guided Positive Prompt strategy, which adopts a multi-center prompting mechanism to construct multiple foreground prototypes, enabling comprehensive spatial coverage in query images. Furthermore, we propose a Boundary-band Ambiguity Filtering strategy that identifies a boundary-adjacent background band and progressively removes ambiguous pixels and unreliable clusters highly similar to the foreground, enabling the selection of reliable negative prompts for suppressing false positives near foreground boundaries. The generated positive and negative prompts are jointly fed into SAM to produce refined segmentation results without additional training. Extensive experiments on Abd-MRI and Abd-CT datasets demonstrate that our method consistently outperforms existing approaches, highlighting its effectiveness and robustness under limited annotation settings. Code will be released upon acceptance.

World models predict future states from observations and actions, enabling planning and decision-making in complex environments. Most existing approaches assume fixed environment dynamics and struggle to generalize to unseen regimes without retraining. However, in many real-world multi-agent systems, environment dynamics vary across scenarios due to diverse styles and evolving agent behaviors. Meanwhile, only a small number of demonstration transitions are available in new environments, making adaptation to such unseen dynamical regimes challenging. In this work, we introduce AdaWM, a world model that achieves zero-gradient few-shot adaptation to unseen dynamical regimes through Feature-wise Linear Modulation (FiLM). AdaWM is built on a JEPA-style latent prediction framework with a permutation-invariant context encoder whose output modulates the model through FiLM, enabling fast adaptation without modifying model parameters. Across heterogeneous multi-agent environments, AdaWM consistently reduces prediction error under distribution shift, outperforming fine-tuning and MAML in both adaptation gain and efficiency. Specifically, it achieves up to 15% reduction in prediction error with only K=5 demonstrations. Moreover, we reveal that scaling model capacity alone does not guarantee effective adaptation: fine-tuning a JEPA-based model with 3.9× more parameters degrades performance by 40%, highlighting the importance of explicit conditioning mechanisms over scale alone. These results show that AdaWM provides an effective approach for adapting world models to novel dynamical regimes.