NeurIPS 2026 论文列表

Dataset pruning reduces the storage and training costs of deep learning by selecting an informative subset from a large dataset. However, most existing pruning methods require fully labeled data, which limits their applicability in realistic settings where unlabeled data are abundant and annotation is costly. Recent label-free pruning methods address this issue, but they rely on features from pre-trained models to estimate example difficulty. This dependence can be unreliable when the target dataset differs substantially from the pre-training distribution. We propose a label-efficient dataset pruning framework that, given only a small labeled subset, uses semi-supervised learning to generate pseudo-labels for unlabeled data, allowing existing supervised pruning methods that require label information to be seamlessly applied to the resulting pseudo-labeled training pool. We then estimate example difficulty from pseudo-label-induced training dynamics and select a coreset. By learning directly from the target dataset, our method better captures the target distribution and provides more reliable signals for difficulty estimation and coreset selection. We validate our approach on domain-specific, image-corrupted, and long-tailed datasets, where it achieves state-of-the-art performance among label-free and label-efficient baselines, while also demonstrating competitive performance on standard benchmarks.

Progress in Sign Language Translation (SLT) is frequently hampered by the "data bottleneck," a challenge particularly acute for regional languages such as Catalan Sign Language (LSC). In this paper, we present LSC-Parlament, a large-scale, multimodal dataset for LSC derived from Catalan Parliament sessions spanning 2021 to 2024. The dataset comprises over 87 hours of LSC video, synchronized with speech and text translations in either Catalan or Spanish. To curate this resource, we developed a fully automated pipeline that enables alignment between sign language, speech, and text without requiring prior human-annotated data. We provide a systematic evaluation of the dataset across multiple dimensions, including video tracking quality, audio transcription accuracy, and translation benchmarks. Our experiments compare transfer learning, zero-shot, and supervised translation settings, demonstrating significant knowledge transfer between spoken languages in the LSC-Spanish modality. By releasing LSC-Parlament, we provide a challenging open-domain benchmark to foster research in inclusive and scalable translation technologies.

As real-world datasets become more complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data, such as time-series, free text, and structured records, often requires non-trivial domain expertise. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples *in-context* to synthesize a *global rubric*, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform *naive* text-serializations of inputs into a more standardized format for downstream models. We also describe *local rubrics*, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive LLM baselines, and a clinical foundation model pretrained on orders of magnitude more data. Beyond performance, rubrics offer operational advantages such as being easy to audit, cost-effectiveness at scale, and facilitating tabular representations.

Object detection data contains a structural asymmetry that is easy to overlook in dataset distillation (DD). Each detection image couples a spatial layout with a visual realization. Layouts define detection targets through object categories and bounding boxes, while pixels instantiate these targets with particular object appearances and backgrounds. Although both forms can be redundant across a dataset, they play different roles. Layouts define the task distribution, so synthesizing them freely risks altering the detection problem. Visual realizations, on the other hand, are conditional samples attached to layouts. This motivates a separation principle: preserve and recombine layouts from the original data, then use generative priors to diversify their visual realizations. Based on this principle, we propose LCD$^3$, a layout-conditioned diffusion framework for object detection DD. LCD$^3$ constructs composite layouts from diverse groups based on scene-level embeddings. A layout-conditioned diffusion model then generates new images from these layouts, enriching object appearance and scene realization without discarding the spatial structure needed for detection. The generation process is grounded with semantically verified object crops to retain the original visual style. Experiments show that this separation between layout and realization produces more effective distilled detection datasets. LCD$^3$ consistently outperforms the previous state-of-the-art, OD$^3$, across benchmarks using fewer objects per distilled image, e.g., a $12.5$\% mAP50 improvement at a $0.5$\% compression ratio on PASCAL VOC.

We present LAION-BVD (LAION - Big Video Dataset), a large-scale open video dataset for multimodal learning, containing _1.3B platform-specific video URLs_ collected from CommonCrawl. From these, we download 80M videos with a total duration of _10 million hours_. The dataset is designed for multimodal pre-training across video, audio, and image modalities. Using content-aware scene detection we extract clips for which we synthetically generate video and audio captions. Models trained on these data achieve competitive performance on standard video-text and audio-text benchmarks, with consistent improvements as training or model scale increases. Additionally, we explore video frames as a new source of image-text data by extracting scene-changing frames. These frames exhibit a visual distribution distinct from standard web image corpora, and models trained on this dataset achieve strong image-text retrieval performance. Overall, LAION-BVD significantly expands open access to multimodal videos at unprecedented scale and will be released to the research community for multimodal research

We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to foster research into the neural effects of various meditation practices and the evolution of these effects over a six-week training period. The dataset contains EEG recordings and psychological assessments from 74 healthy college participants, collected at two distinct time points: pre-intervention and post-intervention. Participants were randomly assigned to one of three distinct meditation groups: two mantra-based techniques (SA-TA-NA-MA and Hare Krishna) and one Breath Focus practice. Leveraging this unique longitudinal and comparative dataset, we propose a benchmark suite comprising three distinct classification tasks: (1) cognitive state decoding to distinguish between resting and meditation states, (2) fine-grained classification of the specific meditation techniques, and (3) cross-session adaptation to evaluate model generalization across the longitudinal time gap. We provide comprehensive baseline results for these tasks utilizing a range of classical machine learning algorithms and deep learning architectures. The complete dataset, preprocessing pipelines, and benchmark evaluation code will be publicly released, offering a valuable resource and a standardized framework for the development and comparison of new analytical methods in computational meditation research and EEG-based machine learning.

Large language models frequently ignore in-context evidence that contradicts their parametric priors, producing answers that reflect default beliefs rather than the supplied passage, a phenomenon termed *knowledge conflict*. This failure is commonly attributed to insufficient encoding of the contextual signal. We challenge this explanation. Through systematic, layer-by-layer analysis in a controlled multi-hop question-answering setting, we show that models faithfully encode the context-supported answer in their intermediate representations, yet suppress it in the final layers before generation. We term this *late-layer reversal*: the representational support for the evidence answer rises through the middle layers, then collapses in the final ~40\% of the network. To explain this phenomenon, we identify a *Dual-Track* computational structure within the transformer's hidden state. A *knowledge direction*, recovered by a learned linear probe, encodes the contextually supported answer; a nearly orthogonal *output direction*, aligned with the unembedding geometry, determines the model's prediction. These two directions are causally severed: interventions along the knowledge direction produce no change in output, whereas targeted suppression of prior-restoring computation along the output direction reliably causes the model to follow the contextual evidence. Our findings hold across nine model-dataset combinations and reveal that knowledge conflict failures arise not because models fail to represent contextual knowledge, but because their generation pathway declines to consult it.

Post-LayerNorm Transformers have seen limited practical adoption due to enduring difficulties in scaling them to large depths. While prior research has focused on stabilising Post-LayerNorm by improving conditioning at initialisation, stability often deteriorates when models are trained with large learning rates, forcing additional compromises. In the decoder-only settings we study, existing Post-LayerNorm methods fail to outperform strong Pre-LayerNorm baselines. We propose KiteNorm, a novel normalisation method designed to break this trend. KiteNorm achieves stability through residual scaling and a regularisation technique based on hidden-state variance. Beyond stabilisation, KiteNorm learns separate scales for the skip and residual branches in each sublayer, improving performance. Across decoder-only Transformers up to 1B parameters, KiteNorm remains stable throughout training and consistently outperforms leading baselines, with scaling laws favouring KiteNorm across depth, width, batch size, and training budget. Ablations show that each component is necessary for the full stability and performance gains. We offer theoretical insights into KiteNorm through a new perspective on the gradient vanishing problem in Post-LayerNorm, linking the underlying hidden-state expansion to rank collapse.

Nearest neighbor search algorithms are widely used and have a long history, but we find that key implementation details are seldom reviewed in articles about them. These details can materially impact performance, and thus change the conclusions one makes about a given algorithm's superiority. In this article, we highlight these impacts by reviewing the Cover Tree as an example, producing RustKNN, a unified Rust implementation of three construction variants of the Cover Tree with three query modes. Our analysis reveals: (1) the optimal query strategy depends on $k$ and on the dataset — single-tree search is fastest at $k{=}1$ on three of our four largest datasets, while dual-tree search is fastest at $k{\ge}5$ on the three largest — and choosing the wrong query mode can cost up to a $7.5\times$ factor in runtime; (2) the cover tree base parameter, set to 2 in theory and 1.3 in practice, is not a safe default for dual-tree search: 1.3 is optimal on only one of seven datasets, and on covtype it is $1.6\times$ slower than the optimum; (3) among five micro-optimizations, only early-exit distance computation has a consistent effect, and that effect grows with dimension (none at $d{=}32$, $1.31\times$ at $d{=}54$, and $1.49\times$ at $d{=}784$); and (4) all-nearest-neighbor and held-out benchmarks yield different implementation rankings. All main timing tables report medians over 20 independent allocations with bootstrap 95\% confidence intervals. RustKNN is open source at https://github.com/CrowdStrike/RustKNN.

Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative judgment elicits pairwise preferences between outputs. Although both methodologies are widely used, the choice between them is rarely justified. We release JudgmentBench, a benchmark of 30 real-world legal tasks, paired with 1,539 rubric scores and 1,530 pairwise preference judgments collected from practicing attorneys—including at major U.S. law firms—with substantial experience. The annotations constitute the first publicly available dataset in a high-expertise domain in which both supervision signals are elicited from the same experts on the same items. Using LLM-generated outputs at three constructed quality levels, we provide an initial empirical comparison: comparative judgments recover the intended quality ordering substantially better than rubrics (mean Spearman's rank correlation of 0.908 vs. 0.150, $\widehat{D}=0.758$ [0.494, 1.021]) while requiring less than half the annotation time. The patterns hold for human annotators and LLM autograders. Beyond this initial comparison, the paired structure of the dataset supports a broader research agenda on how expert judgment should be elicited, aggregated, and used as supervision in domains without verifiable ground truth.

Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity. This creates a tension: while post-training improves response quality, it also sharpens output distributions and reduces the range of ideas, limiting the usefulness of LMs in creative and exploratory tasks such as brainstorming, storytelling, or problem solving. We address this challenge with Diversity-Aware Reinforcement Learning (DARLING), a framework that jointly optimizes for response quality and semantic diversity. At its core, DARLING introduces a learned partition function to measure diversity beyond surface-level lexical variations. This diversity signal is then combined with a quality reward during online reinforcement learning, encouraging models to generate outputs that are both high-quality and distinct. Experiments across multiple model families and sizes show that DARLING generalizes to two regimes: non-verifiable tasks (instruction following and creative writing) and verifiable tasks (competition math). On five benchmarks in the first setting, DARLING consistently outperforms quality-only RL baselines, producing outputs that are simultaneously of higher quality and novelty. In the second setting, DARLING achieves higher pass@1 (solution quality) and pass@k (solution variety). Most strikingly, explicitly optimizing for diversity catalyzes exploration in online RL, which manifests itself as higher-quality responses.

Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options. However, existing model-free hierarchical reinforcement algorithms need a lot of data. We propose a novel method, which we call "AgentOWL" (Option and World model Learning), that jointly learns --- in a sample efficient way --- an abstract world model (abstracting across both states and time) and a set of neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using less data than baseline methods and possesses learning and generalization capabilities that the baselines do not have.

Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable systems, full per-configuration evaluation and optimization is impractical. In this paper, we formalize the behavioral geometry of a population of models that, by leveraging previously evaluated and defended models, supports both efficient susceptibility prediction and effective defense transfer across a population. We apply the framework to 79 models spanning 24 providers and to 100 system configurations of a single base model. Simple methods that use the behavioral geometry reach an AUPRC of $0.94$ for susceptibility detection with $\approx98\%$ fewer probes relative to a full evaluation. Using the behavioral geometry to select which model to transfer an optimized defense from outperforms same-provider assignment ($+2\%$, $p = 0.03$) at no additional probe cost, with a set of three models sufficient to cover the population. Results are robust to hyperparameter selection and judge.

Offline goal-conditioned reinforcement learning (GCRL) enables goal-reaching from static datasets, but the learned value, critic, or compatibility score must support both long-horizon reachability inference and offline policy extraction. We study iterative latent refinement as an architecture-level replacement for the feedforward value or critic backbone used by existing offline GCRL algorithms. Instead of emitting a score after a single feedforward computation, the network repeatedly updates a latent representation using shared recurrent update weights and lightweight step-specific parameters before exposing the final actor-facing signal. On the recent benchmark datasets reported in this study, recurrent variants improve performance across several algorithms without changing their losses, data, actors, or training protocol; across the 21 reported algorithm-dataset rows, the mean improvement is $10.7$ percentage points, with the largest gains on stitching and bottleneck maze tasks. CRL diagnostics are consistent with an improved policy-extraction interface: recurrent critics increase normalized separation between matched and mismatched goals and produce actors that remain closer to dataset actions under several behavior-support proxies. Depth and update-capacity studies further suggest that the effect depends on how computation is allocated between recurrent depth and per-step expressivity.

Introduced is a methodology for adapting the topology of dense neural networks, enabled by isotropic activation functions. Achieved through prescribed reparameterisation symmetries and singular-value decomposition of affine maps, this diagonalises layers into one-to-one, ordered connections. This makes it simpler to assess the impact of individual connections on the function. Low-impact neurons can be removed (neurodegeneration), and a thresholded buffer of largely inactive 'scaffold' neurons is maintained (neurogenesis). These symmetry-led diagonalisation and structural changes are function-invariant, demonstrated to be computationally identical during neurogenesis, arbitrarily well approximated during neurodegeneration, and enable asymptotic 50% parameter sparsification of dense networks with identically preserved function. Thus, real-time restructuring of the architecture in response to task demands, task appending, removal or changes is shown. The approach is conceptually centred on primitive symmetry-prescriptions, through which isotropic functions are derived that feature explicit basis independence and a loss in the individuation of neurons implicit in typical elementwise functional forms. Hence, this allows freedom in the basis to which layers are decomposed and interpreted as individual artificial neurons, directly enabling this adaptive topology approach. Additionally, a new tunable model parameter, the 'intrinsic length', is introduced to improve this analytical invariance, alongside a generalised isotropic-perceptron architecture that enables parallel precomputation of all matrix-vector products and displays a nested functional class. Diagonalisation is suggested to offer new possibilities for interpretability and monitoring of isotropic networks.

Current sign language recognition and translation benchmarks are commonly collected in controlled settings using fixed or studio-style cameras, particularly limiting their ability to reflect how signers communicate in everyday mobile scenarios. As such, this gap becomes increasingly evident when considering the Arabic Sign Language (ArSL), which is a primary sign language used across Arab countries. In this work, we introduce Isharah-Selfie, a large-scale ArSL dataset collected entirely using front-facing smartphone cameras under unconstrained selfie conditions. The dataset comprises 8,997 video clips performed by 16 signers across 1,493 unique sentences, covering several domains such as healthcare, education, and transportation. Unlike conventional datasets dominated by fixed-camera, two-handed signing, Isharah-Selfie captures realistic mobile communication characterized by one-handed signing, close-range viewpoints, partial body visibility, camera motion, unconstrained environments, and heterogeneous device resolutions. Each video is annotated with both gloss sequences for continuous sign language recognition (CSLR) and Arabic sentence translations for sign language translation (SLT). Moreover, we define signer-independent and unseen-sentences evaluation protocols to assess generalization across unseen signers and novel sentence compositions. Extensive benchmarks using RGB-based and pose-based data show that current CSLR and SLT methods remain challenged by selfie-style signing conditions, particularly under unseen-sentence evaluation, where compositional generalization remains weak. By releasing Isharah-Selfie, we aim to support research toward robust, user-centered sign language technologies that operate reliably in realistic smartphone-based communication settings. The Isharah-Selfie dataset is available on https://www.github.com/.

Humanoid teleoperation datasets are growing rapidly, with operator demonstrations retargeted onto a shared robot skeleton to train whole-body controllers and foundation models. We report a surprising property of this pipeline: retargeting normalizes body proportions but preserves operator movement dynamics (joint velocity profiles, ranges of motion, and coordination patterns shaped by the operator's physiology). On BONES-SEED (522 operators, 142K sequences), retargeted Unitree G1 trajectories support gender classification at 96.0% and operator re-identification at 97.2% Top-1; on operators never seen during training, gender holds at 83.4% and age and height regress within ±4.2 yr and ±5.7 cm. Partial correlation analysis reveals emergent, biomechanically interpretable structure: the signals are task-invariant across activity categories and hold across retargeting implementations. We introduce UNVEIL, a skeleton-aware spatiotemporal graph network to measure and interpret this effect, take initial steps toward operator anonymization, and ask the community: as teleoperation datasets scale, what should our data practices be? Code, models, and anonymized trajectories are at our project website https://project-unveil.github.io.

Inverse reinforcement learning (IRL) is a well-established paradigm for circumventing the need for explicit reward. In this paper, we study the problem of estimating the reward function from a single sequence of actions (i.e., a demonstration) of a stochastic linear bandit algorithm. Our main result is a unified approach for inverse linear bandits, based on the idea of formulating a linear program by tightly characterizing the confidence intervals of pulled actions. We show that the estimation error of our algorithms matches the information-theoretic lower bound, up to polynomial factors in $d$ and $\log T$, where $d$ is the dimensionality of the feature space and $T$ is the length of the demonstration. Compared to prior approaches, our approach (i) gives a unified reward estimator that works when the demonstrator employs LinUCB or Phased Elimination, two popular algorithms for stochastic linear bandits, while existing estimator only works for Phased Elimination; (ii) does not require access to hyperparameters or internal states of the demonstrator algorithm as required by prior work; and (iii) works for general action sets, while existing estimator requires assumptions on the density and geometry of the action set. We further demonstrate the practicality of our new approach by validating our new algorithms on synthetic data and demonstrations constructed from real-world datasets, where our estimators significantly outperform existing ones.

Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input or treated as an answer endpoint rather than part of an interleaved search trajectory. We introduce \textbf{InterLV-Search}, a benchmark for Interleaved Language-Vision Agentic Search, in which textual and visual evidence is repeatedly used to condition later search. It contains 2,061 examples across three levels: active visual evidence seeking, controlled offline interleaved multimodal search, and open-web interleaved multimodal search. Beyond existing benchmarks, it also includes multimodal multi-branch samples that involve comparison between multiple entities during the evidence search. We construct Level 1 and Level 2 with automated pipelines and Level 3 with a machine-led, human-supervised open-web pipeline. We further provide InterLV-Agent for standardized tool use, trajectory logging, and evaluation. Experiments on proprietary and open-source multimodal agents show that current systems remain far from solving interleaved multimodal search, with the best model below 50% overall accuracy, highlighting challenges in visual evidence seeking, search control, and multimodal evidence integration. We release the benchmark data and evaluation code at \url{https://anonymous.4open.science/r/InterLV-Search-F423}