NeurIPS 2026 Papers

State-of-the-art Extreme Multi-Label Text Classification (XMC) models rely on multi-label attention to focus on key tokens in input text, but learning high-quality attention weights is challenging. We introduce PLANT (Pretrained and Leveraged Attention), a plug-and-play strategy for initializing attention. PLANT works by \emph{planting} label-specific attention using a pretrained Learning-to-Rank model guided by mutual information gain. This architecture-agnostic approach integrates seamlessly with large language model backbones such as Mistral, LLaMA, DeepSeek, and Phi-3. PLANT outperforms state-of-the-art methods across tasks such as ICD coding, legal topic classification, and content recommendation. Gains are especially pronounced in few-shot settings, with substantial improvements on rare labels. Ablation studies confirm that attention initialization is a key driver of these gains. Code and trained models are available at \url{https://github.com/research-anon-487/xcube/tree/plant}.

Flow cytometry is the primary assay for single-cell immune profiling, yet cross-study modelling remains under-explored. The challenge is two-fold: Studies measure different marker panels, inducing feature-space inconsistency, while batch effects shift signals even on shared markers, making transfer harder than in vision or language where input spaces are stable. We conduct a large-scale empirical study, pretraining an encoder on 89,324 samples from 112 public studies spanning 525 distinct marker panels. We evaluate it frozen --- isolating representation quality from task-specific adaptation, on five clinical tasks under a taxonomy of panel-overlap conditions, including three panel-zero-shot tasks whose marker panels were unseen during pretraining, and one negative control with no expected discriminative signal. We further characterise transfer across scaling axes and probe robustness to marker removal at test time. Despite heterogeneity that disrupts direct transfer, the frozen encoder matches or exceeds in-distribution specialist encoders on 4 of 5 tasks, including all three novel-panel tasks, and behaves as expected on the negative control. Scaling analyses reveal saturation in both encoder capacity and random corpus subsampling, and representations degrade gracefully under marker reduction. These results suggest a pretrained encoder can serve as a general-purpose feature extractor for clinical cytometry data, without task-specific retraining.

Predicting gene expression under perturbations from single-cell data is a fundamental task for modeling cellular responses, with applications in discovering gene function and designing therapeutics. However, existing methods often struggle to generalize across datasets due to differences in experimental protocols, biological contexts, and species. We present \textbf{CytoWave}, a perturbation-centric pretraining framework for single-cell genetic perturbation response prediction. We leverage large-scale cross-species perturbation datasets unified via orthology and train CytoWave in a two-stage manner. In the first stage, we learn reference control cell states via masked gene expression recovery on unperturbed cells. In the second stage, we pretrain the model to predict perturbed gene expression given control states and perturbation signals, together with a contrastive objective that aligns latent representations of perturbation responses with perturbation signals. We perform strict dataset-held-out evaluation across multiple condition combinations and case studies of pathway activity changes, demonstrating consistent and effective performance of CytoWave in predicting single-cell genetic perturbation responses.

This paper concerns the problem of scaling agents to large action spaces demanding pretrained world knowledge. What type of representation pretraining would enable policies such as language models to generalize rewards to appropriate actions? Contrastive learning aligns similar inputs and otherwise enforces distancing of representations, while non-contrastive alternatives omit distancing terms and so learn anisotropic representations that collapse distinctive clusters. Though contrastive pretraining has been proven to promote supervised learning efficiency, non-contrastive pretraining nevertheless remains canonical for language agents. Here we examine the extent to which these pretraining types support on-policy finetuning, in which efficiency is coextensive with online performance. Broadly, we argue that contrastive pretraining optimizes efficient exploration of large spaces. We prove that anisotropic representations collapse distinct actions and necessitate complex policies, whereas contrastive pretraining retains distinctions and enables reward transfer through spectral regularization. We validate our conclusions empirically by providing spectral analyses of language data and simulations involving multimodal personalization policies as well as self-improving reasoning agents finetuned online using a policy gradient. Our latter simulations suggest contrastive pretraining for language agents exploring diverse reasoning paths.

Despite the increasing scale of genome language models (gLMs), their ability to decode the function of regulatory sequences remains unclear. gLM pretraining relies on sequence reconstruction, which may struggle due to the noisy, rapidly evolving nature of regulatory DNA. Self-supervised contrastive approaches provide a promising alternative. Inspired by language-image architectures like CLIP, we demonstrate contrastive promoter-protein pretraining (C3P). By learning to align promoters to their corresponding proteins, we leverage the rich representations of proteins learned by protein language models as supervisory signal for the learning of promoter representations. After training on 88 million bacterial promoter-protein pairs, we evaluate the predictive power of C3P-learned promoter representations for inference of curated regulatory annotations, finding multi-fold improvement over leading gLMs. We also introduce zero-shot co-regulated gene retrieval, the ability to find co-regulated genes in a genome using no experimental data. We find that compared to a randomly initialized baseline, C3P training consistently provides significant zero-shot performance gains, unlike gLMs. Scaling analysis reveals the potential for further improvement as well as the efficiency of C3P, which achieved strong performance at a fraction of the training cost of leading gLMs. In addition to demonstrating that C3P training is effective for learning representations of bacterial regulatory sequences, our strong zero-shot co-regulated gene retrieval performance suggests the possibility of decoding gene regulation for millions of bacteria from their genomes alone.

The recent development of Large Language Models (LLMs) has been accompanied by an effervescence of novel ideas and methods to better optimize the loss of deep learning models. Claims from those methods are myriad: from faster convergence to removing reliance on certain hyperparameters. However, the diverse experimental protocols used to validate these claims make direct comparisons between methods challenging. This study presents a comprehensive evaluation of recent optimization techniques across standardized LLM pretraining scenarios, systematically varying model size, batch size, and training duration. Through careful tuning of each method, we provide guidance to practitioners on which optimizer is best suited for each scenario. For researchers, our work highlights promising directions for future optimization research. Finally, by releasing our code and making all experiments fully reproducible, we hope our efforts can help the development and rigorous benchmarking of future methods.