NeurIPS 2026 论文列表

Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human language, proteins preserve structure despite extensive sequence variation — a property standard tokenization strategies fundamentally fail to capture. We introduce ZEST (Zoned Encoding of Sequence Traits), an evolution-informed vocabulary derived from conserved regions of multiple sequence alignments. ZEST allows embedding domain-level biological priors directly at the tokenization stage rather than learning them implicitly through scale. ZEST natively compresses sequences to an average token length of 4 residues, enabling our model to process 4,000 residues within a standard 1024-token context window. Building on this, we present LEMON (Layered Extraction of Molecular Ordering from Nature), a compact 200M-parameter sequence-based model for detection of remote homology between protein sequences trained on a single H100 GPU for one week. Despite its modest size, LEMON outperforms state-of-the-art models ranging from 600M to 3B parameters. Our results demonstrate that evolution-informed tokenization can substitute for massive parameter scaling, opening a new direction for efficient, biologically-grounded protein representation learning. All code, model weights, and results are publicly available under the MIT license.

World models have enabled interactive exploration of game environments and robotic manipulation, but physical engineering remains beyond their reach: real materials exhibit nonlinear constitutive laws, carry history-dependent internal state, undergo inertial dynamics, and may possess hierarchical structures spanning multiple length scales. We present LEIA (Learned Environment for Interactive Architected materials), a world model that lets engineers apply boundary conditions step by step and observe the resulting deformation and stress fields in real time. LEIA handles large three-dimensional unstructured meshes and generates autoregressive responses to user-specified loading. We introduce MicroPlate, a benchmark of architected plates spanning two regimes of microstructure modeling: architected lattices that resolve microstructure explicitly through three-dimensional geometry, and a homogeneous plate where microstructural change is modeled implicitly through internal degrees of freedom. MicroPlate is used to assess LEIA alongside four baseline methods across both regimes. Finally, we demonstrate that LEIA enables efficient candidate generation and ranking for fast surrogate-guided search for de novo designs of architected materials, with stress-accurate candidate ranking validated by finite element ground truth.

We propose LEGO, a budget-aware Dense-to-MoE methodology that converts pretrained vision-language models into efficient FFN-MoE variants under a target architectural budget $((P_{total},P_{active}))$. Our study is motivated by a counter-intuitive recovery pattern: fixed activation ratios that work well for some dense backbones can degrade sharply on others under the same recovery recipe, indicating that raw sparsity alone is an unreliable design rule. Through controlled recovery experiments across VLM families and scales, we identify the activated-FFN-to-hidden ratio $(D_I/D_H)$ as a backbone-comparable structural factor and summarize empirical sizing rules for avoiding severe bottlenecks while limiting diminishing returns. LEGO operationalizes these rules through a discrete budget-aware search over Split, Upcycle, and Hybrid constructions, and introduces a moment-matching scaling factor to reduce the initialization scale shift caused by sparse FFN aggregation. Finally, we use a two-stage multimodal recovery recipe that first stabilizes the sparse LLM with the vision tower frozen, then performs joint fine-tuning to recover the full VLM. Under matched data and training protocols, LEGO improves Dense-to-MoE recovery quality and turns costly blind architecture search into a small set of rule-guided candidates. Codes can be found in supplementary materials.

Despite the remarkable progress of evolutionary LLM frameworks like AlphaEvolve in program and algorithm discovery, they consistently struggle with tasks requiring deep algorithmic reasoning and long-horizon planning, such as the Abstraction and Reasoning Corpus (ARC-AGI). A fundamental limitation of current evolutionary code generation is its reliance on unguided stochasticity to derive child programs from parents, lacking a systematic mechanism for knowledge accumulation, whereas human problem-solving thrives on the continuous consolidation and reuse of high-level conceptual abstractions. To bridge this gap, we propose the \textbf{Library-driven Evolutionary Abstraction Paradigm (LEAP)}, a cognitive-inspired framework that enables LLMs to autonomously construct and evolve a globally shared library of reusable algorithmic primitives. The evolution of this library is governed by two rigorous theoretical principles: First, the generation of new primitives is driven by a Minimum Description Length (MDL) objective, ensuring the system only assimilates concepts that genuinely compress the problem space. Second, the evolutionary survival and selection of these primitives are governed by a principled exploration-exploitation mechanism, dynamically regulating the library's life-cycle to strike an optimal balance between exploiting proven cognitive operators and exploring novel hypotheses while strictly bounding context entropy. Extensive experiments demonstrate that LEAP significantly outperforms existing LLM evolutionary baselines on the ARC-AGI benchmark and math optimizations problems, exhibiting robust cross-task generalization, highly efficient context utilization, and autonomous concept discovery capabilities.

We consider the problem of learning to adapt a foundation model in a federated setting, particularly the most realistic and general setting: 1) When the local data sets are sampled from different distributions but we want to learn a globally adapted model, 2) Where local agents enter and leave the federation asynchronously at each time tick which is beyond the control of the learning algorithm, and 3) Where the goal is continuous adaptation so that after each time tick, the learned adapter generalizes accurately for all participants that have been seen during training. We propose a simple idea called federated *library-based adaptation* (*LEAN*) for this setting. In library-based adaptation, the system maintains a pool, or "library" of rank-1 "basis pairs". Groups of these pairs are distributed to local sites to be trained in LoRA fashion, and then separated and *shuffled* at communication. Library-based adaptation is designed to avoid problems with more conventional methods, such as those based on averaging. In particular, we demonstrate *LEAN* outperforms traditional averaging baselines in both communication and computation cost efficiency across a broad range of important settings, including heavy data skewness and high asynchrony.

Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated Chain-of-Thought (CoT) trajectories often exceed what the underlying problems require, wasting compute, latency, and context budgets. While introducing length-based efficiency rewards during reinforcement learning offers a natural remedy, existing methods struggle with two fundamental challenges: the optimal balance between correctness and efficiency is non-stationary throughout training, and intrinsic reasoning budgets vary drastically across problems. Relying on static reward weights and global length constraints inevitably forces a compromise between degraded accuracy and unrealized compression. To overcome these limitations, we propose LEAD (Length-Efficient Adaptive and Dynamic reasoning), a method that replaces static heuristics with online, self-adaptive mechanisms. LEAD dynamically calibrates the correctness-efficiency trade-off at each step using a Potential-Scaled Instability, directing optimization capacity to the most informative learning signal. Furthermore, it estimates an adaptive per-problem target length online based on the model’s own correct rollouts, applying a symmetric efficiency reward that penalizes both overthinking and over-compression. Evaluated on five mathematical reasoning benchmarks, LEAD achieves the highest accuracy and Accuracy-Efficiency Score among RL-trained efficient-reasoning methods while producing substantially shorter outputs than the base model.

The Model-as-a-Service (MaaS) paradigm enables resource-constrained edge users to access cloud-based large language model (LLM) services but raises significant privacy concerns. Existing approaches perturb user queries under local differential privacy (LDP) before sending them to LLMs. However, naively applying these methods to multi-query scenarios leads to linear growth in privacy budget consumption. To address this, we propose LDPCache, the first cache-enhanced LDP framework for multi-query LLM processing. By leveraging semantic correlations across consecutive user queries, LDPCache maintains a cache of historical responses and intelligently decides whether to reuse a cached result or query the cloud-based LLM, thereby reducing privacy budget consumption. The core of LDPCache is an LDP-compliant scheduler that evaluates cache reusability based on both semantic similarity and the expected accuracy gains from LLM access. This scheduler enables hit decisions without actual LLM access while ensuring that the scheduling process itself satisfies LDP guarantees. To accommodate the limited memory of edge devices, we further design a multi-factor cache eviction strategy that balances query diversity, response accuracy, and recency to improve the hit ratio. Extensive experiments on real-world datasets demonstrate that LDPCache significantly improves response accuracy over baseline methods.

Random Feature Models (RFMs) are attractive PDE surrogates because their linear-in-features form reduces training to regularized least squares. Standard RFMs, however, use one stationary spectrum over the whole domain, creating a bandwidth dilemma on heterogeneous PDE solutions: fine scales resolve sharp local gradients but oscillate in smooth regions, while coarse scales miss interface-dominated derivatives. We propose \textbf{LDD-RFM}, a learnable domain-decomposition framework whose local experts remain linear RFMs and whose nonlinear variables specify compact-support partition-of-unity (PoU) gates and local spectral scales. For fixed gates and spectra, expert coefficients are eliminated by ridge regression through variable projection, yielding a differentiable reduced objective with explicit linear-solve and partition-smoothness controls. We prove an $H^1$ error decomposition in which PoU sharpness enters through a product-rule term. Controlled regression and PDE experiments show that LDD-RFM reduces $H^1$ errors by $10\times$ over global and fixed-partition RFM baselines under matched feature budgets; exact-solver, timing, and robustness studies check that these gains are not artifacts of the linear solver or evaluation protocol.

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.

Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain–computer interfaces, but remains challenging due to low signal-to-noise ratios and high inter-subject variability. As a result, many existing approaches rely on subject-specific models, which fail to exploit shared structure in neural signals and do not generalize to unseen subjects. To address these limitations, we propose LAtte, a framework that combines Lorentz attention with a hyperbolic InceptionTime-based encoder to improve cross-subject generalization in EEG classification. The model explicitly decomposes EEG signals into a learned baseline component and task-relevant deviations, enabling more structured representation learning. To further improve robustness and adaptability, we incorporate subject-specific low-rank adaptation (LoRA) modules at both encoder and decoder levels, augmented with a Lorentz boost–based LoRA mechanism and hyperbolic projection layers to reduce overfitting in geometric representations. We evaluate LAtte with and without finetuning in three settings: subject-specific, subject-conditional, and leave-one-subject-out (LOSO) on five established EEG datasets, achieving a consistent improvement in performance over current state-of-the-art methods for smaller datasets and maintaining performance for larger datasets.

Long-context inference in Large Language Models (LLMs) suffers from high latency because attention computation and memory traffic scale linearly during decoding. KV cache pruning reduces this cost by retaining only a subset of cached tokens, but its effectiveness depends on whether the pruning proxy can recover the tokens with the largest exact attention logits under the current query. Existing proxies suffer from ranking misalignment: static heuristics rely on historical statistics and thus ignore the current query, while hashing-based retrieval proxies rank candidates in a discrete metric space that does not coincide with attention's inner product. To avoid these problems, we propose LAPrune, a KV pruning framework that uses additive vector quantization to build an efficient attention-logit-aligned proxy. LAPrune represents each cached key as a sum of learned codewords and decomposes the query-key dot product into lookup-and-add operations over query-codeword inner products. This replaces high-dimensional dot products with lightweight scalar lookups while preserving fidelity to the original attention ranking. LAPrune requires no base-model finetuning and can be integrated into long-context decoding with minimal modification. Across long-context evaluations, LAPrune improves exact-logit Top-$k$ recovery and perplexity over hashing-based pruning proxies, achieving up to $2.73\times$ decoding speedup over FlashAttention-2 and $4.83\times$ end-to-end speedup over the vanilla baseline at 128K context. Our code is available at [Anonymous/LAPrune](https://anonymous.4open.science/r/LAPrune-VQ).

Fast linear algebra in deep learning usually comes with a choice: fixed geometry and exact computation, as in the Fourier transform, or adaptive geometry paid for by dense parameters, random features, or low-rank surrogates. To move beyond this trade-off, we introduce LAPLEX, a class of exact, trainable (phased) Laplace-kernel operators. A LAPLEX layer is a typically full-rank dense matrix, implicitly defined by learnable coordinate anchors, with FFT-like scaling. Consequently, it supports trainable matrix--vector operations at vector dimensions up to $10^9$ on modern GPUs. As a neural layer, it yields compact projections and classification heads interpretable as soft, trainable routing models. The same primitive also serves as an efficient Gram operator, enabling high-dimensional covariance models on flattened images of dimension $3 \cdot 10^6$ that preserve visible spatial structure without imposing convolutional bias. These applications reflect a single principle: dense geometry can be learned without storing a dense matrix, which enables data-adaptive global interactions in regimes where ordinary dense layers are out of reach. In this sense, LAPLEX separates expressivity from storage cost: it behaves like a dense trainable matrix, but is represented and applied through a small structured set of parameters.

Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article addresses the floating-point computation of compositionally-rich functions, concentrating on transformer inference. Based on the rounding error analysis of a composition $f(g(x))$, we provide an adaptive strategy that selects a small subset of components of $g(x)$ to be computed more accurately while all other computations can be carried out with lower accuracy. We then explain how this strategy can be applied to different compositions within a transformer and illustrate its overall effect on transformer inference. We study the effectiveness of this algorithm numerically on GPT-2 models and demonstrate that already very low recomputation rates allow for improvements of up to two orders of magnitude in accuracy.

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

Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder, has emerged as a promising paradigm for scalable latent policy learning. However, existing approaches typically rely on behavior cloning, which tends to collapse inherently multimodal action distributions into unimodal ones, thereby degrading the pretrained latent action structure. While flow matching provides a potential alternative, directly applying it leads to a misalignment between latent actions and physical actions during action decoder training, due to the stochastic nature of the learned policy. To address these, we propose Latent Action Flow Policy (LAFP), which leverages flow matching for latent policy learning and introduces an inference-time interpolation mechanism to mitigate stochasticity-induced misalignment. Experimental results demonstrate that LAFP consistently outperforms prior methods on downstream imitation learning tasks, achieving up to 10–15\% improvement in success rate while incurring less than 1× additional inference overhead.

Reinforcement learning (RL) has become central to post-training large language models, but its rollout generation stage is often the dominant system bottleneck. The inefficiency comes from two sources. First, autoregressive decoding makes latency grow with response length. Second, response lengths vary widely within a batch: short sequences finish early, but the batch remains blocked by the longest sequence, leaving GPU capacity underutilized. This effect is amplified in multi-sample rollouts, where long responses can also concentrate on a few decoding workers and delay synchronization. We propose LADDERS (Length-Aware Data Distribution and Existing-Response Speculation), a lightweight framework for accelerating RL rollout generation. LADDERS first uses hidden-state-based length prediction to group prompts with similar expected response lengths, and then applies an S-shaped allocation rule to balance multi-sample requests across workers. It further reuses responses already generated by the policy as draft continuations through prompt-specific suffix trees, enabling speculative decoding without an auxiliary draft model. Experiments on Qwen3-1.7B and Qwen3-8B show that LADDERS reduces rollout generation time by up to 56% while preserving final task performance, and can be integrated into existing RL systems with minimal engineering effort.

Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. By repurposing the model architecture to enable cross-thread attention, LACE allows concurrent reasoning paths to share intermediate insights and correct one another during inference. A central challenge is the absence of natural training data that exhibits such collaborative behavior. We address this gap with a synthetic data pipeline that explicitly teaches models to communicate and error-correct across threads. Experiments show that this unified exploration substantially outperforms standard parallel search, improving reasoning accuracy by over 7 points. Our results suggest that large language models can be more effective when parallel reasoning paths are allowed to interact.

Despite rapid AI adoption in education, rigorous evaluation of AI-powered educational (AIED) systems remains critically underdeveloped, particularly in second-language (L2) education, one of the most common yet least evaluated AI applications. We introduce L2-Bench, an open-source benchmark of 1,000+ task-response pairs to aid the pedagogy-led evaluation of LLM capabilities relating to language learning and assessment. Crucially, L2-Bench measures model performativity on the application of learning experience design principles rather than mere knowledge of those principles or broad learning outcomes. Our contributions include: (1) a validated taxonomy of 12 competencies and 31 subcompetencies validated by 200+ expert practitioners (task authenticity = 4.42/5.00, criteria adequacy = 4.18/5.00); (2) a rubric-based evaluation methodology that we believe can, if adapted, generalize to similar (open-ended, qualitative) disciplines; (3) an evaluation dataset that produces reliable signal about model strengths, weaknesses, and contextual robustness across diverse L2 education scenarios. We find that, among large models, Claude Opus 4.7 performs best overall (85.5\%), though is marginally outperformed on several constituent tasks. We also find that performance drops notably on harder tasks $\in$ (69.9\%–73.4\%). L2-Bench provides education stakeholders better methods to make more informed decisions about real-world AIED adoption, use, and governance, while advancing the maturing science of AI evaluations for education.

Speech is an increasingly promising biomarker for neurodegenerative disorders due to its non-invasive nature, low cost, and suitability for frequent, longitudinal monitoring. However, existing clinical speech datasets are typically small, sparse, and irregularly sampled, limiting the ability to model continuous disease progression and develop robust biomarkers. We propose L-Flow, a progression-aware conditional speech generation framework for longitudinal speech trajectory completion. Given a patient’s speech recordings, timestamps, and clinical progression labels, L-Flow learns a query-specific progression representation to synthesize speech at intermediate time points within the observed recording span. Experiments on two real-world longitudinal speech datasets demonstrate that L-Flow generates synthetic speech with stronger progression consistency than eight baseline augmentation methods. In addition, incorporating L-Flow–augmented data improves downstream regression performance to predict clinical severity, highlighting its utility for speech-based biomarker development.