NeurIPS 2026 Papers

We study how to design online optimization loops for molecular optimization via adaptation of pre-trained generative models. At test time, we aim to utilize limited oracle feedback to steer generation toward top-molecules achieving high rewards. This creates a design problem coupled across several dimensions, including which candidates receive oracle evaluations, how observed rewards are utilized for model update, and how to overcome pre-trained model biases–to effectively explore over complex design spaces. Despite recent algorithmic progress on each individual component, it remains unclear how they interact in practice, within real-world online adaptation loops. For instance, top-$K$ optimization objectives may make adaptation overly greedy and thereby reduce exploration, while model-debiasing techniques may be unnecessary when exploration is already induced by exploratory acquisition functions, e.g., Thompson sampling. To address this, we conduct a controlled study on discrete-diffusion molecular optimization, finding that well-designed components remain beneficial when combined, indicating that they tackle complementary issues. Together, these components yield an online fine-tuning recipe that outperforms offline fine-tuning and search-augmented baselines across several small-molecule binding affinity and protein fitness optimization tasks, under equal oracle-call budgets and GPU-hour accounting.

Identifying molecular structures from LC-MS/MS spectra is a central problem in computational metabolomics, often approached by predicting molecular fingerprints and retrieving candidates via similarity search. Despite widespread use, the relationship between training objectives for fingerprint prediction and downstream retrieval performance remains poorly understood. In this work, we show that these objectives are fundamentally misaligned. Adopting a decision-theoretic perspective, we derive novel regret bounds that characterize when Bayes-optimal predictors for fingerprint similarity diverge from those for molecular retrieval. Our analysis reveals that optimizing standard similarity-based losses can provably degrade retrieval performance, and that the extent of this mismatch depends on the similarity structure of candidate sets. Empirically, we validate our theory on the MassSpecGym benchmark, demonstrating a Pareto frontier between fingerprint accuracy and retrieval metrics across commonly used loss functions. These results expose an inherent trade-off in fingerprint-based molecular identification and provide principled guidance for the design of learning objectives in computational mass spectrometry.

Theory of Mind (ToM), the ability to infer others’ knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-point question answering, where performance is judged solely by the final answer to a social reasoning query. This paradigm obscures whether the model actually constructs the underlying mental-state representations required for robust reasoning, particularly in scenarios involving divergent, evolving, or mistaken beliefs. In order to address this research gap, we introduce OmniToM, a benchmark that directly evaluates these representations by requiring explicit modeling of belief structures for all relevant actors within a narrative. These structures are composed of belief propositions: minimal statements of what an actor takes to be true about the world or another actor's mental state, allowing knowledge, intentions, emotions, and false beliefs to be analyzed in a common format. Models are evaluated in two stages: Stage 1: Belief Extraction, which extracts from the story the beliefs relevant to its social dynamics, and Stage 2: Belief Labeling, which assigns each belief a seven-dimensional schema label covering recursive order, truth status, knowledge access, explicitness, content type, mental source, and context. Built from 895 stories from the existing ToMBench story corpus and augmented with 22,343 labeled belief propositions, OmniToM uses a human-calibrated LLM-assisted annotation pipeline. Across diverse models in zero-shot evaluation, OmniToM reveals an actor-specific belief-tracking bottleneck: current LLMs struggle with the knowledge-access and representational decisions required to transform narrative facts into actors’ beliefs and shared mental states.

Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence particularly at high frame rates, limited converged accuracy, and slow inference due to iterative video denoising, as the training supervision is confined to the current chunk without explicit signals about future dynamics. In this paper, we present Next Forcing, a multi-chunk prediction (MCP) framework for causal world modeling that enables faster training, higher accuracy, and accelerated inference. Inspired by multi-token prediction in large language models, Next Forcing introduces an MCP training objective that augments the main model with lightweight auxiliary MCP modules to simultaneously denoise video chunks at multiple future temporal horizons (next$^1$, next$^2$, next$^3$ chunks). These MCP modules form a causal chain across prediction depths, where intermediate features fused from multiple layers of the main model are leveraged to predict future dynamics, allowing near-future predictions to inform farther-future ones and providing dense multi-scale temporal supervision back to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates. At 50 fps, our method achieves a 93.1\% relative improvement over the baseline LingBot-VA at 5k training steps and achieves 2.3$\times$ faster convergence. At inference, the MCP modules can be retained to predict the next video chunk in parallel with the current one, accelerating generation. Next Forcing establishes new state-of-the-art results on the RoboTwin benchmark (94.1\%/93.5\% on Clean/Random) and demonstrates significant improvements on PhyWorld, a benchmark evaluating adherence to physical laws in video generation.

Static-input Spiking Neural Networks (SNNs) commonly create temporal dynamics by repeating the same image over multiple timesteps. For handwritten text recognition, however, this convention is input-redundant: the image contains no native event stream, while recognition depends on faint stroke fragments, degraded ink boundaries, and monotonic left-to-right alignment. We introduce NeuroInk, a retinomorphic spike-oriented recognizer that converts static handwriting into a low-timestep sequence of temporally ordered ink evidence. Instead of replaying identical frames, NeuroInk performs temporal ink transduction, decomposing each text-line image into coarse stroke-mass support and fine stroke-boundary evidence. The resulting evidence sequence is injected as time-varying currents into an adaptive spiking visual hierarchy, where high-frequency enhancement and mid-level detail reinjection are used to preserve fine ink structures during spiking propagation. For CTC decoding, two-dimensional spiking maps are read out as width-wise columnar sequences and processed by CTC-aligned association circuits that combine local horizontal context, global association, and selective trace memory. On the LAM benchmark, NeuroInk achieves 2.4\% CER on the validation split and 2.6\% CER on the test split using only two timesteps and greedy CTC decoding, without an external language model or lexicon. These results support the view that static-input SNNs sequence recognition can benefit from co-designing sensory temporalization, spiking visual abstraction, and monotonic sequence association.

Most recent real-world image super-resolution (Real-ISR) methods employ pre-trained text-to-image (T2I) diffusion models to synthesize the high-quality image either from random Gaussian noise or directly from the input low-quality image. These approaches train ControlNet or LoRA modules while keeping the pre-trained model fixed, which often introduces over-enhanced artifacts and hallucinations, suffering from limited robustness to inputs with varying degradations. Recent visual autoregressive (AR) models, such as pre-trained Infinity, can provide strong T2I generation capabilities while offering superior efficiency by using the bitwise next-scale prediction strategy. Building upon next-scale prediction, we introduce a robust Real-ISR framework with a generation pathway control mechanism, namely Next-Scale Autoregressive Modeling (NSARM). Specifically, we train NSARM in two stages: a transformation network is first trained to map the input low-quality image to preliminary scales, followed by an end-to-end full-model fine-tuning. Such a comprehensive fine-tuning enhances the robustness of NSARM in Real-ISR tasks without compromising its generative capability. Extensive quantitative and qualitative evaluations demonstrate that as a pure AR model, NSARM achieves superior visual results over existing Real-ISR methods, with fast inference speed and comparable fidelity. Most importantly, it demonstrates much higher robustness to varying input quality, showing stronger generalization performance.

Transformer-based autoregressive models offer an efficient alternative to diffusion- and flow-matching-based approaches for generating 3D molecules. One challenge remains: standard transformer architectures require a sequential ordering of tokens, which is not inherently defined for the atoms in a molecule. Prior works have addressed this by using canonical atom orderings. However, these approaches are not permutation invariant w.r.t. atoms and bias next-token prediction towards ordering conventions. We overcome this limitation by introducing a novel neighborhood-guided training strategy. Our model, NEAT (Neighborhood-Guided, Efficient, Autoregressive Set Transformer) treats molecular graphs as sets of atoms and learns an order-agnostic distribution over admissible tokens at the graph boundary, thereby ensuring atom-level permutation invariance. NEAT achieves state-of-the-art generation quality on the QM9 and GEOM-Drugs datasets while offering a significant speed advantage over existing baselines.

Efficient long-sequence modeling remains a central challenge for large language models, as self-attention scales quadratically with sequence length. Mamba offers a linear-time alternative through selective state space recurrence, but its predominantly diagonal state transitions restrict explicit interactions among state dimensions. We propose Motif-Mamba, a structured state space model that augments Mamba with a motif-constrained low-rank recurrent pathway. Inspired by the dynamics of three-node network motifs, the proposed pathway projects hidden states into a compact dynamical subspace, imposes motif-guided interactions, and maps the resulting dynamics back to the original state space. This design enhances cross-dimensional communication while preserving the linear-time recurrent structure of Mamba. Experiments on long-sequence extrapolation, language modeling benchmarks, and brain--computer interface decoding show consistent improvements over Mamba backbones, suggesting that motif-guided low-rank dynamics provide an effective structural prior for long-range sequence modeling.

Untargeted metabolomics produces massive numbers of tandem mass spectra, yet most spectra remain difficult to assign to molecular structures because each spectrum is only a partial, instrument-dependent observation of the underlying molecule. We study MS/MS annotation as modality-incomplete molecular--spectral inference: molecules, spectra, and fingerprints provide complementary views of the same chemical entity, but fully paired molecule--spectrum data are limited and many training examples contain only molecular or only spectral information. We introduce MolSpecFlow, a mask-conditioned heterogeneous flow framework that represents these modalities as coupled components of a shared molecular--spectral state. Availability masks encode which components exist in each example, and observation masks specify which components are conditioned on or generated, instantiating de novo annotation, molecular retrieval, and spectrum simulation within one backbone. MolSpecFlow uses a shared Mol-Spec Transformer, hybrid discrete--continuous flow dynamics, and expected mass/formula regularization over soft molecular token distributions. Experiments show consistent gains from modality-incomplete pretraining, multi-interface reuse, and external de novo transfer.

Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D molecular graphs, existing models suffer from low chemical validity and struggle to meet the desired properties compared to 1D modeling. In this work, we introduce MolHIT, a powerful molecular graph generation framework that overcomes long-standing performance limitations in existing methods. MolHIT is based on the Hierarchical Discrete Diffusion Model, which generalizes discrete diffusion to additional categories that encode chemical priors, and decoupled atom encoding that splits the atom types according to their chemical roles. Overall, MolHIT achieves new state-of-the-art performance on the MOSES dataset with near-perfect validity for the first time in graph diffusion, surpassing strong 1D baselines across multiple metrics. We further demonstrate strong performance in downstream tasks, including multi-property guided generation and scaffold extension.

Variational quantum algorithms offer a promising route to practical quantum advantage on near-term hardware, yet training quantum neural networks (QNNs) remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time–memory cost of differentiating through an $n$-qubit, $L$-layer circuit. We propose RLQ-Grad, a reinforcement-learning-based optimizer in which a classical policy $\pi_\phi$ (a spectrally-normalized PPO agent) learns to propose parameter updates directly, conditioned on the QNN's current parameters, loss, accuracy, and previous update. Because the surrogate gradient is emitted by a classical network rather than obtained by differentiating through the unitary $U(\theta)$, its variance is not constrained by the barren plateau concentration bound, and its per-step cost scales independent of the Hilbert-space dimension. We prove these two properties formally and verify them empirically on a hardware-efficient ansatz across four supervised benchmarks. RLQ-Grad preserves a near-flat gradient-variance curve where backpropagation, parameter-shift, and adjoint differentiation decay by 1–2 orders of magnitude, attains $128\times$–$1841\times$ wall-clock speedups and constant $\sim0.1$ MB memory at $n=12$ qubits, and improves top-1 validation accuracy by up to $+10\%$ over the strongest gradient-based baseline at every circuit scale tested.

Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (MIL) problem, where patch-level features are aggregated into slide-level representations. However, diagnostic and prognostic evidence often arises from spatially coherent tumor microenvironment regions and their interactions, rather than isolated patches alone. Existing patch-level or static region-based methods usually overlook how tissue regions should be adaptively formed and subsequently evolved through microenvironment interactions across heterogeneous boundaries. In this paper, we propose Concept-Guided Tumor Microenvironment Evolution (TMEvolve), a reaction-diffusion-inspired framework that models WSIs as latent tumor microenvironment fields over discrete patch graphs. TMEvolve instantiates this view as a learnable graph-discretized evolution process over patch neighborhoods. It first forms adaptive soft tissue regions as coherent microenvironment units, then performs pseudo-time evolution through two complementary local dynamics: intra-region diffusion, which stabilizes latent states within coherent tissue compartments, and concept-guided boundary flux, which propagates visual feature signals and language-derived concept signals across heterogeneous region interfaces. The evolved microenvironment regions are finally aggregated for slide-level prediction. We evaluate TMEvolve on six datasets across three weakly supervised WSI tasks: survival prediction, gene expression prediction, and histological subtype classification. TMEvolve consistently improves over representative MIL methods, pathology foundation models, and concept-guided baselines. Ablation studies and visualizations further support the effectiveness and interpretability of TMEvolve, highlighting the value of dynamic region modeling and boundary interaction.

Large language models rely on a single tokenizer that is chosen at training time and fixed thereafter. Most tokenizers are trained on English-dominant corpora, making them under-serve morphologically rich and non-Latin-script languages, where they can produce fragmented representations, longer sequence lengths, and reduced information density during training. We propose *Mixture of Tokenizers* (MoTo), a modular tokenization framework that trains dedicated per-language BPE tokenizers and composes them into a unified superset vocabulary, where tokens shared across languages are deduplicated into a common ID space. Each language tokenizer maintains its own normalization policy, pre-tokenization rules, and vocabulary budget. Despite allocating significantly fewer entries to English, MoTo retains English performance while delivering consistent gains across 21 typologically diverse languages spanning six scripts, with improvements most pronounced on morphologically complex and non-Latin-script languages that are systematically underserved by English-dominant tokenizer design. Our results suggest that modular subword tokenization is a practical and extensible alternative to monolithic tokenization.

Recent advances in neural decoding have enabled the reconstruction of 3D visual content from functional magnetic resonance imaging (fMRI) signals, opening a route toward brain-conditioned 3D generation. Nevertheless, existing approaches largely rely on semantic or multi-view priors without explicitly modeling 3D geometric constraints, often leading to structural distortions and degraded geometric fidelity. To address this limitation, we propose MindShape, a geometry-aware neural decoding framework for fMRI-conditioned 3D reconstruction. Unlike previous methods, MindShape decodes superquadric geometry as an explicit, low-dimensional structural constraint and jointly conditions the 3D generation process on both geometric primitives and semantic descriptions, enabling semantically consistent and geometrically faithful reconstructions. Experiments show that MindShape improves reconstruction quality in terms of geometric consistency and semantic fidelity over existing baselines. These results suggest that explicit geometric primitives provide an effective structured interface between noisy neural measurements and controllable 3D generation.

Modeling traffic dynamics is a critical challenge for urban computing, with applications from real-time traffic management to infrastructure planning. However, progress in this area is fundamentally constrained by a lack of large-scale public datasets that capture the subtle properties of real city road networks. Existing benchmarks are often limited by their small scale, reliance on sparse highway traffic sensors, absence of true road connectivity information, and lack of information about road properties. To address this issue, we introduce datasets representing fine-grained road networks of two major cities, which are unique in their scale (up to 100,000 road segments), use of real road connectivity, presence of time series measurements for both traffic speed and volume at a 5-minute resolution, and inclusion of rich static road attributes. These datasets enable in-depth analysis of spatiotemporal traffic patterns and can serve as benchmarks for various ML applications. As a practical demonstration of the utility of our datasets and the challenges they present, we use them for the task of traffic forecasting. The size of the real-world road networks in our datasets reveals significant scalability issues in current traffic forecasting models. To address them, we propose a simple and efficient baseline that not only scales to large road graphs but also achieves forecasting performance competitive with other established spatiotemporal models. We hope that the proposed datasets will serve as a foundational resource for a broad range of research in traffic modeling, urban computing, and smart city development.

Accurately identifying metabolites (small molecules) from mass spectrometry data remains a core challenge in metabolomics, with broad applications in drug discovery, environmental analysis, and clinical research. We address the Molecule Retrieval task, which consists in recovering the chemical structure of a metabolite from its MS/MS spectrum given a set of candidate molecules. While the recent release of benchmark datasets such as MassSpecGym and Spectraverse has considerably accelerated the development of novel machine learning approaches, the complexity of data preprocessing pipelines and the lack of unified implementations make methods and results difficult to reproduce and compare. We make three contributions. First, we propose a unified framework encompassing recent approaches based on representation alignment and contrastive learning. Second, we introduce MSAlign, inspired by multimodal alignment in vision-language models, which learns a shared representation space by aligning two frozen foundation models (DreaMS for mass spectra and ChemBERTa for molecules) through lightweight MLP projections trained with a candidate-based contrastive objective. MSAlign is simple to implement, fast to train and consistently outperforms existing approaches across all benchmarks. Third, we investigate a long-standing evaluation problem: data splitting strategies in molecule retrieval implicitly trade off data leakage against domain shift. We formalize this tension by introducing a quantitative measure of distribution shift, and use it to evaluate splitting strategies in existing benchmarks. All datasets, splits, candidate sets, and a unified implementation of MSAlign and baselines are publicly released to support reproducible research.

Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks for this capability are almost entirely text-only. This omits many optimization-modeling tasks that arise in operational practice, where requirements are described in text but essential instance information is often conveyed through visual artifacts such as tables, graphs, maps, schedules, and planning dashboards. We introduce \emph{multimodal optimization modeling}, a benchmark setting in which models must construct both a mathematical formulation and executable solver code from a text-and-visual problem specification. To evaluate this setting, we develop a solver-grounded framework that generates structured optimization instances, verifies each instance with an exact solver, and builds both the model-facing inputs and the hidden reference files from the same verified source. We instantiate the framework as MM-OptBench, a benchmark of 780 solver-verified instances spanning 6 optimization families, 26 subcategories, and 3 structural difficulty levels. We then evaluate 9 multimodal large language models (MLLMs), including 6 frontier general-purpose models and 3 math-specialized models, with aggregate, family-level, difficulty-level, and failure-mode analyses. The results show that the task remains far from solved: the best two models reach 52.1\% and 51.3\% pass@1, while on average across the six general-purpose MLLMs, pass@1 is 43.4\% on easy instances and 15.9\% on hard instances. All three math-specialized MLLMs solve 0/780 instances. Failure attribution shows that errors arise both when extracting instance data from text and visuals and when turning extracted data into solver-correct formulations and code. MM-OptBench provides a concrete testbed for solver-grounded, decision-oriented multimodal intelligence.

Foundation models for electroencephalography (EEG) have shown promise in learning transferable representations, but existing approaches treat EEG as a generic time series, ignoring the Riemannian geometry of spatial covariance structure that is fundamental to neural signal analysis. We propose MENDR (Manifold-Embedded Neural Data Representations), the first EEG foundation model that builds this geometry directly into its architecture (via the matrix logarithm and Log-Euclidean tangent space) rather than learning it from data. MENDR embeds windowed covariance matrices into the Log-Euclidean tangent space via differentiable matrix logarithm, where standard transformer operations become geometrically equivalent to Riemannian operations. A channel-agnostic spatial projection based on Perceiver-style cross-attention enables seamless transfer across electrode configurations ranging from 6 to 64 channels. Pretrained on the Temple University Hospital EEG Corpus (4,000+ hours, 14,987 subjects) using a masked autoencoder objective in the tangent space, MENDR achieves state-of-the-art results on 3 of 6 downstream benchmarks (TUAB abnormality, TUEV event, CHB-MIT seizure) with only 1.2M parameters, up to $42\times$ fewer than competing foundation models.

Multimodal evidence can improve topic discovery, but dense auxiliary signals can make topics harder to interpret when they replace readable descriptors. This creates an evidence-role problem: symbolic signals should define what topics mean to a reader, while dense aligned signals should help infer which topics a document expresses. We introduce M3-HNTM, a hyperspherical multimodal neural topic model that separates these roles through a shared document-level latent direction. M3-HNTM utilizes a shared von Mises-Fisher (vMF) posterior for document-level topic inference and vMF-mixture symbolic decoders that represent topics as concentration-aware semantic regions. Text Bag-of-Words and speech Bag-of-Acoustic-Words provide reconstructable symbolic evidence, while aligned image-text embeddings enter posterior inference as contextual evidence and help preserve semantic structure. The model applies to image-text, speech-text, and image-speech-text corpora while keeping topics readable through symbolic descriptors. Experiments on five datasets show that M3-HNTM improves the coherence-diversity quality score over strong text-only, hyperspherical, and multimodal baselines. On SpokenCOCO-Tri, the full tri-modal model outperforms both image-text and speech-text variants, indicating that visual and acoustic signals provide complementary evidence for interpretable topic discovery.