NeurIPS 2026 论文列表

Designing DNA libraries is a key challenge from drug design to protein engineering and synthetic biology. Modern generative models offer opportunities to navigate the design space and propose specific sequences predicted to be effective in-silico. Designing deterministic libraries of specific sequences is however limited by the cost of DNA synthesis -- the synthesis barrier. In contrast, high-throughput multiplexed screening can measure the function of billions of biological sequences in parallel. Harnessing this technology requires the design of randomized libraries with specific design constraints to achieve low synthesis costs. In practice, such stochastic libraries are often chosen heuristically, sacrificing control for scale. Is there a way to bridge AI-based in-silico sequence design with high-throughput experimentation? In this work, we introduce Policy Gradients for Library Design (PGLD). PGLD uses a synthesis-aware parametrization of stochastic DNA libraries and optimizes them against a specified objective function. This allows for designing massive, controlled libraries without being limited by synthesis costs. We show how PGLD enables lab-in-the-loop design of multi-round high-throughput experiments, and large-scale in-vitro DNA sampling from generative models. Finally, we use PGLD to design a library of $\sim 10^6$ unique sequences at a cost of $\sim 700$ USD to explore the mutation space of a broadly neutralizing influenza antibody.

Omni-modal caption models equipped with precise timestamp grounding are crucial for fine-grained video understanding and temporally controllable video generation. However, existing open-source models, and even proprietary systems, struggle to provide fine-grained timestamp grounding for short video captioning. To bridge this gap, we present **Deci-Omni-Captioner**, an advanced open-source omni-modal captioning framework designed for sub-second timestamp precision. During the Supervised Fine-Tuning (SFT) stage, we leverage Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) models to strictly calibrate visual text and spoken dialogue boundaries, offering highly deterministic temporal anchors to endow the model with sub-second timestamp grounding capabilities. Furthermore, we curate a specialized dataset for multi-reward Reinforcement Learning (RL) and propose Span-Specific Credit Assignment (SSCA). Unlike conventional global advantage normalization, which dilutes reward signals across weakly coupled multimodal descriptions, SSCA calculates advantages independently for different structural spans, effectively isolating penalties and rewards across semantic and temporal dimensions. Extensive experiments demonstrate that Deci-Omni-Captioner establishes state-of-the-art (SOTA) performance among open-source models on multiple semantic comprehension benchmarks (e.g., AVUT, UGC, VCapsBench). On our challenging Deci-Timestamp Bench, it significantly surpasses leading proprietary models, including the Gemini 2.5 and 3.0 series.

Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-data regime: improving data quality typically comes at the cost of increased memorization of training samples, thereby weakening privacy protection. This tradeoff arises because small training sets make it difficult for dataset-specific generative models to distinguish generalizable structure from sample-specific patterns. To address this, we propose DiffICL, which formulates tabular data generation as an in-context learning problem. Instead of fitting each dataset from scratch, DiffICL leverages pretrained structural priors learned from a large collection of datasets, enabling it to infer data distributions from limited context rather than memorizing individual samples. We evaluate DiffICL on 14 real-world datasets. Results show that DiffICL improves both data quality and privacy, and generates synthetic data that provides effective data augmentation. Our findings suggest that the quality–privacy tradeoff can be improved through better training paradigms.

Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle with the curse of dimensionality in such large discrete action spaces. We propose Distance-Guided Reinforcement Learning (DGRL), combining Sampled Dynamic Neighborhoods and Distance-Based Updates to enable efficient RL in problems with up to $10^{20}$ actions. Unlike prior methods, DGRL performs stochastic volumetric exploration and transforms policy optimization into a stable regression task, decoupling gradient variance from action space cardinality. On structured tasks, DGRL provably guarantees local value improvement.DGRL naturally generalizes to hybrid continuous-discrete action spaces. We demonstrate performance improvements of up to 66% against state-of-the-art benchmarks across regularly and irregularly structured environments, while simultaneously improving convergence speed and computational complexity.

While standard reinforcement learning optimizes a single reward signal, many applications require optimizing a nonlinear utility $f(J_1^\pi,\dots,J_M^\pi)$ over multiple objectives, where each $J_m^\pi$ denotes the expected discounted return of a distinct reward function. A common approach is concave scalarization, which captures important trade-offs such as fairness and risk sensitivity. However, nonlinear scalarization introduces a fundamental challenge for policy gradient methods: the gradient depends on $\partial f(J^\pi)$, while in practice only empirical return estimates $\hat J$ are available. Because $f$ is nonlinear, the plug-in estimator is biased ($\mathbb{E}[\partial f(\hat J)] \neq \partial f(\mathbb{E}[\hat J])$), leading to persistent gradient bias that degrades sample complexity. In this work we identify and overcome this bias barrier in concave-scalarized multi-objective reinforcement learning. We show that existing policy-gradient methods suffer an intrinsic $\widetilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity due to this bias. To address this issue, we develop a Natural Policy Gradient (NPG) algorithm equipped with a multi-level Monte Carlo (MLMC) estimator that controls the bias of the scalarization gradient while maintaining low sampling cost. We prove that this approach achieves the optimal $\widetilde{\mathcal{O}}(\epsilon^{-2})$ sample complexity for computing an $\epsilon$-optimal policy. Furthermore, we show that when the scalarization function is second-order smooth, the first-order bias cancels automatically, allowing vanilla NPG to achieve the same $\widetilde{\mathcal{O}}(\epsilon^{-2})$ rate without MLMC. Our results provide the first optimal sample complexity guarantees for concave multi-objective reinforcement learning under policy-gradient methods.

This work proposes the Adaptive Federated Hamiltonian Monte Carlo (AFHMC) for Bayesian federated learning (FL) tasks. The prior work on HMC applications for FL achieves $O(\sqrt{d}/\epsilon)$ communication cost under log-convex distributions, already demonstrating its advantage over the Langevin dynamic-based counterpart. Based on a refined analysis, this paper further reveals the benefit of HMC for the FL regime by incorporating a control mechanism for local node shifts. We prove that AFHMC at most requires $O((d/\epsilon^2)^{1/3})$ communication cost up to a logarithmic term, which is significantly better than the existing results. Moreover, the proposed method adapts to client heterogeneity. Under low-heterogeneity settings (i.e., the target sampling precision $\epsilon^2$ is higher than the heterogeneity level), the communication rate of AFHMC can be as good as $O(\log(d/\epsilon^2))$, resembling the communication costs for FL optimization tools. To the best of our knowledge, this provides the first logarithmic communication complexity result for federated sampling. Our code is available at https://anonymous.4open.science/r/Adaptive-Federated-HMC.

Self-supervised learning (SSL) is increasingly applied to scientific imaging domains, such as microscopy, where labels are scarce but noisy data is abundant. These domains exhibit significant signal-dependent noise, which can create a representational shortcut: since both augmented views derive from the same noisy image, the encoder can boost view agreement by also encoding noise patterns rather than semantic content. To enable noise-robust representation learning without knowledge of underlying noise models, we introduce Self-Aligned Noise Augmentation (SEANA), a drop-in module that generates noise-aware SSL views. SEANA learns an invertible variance-stabilizing transform (VST) from noisy data, then estimates the clean signal and resamples independent noise in learned VST space, requiring no clean targets, no dataset-specific denoiser training or use at inference, and no changes to the SSL objective or encoder. On CIFAR-10 and ImageNet-100 under synthetic Gaussian, Poisson–Gaussian, and multiplicative noise, SEANA improves clean-test linear-probe accuracy across contrastive/non-contrastive SSL methods, indicating higher-quality representations. On real fluorescence microscopy, SEANA improves Jurkat cell-cycle stage classification by up to 28.6pp over standard SSL and 27.5pp over denoiser-preprocessed baselines. In both settings, SEANA outperforms the denoiser-preprocessed SSL pipeline, demonstrating that learned VST-space noise-aligned resampling yields more robust representations than denoising alone. Code will be made publicly available.

Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but also on the feature-interaction topology induced by the sparse update. Limited or uneven cross-dimensional interactions may isolate subsets of feature dimensions, forming information islands that hinder task-specific information mixing. To address this issue, we propose **S**mall-**W**orld **I**nduced **F**ine-**T**uning (**SWIFT**), a sparse tuning framework that induces small-world connectivity in parameter space. SWIFT combines block-diagonal sparse updates for local feature interactions with a fixed sufficiently scattering permutation that routes these updates to distant feature groups, creating sparse long-range shortcuts without additional trainable parameters. We further show theoretically that random or block-diagonal sparse tuning can suffer from information islands, whereas SWIFT restores positive expansion through permutation-induced routing. Extensive experiments on commonsense reasoning, natural language generation, and image classification demonstrate that SWIFT consistently outperforms competitive PEFT baselines and can match or surpass full fine-tuning.

Neural surrogate solvers of partial differential equations (PDEs) promise dramatic speedups over numerical methods, especially in scenarios requires many solves. However, current accuracy-based evaluations do not fully consider two central issues: (i) neural solvers incur substantial up-front costs for data generation, training, and tuning; and (ii) classical solvers can also generate low-fidelity solutions at a sufficiently low simulation cost. To explicitly account for these realities and fully incorporate end-to-end costs, we propose an evaluation framework centered on **breakeven complexity**, a metric that counts the forward solves before a learned solver is cost-effective relative to an error-equivalent traditional solver. To evaluate this measure, we apply scaling laws to determine how much training budget to allocate to data generation and discuss how to achieve smooth error-matching in diverse settings. We evaluate the breakeven complexity of many modern neural PDE solvers on three PDEs on 2D periodic domains from APEBench and a novel benchmark of flows past multiple obstacles generated by the GPU-native PyFR code. Among other findings, our results suggest that neural PDE solvers will be *more effective* on problems as problems get harder in terms of cost, dimension, rollout, physics regime (e.g. higher Re), etc.

LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk: web content retrieved during execution is untrusted, exposing agents to prompt injection and goal hijacking. Prior work on search-agent safety primarily focuses on static web-content injection, but modern agents issue follow-up queries and cross-check competing sources, so a single injected page is often diluted or rejected. We show that the channel delivering search and page observations is a fragile security boundary: beyond exposing the agent to a single poisoned page, a mediated search interface can repeatedly steer how the agent gathers evidence and forms its final answer. Under a constrained tool-intermediary threat model, appending only one controlled result per query can substantially increase attack success when the evidence is coordinated across the agent's trajectory. We study this setting with a strategy-driven long-horizon attack system and introduce Authority-Chain Hijack (ACH), an expert-refined strategy that turns isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources. ACH achieves the highest Overall ASR among all baselines, reaching 56.0\%/85.0\% ASR/MaxN\,ASR on the SafeSearch benchmark. We further introduce Trace-Guided Strategy Evolution (TGSE), which automatically improves reusable attacker strategies from execution traces, replacing manual redesign with trace-driven refinement and further raising these to 71.4\%/95.0\%.

Diffusion and flow matching approaches to generative modeling have shown promise in domains where the number of elements in a state is fixed in advance (e.g. images), but require *ad hoc* solutions when, for example, the length of a response from a large language model, the number of atoms in a molecule, or the number of amino acids in a protein chain is not known *a priori*. Here we propose Branching Flows, a generative modeling framework that, like diffusion and flow matching approaches, transports a simple distribution to the data distribution. But in Branching Flows, the elements in the state evolve over a forest of binary trees, branching and dying stochastically with rates that are learned by the model. This allows the model to control, during generation, the number of elements in the sequence. We show that Branching Flows can compose with any flow matching base process on discrete sets, continuous Euclidean spaces, Riemannian manifolds, and "multimodal" product spaces that mix these components, and we demonstrate distribution matching on small molecules and antibody sequences, and that this scales to complicated domains such as protein structures.

Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream prediction rather than conditional predictive generation. We introduce BrainWorld, a structural-prior-conditioned generative model for whole-brain 4D fMRI dynamics. BrainWorld uses sMRI as subject-level anatomical context to guide future fMRI generation, integrating structural information into the denoising process rather than treating it as a parallel modality. Evaluated on 22 datasets spanning diverse cohorts and brain states, BrainWorld generates stable 4D fMRI trajectories up to 400 frames, improves downstream performance through generated-example augmentation, and learns transferable multimodal representations that outperform baselines. Together, these results establish BrainWorld as a condition-aware generative framework for long-horizon brain dynamics modeling and multimodal representation learning.

Decoding continuous speech from intracortical recordings holds transformative potential for individuals with severe motor impairments, yet current systems face fundamental barriers to real-world deployment: training data is scarce, neural signals are non-stationary across sessions, and the external language models required by state-of-the-art cascaded decoders impose memory requirements that preclude local, privacy-preserving inference. We introduce BrainWhisperer, a neural speech decoder that adapts Whisper–pretrained on approximately 680,000 hours of speech–to microelectrode array recordings via a convolutional front-end, hierarchical low-rank projections for non-stationarity, windowed self-attention in phoneme-selective encoder layers, and a multi-task objective combining CTC and cross-entropy losses. Subject-specific embedders enable cross-participant training within a unified architecture. Evaluated on publicly available Utah array datasets from BrainGate participants, BrainWhisperer achieves a word error rate of 8.5\% in end-to-end decoding on the Card benchmark--to the best of our knowledge, the best result reported in this setting. Cross-dataset training improves performance without participant-specific fine-tuning, pointing toward scalable foundation models for speech brain-computer interfaces.

Naturalistic fMRI offers a time-resolved window into cortical dynamics during continuous multimodal experience, where future brain states are jointly governed by endogenous neural context and exogenous sensory drive. Forecasting such dynamics, however, remains challenging because fast-changing stimulus streams must be reconciled with the sluggish hemodynamic BOLD response, while cortical activity is organized across functionally heterogeneous brain networks. To address these challenges, we introduce BrainVista, a multimodal autoregressive framework for observed-stimulus conditional brain-state forecasting. BrainVista predicts future fMRI states from the history of brain activity and stimulus tokens temporally aligned to each prediction query, enabling autoregressive prediction without access to future ground-truth fMRI states. This design supports causal, temporally consistent modeling of naturalistic brain dynamics under realistic sensory stimulation. Specifically, we design Network-wise Tokenizers for functionally structured brain representations, a Spatial Mixer Head for cross-network interaction refinement, and Stimulus-to-Brain masking to preserve temporally causal stimulus–brain conditioning while preventing future brain-state leakage and stimulus look-ahead. We evaluate BrainVista on Algonauts 2025, CineBrain, and HAD, three large-scale naturalistic fMRI benchmarks, under long-horizon conditional autoregressive forecasting. BrainVista consistently outperforms baselines, improving pattern correlation by 10.8\% and 9.4\% relative to the strongest baseline on Algonauts 2025 and CineBrain, respectively.

Connectomics requires dense segmentation of large-scale electron microscopy (EM) volumes using models trained on small labeled blocks. In practice, a connectomics lab facing a new raw volume has two options: annotate a small block and train on it, or skip annotation and train on the union of available public labeled datasets. We introduce BrainEM, a benchmark that formalizes these options as two standard tracks: the Same-Source Track (Annotate-and-Train) and the Cross-Source Track (Reuse-and-Train). BrainEM brings together 16 EM datasets, including one in-house mouse dataset that we contribute, spanning multiple species, imaging modalities, and resolutions. We further add large-scale test volumes that bring the evaluation closer to realistic practice, and four diverse test targets in the Cross-Source Track. The data, post-processing, and evaluation metrics are fixed across all methods, and the framework is fully model-data decoupled so that new methods can be added with minimal effort. We conduct a systematic evaluation of six representative baselines covering the main technical directions in EM neuron segmentation, making BrainEM the first systematic evaluation at large scale and across diverse cross-source targets. The results differ from prior small-scale evaluations: rankings on the Same-Source Track no longer match those reported on small CREMI splits, and methods diverge sharply on the Cross-Source Track. Both tracks still leave clear room for improvement. We release BrainEM as a public platform to support unified comparison and faster method development in connectomics. All datasets and code are available at https://github.com/kwinderic/BrainEM

Brain signals exhibit substantial multi-center heterogeneity, strong inter-subject variability, and highly diverse task settings, which makes it difficult to build models that generalize beyond a single task. While recent brain signal foundation models leverage large-scale pretraining to learn generalizable representations, they often fall short in practice because they still require target-data-specific supervised adaptation, lack multi-task zero-shot capability, and rarely provide verifiable evidence to support their predictions. In this work, we present **BrainCoT**, a multi-task zero-shot Brain signal foundation model with neurometric-anchored Chain-of-Thought (CoT) reasoning. BrainCoT unifies heterogeneous tasks into an instruction-driven framework so that, after pretraining, a single model can be directly applied to multiple tasks in zero-shot settings without additional adaptation. It further introduces neurometric-anchored chain-of-thought grounded in neurometric evidence for verifiable reasoning, and adopts Decision-Consistent Rationale Training to keep process learning aligned with correct predictions. Across extensive evaluations, BrainCoT achieves an average ACC of 67.41\% and AUC of 74.33\% in zero-shot classification without downstream training data, outperforming the strongest linear-probing baseline trained with 1\% labeled downstream data by 3.85\% in ACC and 5.97\% in AUC. Code will be released upon acceptance.

Brain-computer interfaces (BCIs) offer a promising solution to speech loss due to neurological injury by decoding intended speech directly from brain activity. While recent BCIs have restored high-accuracy text-based communication, they fail to provide instantaneous voice output essential for the natural flow of conversation. Brain-to-voice BCIs address this gap by decoding voice directly from neural signals. However, even the state-of-the-art (SOTA) BCI-synthesized voice is not yet intelligible enough for real-world adoption. We introduce brain2voice 2.0, a new multimodal Transformer-based BCI decoder architecture capable of synthesizing intelligible voice from intracortical neural signals in real-time. Brain2voice 2.0 is trained on continuous and custom-tokenized acoustic targets and phoneme targets, leveraging their complementary speech information. We use self-supervised and adversarial training objectives that enhance acoustic feature quality and improve synthesis intelligibility. At each 10 ms timestep, the model causally outputs continuous and tokenized acoustic features for real-time voice synthesis as well as time-aligned phoneme predictions (raw phoneme error rate: 7\%, comparable to the latest brain-to-text models). We evaluated this new approach on the publicly available SOTA intracortical brain-to-voice benchmark dataset. Naïve human listeners transcribed brain2voice 2.0 synthesized voice with a word error rate of 5.24\%—an 8$\times$ improvement in intelligibility over previous results (43.75\%). Brain2voice 2.0 demonstrates that highly intelligible real-time voice synthesis from neural signals is achievable, for the first time crossing the intelligibility threshold necessary for clinically viable brain-to-voice BCIs for people with paralysis.

Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a single functional modality, restricting their ability to exploit complementary spatiotemporal dynamics and the collective data scale across different neuroimaging techniques. This limitation largely arises from severe semantic heterogeneity and resolution discrepancies among modalities. To address these challenges, we propose Brain-OF, an omnifunctional brain foundation model jointly pretrained on fMRI, EEG and MEG, capable of handling both unimodal and multimodal inputs within a unified framework. To reconcile heterogeneous spatiotemporal resolutions, we introduce the Any-Resolution Neural Signal Sampler, which projects diverse brain signals into a shared semantic space. To further manage semantic shifts, the Brain-OF backbone integrates DINT attention with a Sparse Mixture of Experts, where shared experts capture modality-invariant representations and routed experts specialize in modality-specific semantics. Furthermore, to explicitly internalize the characteristics of neural activity through self-supervised learning, we propose Masked Temporal-Frequency Modeling, a dual-domain pretraining objective that jointly reconstructs brain signals in both the time and frequency domains. Brain-OF is pretrained on a large-scale corpus comprising around 40 datasets and demonstrates superior performance across diverse downstream tasks, highlighting the benefits of joint multimodal integration and dual-domain pretraining. Our code will be made publicly available upon acceptance.

Object-level spatial-temporal understanding is essential for video question answering, yet existing multimodal large language models (MLLMs) encode frames holistically and lack explicit mechanisms for fine-grained object grounding. Recent work addresses this by serializing bounding box coordinates as text tokens, but this text-coordinate paradigm suffers from a fundamental *modality mismatch*: object information is inherently visual, yet encoding it as text incurs a high token cost that forces aggressive temporal downsampling. We propose *BoxTuning*, an object-aware visual prompting framework for multimodal model fine-tuning that moves spatial-temporal object states from text-coordinate serialization into the visual stream while retaining only object identity in a compact text legend. Colored bounding boxes and trajectory trails encode geometry and motion on video frames, while the color-to-object legend provides the minimal textual identity bridge. This reduces the token cost significantly, achieving 87-93\% text token reduction in practice. It also preserves full temporal resolution, where the trajectory trails further encode inter-frame motion direction and speed within each keyframe, recovering fine-grained dynamics that text-coordinate methods are forced to discard. Experimental results on five video QA benchmarks (CLEVRER, Perception Test, STAR, NExT-QA, IntentQA) show that BoxTuning surpasses text-coordinate baselines on spatially oriented tasks and avoids their degradation on reasoning-centric tasks, establishing object-aware visual prompting as a natural and efficient way to convey object information to MLLMs.