NeurIPS 2026 论文列表

Learning parity functions, more general modular addition, is a challenging machine learning task due to its input sensitivity. A recent study substantially scaled modular addition learning in both the number of summands and the modulus. Its key idea is to increase zeros in training sequences, reducing the effective number of summands and thus controlling training difficulty; however, this induces covariate shift between training and test input distributions. This study theoretically and empirically analyzes this side effect and proposes a covariate-shift-free method for modular addition. Specifically, we introduce an auxiliary modulus $Kq$ during training, which reduces wrap-around frequency and problem difficulty while preserving the same input distribution across training and testing. Experiments show strong scalability and sample efficiency: even for large input length $N$, large modulus $q$, and small datasets---where the sparse method fails to learn---our method achieves equal or better match accuracy and relaxed $\tau$-accuracy. For example, at $N=64$ and $q=974269$, our method trained on 100K samples achieves $97.0\%$ $\tau$-accuracy at $\tau=0.05$, while the sparse method achieves only $9.5\%$ with the same data size and $93.9\%$ even when extended to 1M samples.

GraphRAG enhances Large Language Model reasoning by organizing retrieved knowledge into structured evidence graphs, enabling inference over connected evidence rather than isolated text fragments. Yet, existing GraphRAG methods often either miss query-critical links or introduce noisy or conflicting evidence that distracts reasoning. We propose CONSIST, a Conflict-aware Nash-optimized Minimal Sufficient graph learning algorithm that regulates evidence graphs to resolve conflicts during reasoning. Specifically, we cast graph learning as an exploration–editing process, where a candidate graph is expanded via structural and semantic connections and then refined by pruning unreliable edges. We further introduce Gumbel-based edge editing, framing it as a Nash-guided bargaining process, where current and future players negotiate over a customized utility to determine reliable edits that improve both immediate answer support and long-term graph quality. Experiments on multi-hop question answering and conflict-aware benchmarks demonstrate that CONSIST consistently outperforms recent state-of-the-art baselines, achieving average performance gains up to 7.0% EM and 6.3% F1. Moreover, CONSIST also produces substantially more compact evidence graphs, reducing retained nodes by 42.9% and edges by 54.7% on average.

Budget-feasible mechanisms (BFMs) are a fundamental tool for budget-constrained procurement, but designing strong BFMs remains challenging, particularly for general valuation classes. Classical approaches rely on problem-specific analysis and worst-case approximations, often yielding conservative mechanisms in practice. This motivates data-driven methods for discovering mechanisms with stronger empirical performance. However, strict budget constraints expose a key limitation of existing neural automated mechanism design (AMD): they either fail to provide exact economic guarantees or suffer from severe scalability bottlenecks. We propose BFMNet, a scalable menu-based neural framework for learning truthful budget-feasible procurement mechanisms. BFMNet first learns *self-bid-independent* menus from market context, ensuring dominant-strategy incentive compatibility (DSIC) and individual rationality (IR) by construction, and then applies an ex-post, instance-level menu transformation to enforce budget feasibility. We prove that the resulting mechanism satisfies exact DSIC, IR, and budget feasibility. By avoiding intractable global discretization grids and restrictive Lipschitz constraints, BFMNet remains both expressive and scalable. Extensive experiments show that BFMNet improves utility over classical baselines by up to $34.1$%, remains competitive with state-of-the-art neural baselines that provide only approximate economic guarantees, and scales robustly to larger auction instances.

We study how to choose a probability distribution, or lottery, over $m$ alternatives for $n$ agents when each agent's acceptable lotteries form an unknown linear halfspace of the simplex. The learner observes only binary accept/reject membership queries. Prior work asks whether there exists a lottery accepted by every agent; we study the relaxation that minimizes the number of agents who reject the lottery. We first show that this problem is APX-hard even when all halfspaces are explicitly known and scores are binary. We then give exact algorithms under additional structure: fixed $m$, and a margin condition under which an optimal lottery can be replaced by one supported on $\mathcal{O}(\log n/\gamma^2)$ alternatives. With limited queries, uniform sampling of agents gives additive population guarantees after recovering the sampled agents' normalized halfspaces. For arbitrary $m$, we give a polynomial-time LP based on a one-sided margin penalty and prove a population guarantee depending on the number of true violations and the number of agents near their thresholds. Finally, we prove a tight deterministic lower bound: without a stochastic link between queried and unqueried agents, querying only $K$ distinct agents cannot guarantee additive gap below $n-K$, and this gap is achievable.

While multi-agent reinforcement learning (MARL) has shown strong empirical performance, existing methods struggle with large number of agents, due to the combinatorial growth of joint interactions. Mean-field (MF) approximations address this by replacing pairwise interactions with interactions against population distributions, yielding tractable large-population policies. However, existing MF formulations focus on fully cooperative or purely competitive settings and fail to capture the mixed cooperative–competitive structure of team-based games. We extend mean-field learning to _zero-sum team games_, where agents cooperate within teams and compete at the team level. We show that such games admit $\epsilon$-optimal decentralized policies that depend only on local states and population distributions. Building on this structure, we propose MF-MAPPO, a scalable algorithm with a shared actor and a minimally informed critic per team. MF-MAPPO is trained directly in finite-population simulators rather than using mean-field oracles, thereby enabling deployment to realistic scenarios with thousands of agents. We further extend MF-MAPPO to partially observable settings via a simple gradient-regularized training scheme. Experiments on large-scale benchmarks in our simulation platform $\texttt{MFEnv}$, including population games with analytical solutions and high-dimensional battlefield scenarios, demonstrate that MF-MAPPO outperforms existing MARL baselines and yields rich, heterogeneous behaviors.

Modern recording technologies enable simultaneous measurement of high-dimensional neural activity and rich behavioral variables, creating both an opportunity and a modeling challenge: identifying the shared latent structure that mediates the brain-behavior relationship and the underlying dynamics through which it evolves across behavioral states. Existing approaches focus on different parts of this challenge: shared representation learning methods identify neural-behavioral subspaces but typically do not model dynamical structure, while dynamical models capture temporal variation and discrete state transitions in neural activity, but do not explicitly disentangle shared neural-behavioral structure from modality-specific variability. We introduce Switching Shared Latent Dynamics (SSLD), a unified framework that learns a latent representation shared between neural activity and behavior and endows this shared space with nonlinear switching recurrent dynamics to reflect the hypothesis that behaviorally relevant neural representations exhibit structured dynamical changes across behavioral states. Complementary private latent variables capture modality-specific variability, providing a window into neural activity that does not directly interact with behavior. We evaluate SSLD on a simulated dataset and four diverse experimental datasets spanning species, brain regions, and recording modalities: monkey motor and premotor cortex during reaching, somatosensory cortex during a bump task, widefield calcium imaging across mouse dorsal cortex during (a) self-initiated decision-making and (b) spontaneous movements. Across all four datasets, SSLD accurately reconstructs neural and behavioral signals, recovers discrete states in the dynamics that align with experimentally defined behavioral epochs, and isolates behaviorally-relevant neural information in the shared latent while preserving private neural variability. Ablations confirm that shared representation learning, behavioral supervision, and switching dynamics each contribute to performance. SSLD offers an interpretable approach to modeling shared neural-behavioral dynamics that complements ongoing efforts toward foundation models for neuroscience.

Domain generalization aims to learn models that generalize to unseen target domains using labeled data from multiple source domains. Many existing approaches focus on learning domain-invariant representations, but enforcing invariance alone may fail to preserve sufficient task-relevant information under domain shift. We propose BLENDER, a reconstruction-aware framework for domain generalization that employs a hierarchical latent decomposition to separate domain-invariant and domain-specific factors. This structure prioritizes informativeness in the domain-invariant representation and captures residual domain-specific variation through conditional modeling. We further provide a theoretical analysis establishing a bound on the reconstruction risk for unseen target domains, revealing how domain invariance and latent disentanglement contribute to generalization beyond the observed domains. Experiments on standard domain generalization datasets demonstrate strong performance under substantial domain shifts, and qualitative analyses show that BLENDER promotes a structured allocation of information between domain-invariant and domain-specific latent representations.

Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding, human motion, and embodiment-specific action. We introduce MoT-HRA, a hierarchical vision-language-action framework that learns human-intention priors from large-scale human demonstrations. We first curate HA-2.2M, a 2.2M-episode action-language dataset reconstructed from heterogeneous human videos through hand-centric filtering, spatial reconstruction, temporal segmentation, and language alignment. On top of this dataset, MoT-HRA factorizes manipulation into three coupled experts: a vision-language expert predicts an embodiment-agnostic 3D trajectory, an intention expert models MANO-style hand motion as a latent human-motion prior, and a fine expert maps the intention-aware representation to robot action chunks. A shared-attention trunk and read-only key-value transfer allow downstream control to use human priors while limiting interference with upstream representations. Experiments on hand motion generation, simulated manipulation, and real-world robot tasks show that MoT-HRA improves motion plausibility and robust control under distribution shift.

Despite the effectiveness of Cube-and-Conquer (C&C) for solving challenging Boolean Satisfiability (SAT) problems, no prior work has shown that transformer-based models can learn effective cubing heuristics. We introduce a neuro-symbolic post-training framework for this task. We design an MCTS-based data curation pipeline that uses symbolic heuristics to explore splitting decisions over SAT competition formulas, producing preference data grounded in solver statistics and augmented with reasoning traces from a teacher model. Our two-stage post-training, supervised fine-tuning (SFT) followed by direct preference optimization (DPO), enables a 4B-parameter model to achieve a pass@5 score of 53 on 100 SAT competition benchmarks, surpassing frontier LLMs such as Claude-Sonnet-4 (50) and matching the best symbolic heuristic (53). Ablations show that SFT alone improves pass@5 from 46 to 51, with DPO adding 2 additional benchmarks; an entropy/agreement ablation on realized first-cube decisions further shows that SFT, not DPO, accounts for the root-level decision diversity that produces complementary per-run coverage over deterministic symbolic methods. This demonstrates that transformers can be trained to make effective cubing decisions in a domain traditionally dominated by symbolic methods.

Vision Transformers (ViTs) are bottlenecked by the length of their input token sequence. Standard patchification fixes this length with a single patch size applied across the image, regardless of regional content. Prior work on adaptive patch sizing reduces token count by assigning coarser patches to visually simple regions, but relies on noisy pixel-level heuristics such as local entropy to make patchification decisions. We argue that this compute allocation should be grounded in the model’s own semantic representations, not in raw pixel statistics. We introduce Split Policy Learned Image Tokenization which learns to allocate token resolution based on what the model finds meaningful, producing shorter token sequences that are better matched to task-relevant content. SPLIT matches the accuracy of full-resolution ViT classification baselines while using 30% fewer tokens. The same gains hold dense prediction: SPLIT achieves competitive accuracy on COCO and ADE20K, demonstrating that semantically grounded patchification generalises well beyond classification.

Discrete diffusion language models (DLMs) enable parallel generation but usually rely on fixed forward processes, such as masking, uniform noise, or precomputed hierarchies. We investigate whether the hierarchical forward process can instead be learned while preserving continuous-time tractability. We propose \textbf{Learnable Hierarchical Diffusion Language Model (LHDLM)}, which extends a learnable column-stochastic token-cluster map. This many-to-many hierarchy preserves a CTMC with block-conditional transition and closed-form CT-ELBO, while recovering HDLM in the one-hot limit and masked diffusion in the collapsed limit. We identify degenerate hierarchy maps caused by learning the same map used in both forward targets and model-induced cluster predictions, and mitigate them with an information-retention regularizer. Empirically, LHDLM is competitive with discrete diffusion approaches and reveals soft token structures --- cluster-agnostic function tokens and sharp content-token assignments --- that cannot be represented by hard partitions.

Statistical modeling and inference for temporal networks are increasingly important across modern applications, yet remain challenging in the sparse regime with evolving network sizes and temporal dependence. We propose a tractable model that captures these key features in a unified framework. Under this model, we establish geometric ergodicity and characterize the asymptotic behavior of cycle counts. These counts provide informative low-order summaries of temporal network dynamics. Moreover, we develop method-of-moments estimators based on cycle counts. We prove identifiability and uniqueness of the parameter estimates, and establish the strong consistency and asymptotic normality. To support uncertainty quantification, we further propose a simulation-based plug-in estimator of the asymptotic variance. Extensive simulations and a real data example demonstrate that our methods are accurate and effective for sparse, size-varying temporal networks.

Diffusion models have recently emerged as a powerful paradigm for generating 3D molecules. Despite showing remarkable promise, they learn atomic distributions implicitly from data, without explicitly encoding physicochemical rules into their generative dynamics. Consequently, generated samples can be statistically plausible yet chemically invalid. To address this problem, we propose Sequential Neuro-Symbolic Constrained Diffusion (SensDiff), a framework that integrates symbolic rules into generative modeling, enabling the model to internalize scientific priors during training. Central to SensDiff is the observation that the diffusion trajectory of 3D molecule generation exhibits a coarse-to-fine evolution: valid molecules arise by first mitigating macroscopic geometric conflicts and then refining structures toward microscopic chemical consistency through iterative denoising. Accordingly, SensDiff mirrors this dynamic by sequentially enforcing constraints with adaptive weighting during training, progressively steering generation toward valid molecular geometries. Moreover, SensDiff translates domain knowledge into interpretable generative controls, grounding opaque neural denoising in scientific principles. Experiments on molecular benchmarks and real-world constrained generation tasks confirm that SensDiff consistently improves chemical validity while adhering to physicochemical rules and user-specified targets.

We present a system for learning generalizable hand-object tracking controllers purely from synthetic data, without requiring any human demonstrations. Our approach makes two key contributions: (1) HOP, a Hand-Object Planner, which can synthesize diverse hand-object trajectories; and (2) HOT, a Hand-Object Tracker that bridges synthetic-to-physical transfer through reinforcement learning and interaction imitation learning, delivering a generalizable controller conditioned on target hand-object states. Our method extends to diverse object shapes and hand morphologies. Through extensive evaluations, we show that our approach enables dexterous hands to track challenging, long-horizon sequences including object re-arrangement and agile in-hand reorientation. These results represent a significant step toward scalable foundation controllers for manipulation that can learn entirely from synthetic data, breaking the data bottleneck that has long constrained progress in dexterous manipulation.

Reinforcement learning has achieved remarkable success in robot learning. However, under challenging exploration and contact-rich dynamics, early-stage training is frequently dominated by premature terminations such as collisions and falls. As a result, learning is overwhelmed by short-horizon, low-return trajectories, which hinder convergence and limit long-horizon exploration. To alleviate this issue, we propose a technique called Failure Episodic Memory Alert (FEMA). FEMA explicitly stores short-horizon failure experiences through an episodic memory module. During interactions, it retrieves similar failure experiences and prevents the robot from recurrently relapsing into unstable states, guiding the policy toward long-horizon trajectories with greater long-term value. FEMA can be combined easily with model-free reinforcement learning algorithms, and yields a substantial sample-efficiency improvement of 33.11\% on MuJoCo tasks across several classical RL algorithms. Furthermore, integrating FEMA into a parallelized PPO training pipeline demonstrates its effectiveness on a real-world bipedal robot task.

Vision-Language Pre-trained (VLP) models such as CLIP learn strong representations from large-scale image–text pairs and demonstrate impressive zero-shot transfer. However, by learning to align visual and textual features only at a global level, they often suffer from limited interpretability and weak fine-grained perception. This issue stems from pre-training data that overlooks key visual details of object parts, which are often important for distinguishing between subordinate categories (e.g., species of birds or models of cars). Multimodal Large Language Models (MLLMs) built on CLIP-style vision encoders inherit this weakness, limiting both accuracy and trustworthiness. To address this, we propose Part-Aware CLIP (*PA-CLIP*), a framework that improves fine-grained perception while enhancing interpretability. First, we leverage MLLMs to construct a new dataset,*FG-Part*, containing about one million part-level image–text pairs that explicitly describe discriminative components (e.g., beak shape or wing patterns). Second, we introduce a part-aware training strategy that encourages explicit grounding of fine-grained textual descriptions to corresponding image regions, strengthening part-level cross-modal alignment. Extensive experiments show that PA-CLIP achieves state-of-the-art results on multiple fine-grained visual recognition benchmarks, validating the benefit of part-level captions for capturing subtle details. Moreover, evaluations on general tasks such as cross-modal retrieval indicate that these improvements do not compromise the model's core generalist capabilities.

A point source in a PDE does not occupy an entire grid. A weather station reports one value at one site; it does not observe the temperature between stations. A pickup event in a city occurs at a coordinate and a time; it is not born as an image. Yet many neural-operator pipelines begin by turning such sparse observations into dense surrogate fields before learning. This interpolation step is often treated as harmless preprocessing, but in sparse regimes it changes the object on which the operator is asked to act. We introduce the Dirac Neural Operator (DIRAC-NO), a measure-native neural operator for sparse event-to-field learning without interpolation. DIRAC-NO keeps observations as atomic event measures until query time and constructs the output field through adaptive Dirac-kernel aggregation. Because this aggregation is implemented by direct summation, the front end preserves source additivity at the aggregation stage and admits a learned Green-kernel view of source-to-field response. Across controlled point-source PDE benchmarks and real sparse reconstruction tasks, DIRAC-NO exposes the cost of interpolation-first learning. On MeasureBench-1D, it achieves relative $L^2$ error 0.015 at $n=128$, compared with 0.49 for FNO, and it better preserves superposition structure in trained models. In 2D, it is strongest under parameter and density shift, while ERA5 reconstruction shows that the representation advantage persists beyond analytic PDEs at high observation budgets. Boundary cases and negative-control event streams further show that the benefit appears where the representation argument predicts: sparse, event-native tasks with source-response structure. These results suggest that sparse operator learning should be organized not only by the neural backbone, but by the native space in which the input is represented.

Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-based diffusion models, use stochastic interpolants to corrupt data samples at different noise levels indexed by a time variable. This defines a joint density over both the data space and time, and most methods learn its energy through either spatial or temporal differences. We identify distinct failure modes for both of these approaches. To solve them, we propose Spatiotemporal Noise-Contrastive Estimation (stNCE), a framework for learning the energy through joint spatiotemporal differences. stNCE unifies many existing methods and leads to new training objectives. Experiments on images and molecules demonstrate performance competitive with state-of-the-art density estimation methods.

Large distribution shifts remain a primary obstacle for domain generalization and unsupervised adaptation. Although gradual domain adaptation alleviates this difficulty by introducing ordered intermediate domains, most existing approaches still operate in a discrete, step-wise manner, which can accumulate errors and often relies on unstable Min--Max optimization. We propose \textbf{FlowGDA}, a flow-matching framework that models gradual adaptation as \emph{continuous} transport on a statistical manifold in a representation space. FlowGDA learns time-dependent velocity fields that induce smooth domain trajectories, using a simulation-free regression objective rather than adversarial training. To prevent semantic drift, we couple left-to-right transport between consecutive domains with a source-anchored transport and a semantic consistency loss, and we further stabilize learning via a two-stage sequential optimization scheme. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques, validating flow matching as an effective alternative for domain adaptation.