NeurIPS 2026 论文列表

Graph representation learning often relies on message passing or spectral/positional encodings to summarize graph structure, but these indirect summaries can collapse structurally distinct graphs, including non-isomorphic cospectral pairs. We propose A2I, a structural encoding framework that renders BFS--degree-ordered local adjacency patterns as fixed-resolution images and embeds them with a frozen pre-trained vision encoder. The resulting structural tokens are mapped to learnable prototypes and aggregated with node embeddings via a lightweight Transformer. We provide a conditional analysis with two related findings: a stability result, showing that two orderings of the same graph yield embeddings that converge under bounded-degree assumptions, and a separability result, showing that two distinct graphs yield embeddings whose gap grows at least linearly with the difference in their BFS profiles. Empirically, A2I is competitive with recent GNN, Graph Transformer, and structural-encoding baselines on six node-classification benchmarks, with pronounced gains for GCN-based models on heterophilous graphs and under partial feature masking.

Current embodied VLM evaluation relies on static, expert-defined, manually annotated benchmarks that exhibit severe redundancy and coverage imbalance. This labor‑intensive paradigm drains computational and annotation resources, inflates costs, and distorts model rankings, ultimately stifling iterative development. To address this, we propose Agentic Automatic Evaluation (A2Eval), the first agentic framework that automates benchmark curation and evaluation through two collaborative agents. The Data Agent autonomously induces capability dimensions and assembles a balanced, compact evaluation suite, while the Eval Agent synthesizes and validates executable evaluation pipelines, enabling fully autonomous, high-fidelity assessment. Evaluated across 10 benchmarks and 32 models, A2Eval compresses evaluation suites by 85\%, reduces overall computational costs by 77\%, and delivers up to a 4.6$\times$ speedup while preserving evaluation quality. Crucially, A2Eval corrects systematic ranking biases, improves human alignment to Spearman's $\rho=0.85$, and maintains high ranking fidelity (Spearman's $\rho=0.87$), establishing a new standard for high-fidelity, low-cost embodied assessment. Our code and data will be public upon acceptance.

Recent Multimodal Large Language Models (MLLMs) have achieved strong performance on general video understanding benchmarks, yet their ability to reason about participant roles in aggressive interactions remains largely unexplored. Existing violence and crime datasets primarily evaluate whether aggression occurs, without testing whether models can identify who performed the action, who received it, and how participants are related within the scene. To address this gap, we introduce **A$^3$Bench**, a benchmark to study *aggressive action and association* in videos. The benchmark evaluates whether models can jointly recognize aggressive actions, distinguish aggressors from victims and bystanders, and correctly bind actions to participants under challenging distractor settings. We benchmark 15 recent MLLMs and find that current models struggle substantially on fine-grained aggressive-scene understanding. Performance degrades consistently as reasoning complexity increases, with models frequently confusing aggressors with victims or bystanders, revealing a systematic failure in role-action association rather than coarse aggression recognition alone. We further observe that increasing model scale or applying generic reasoning prompts provides limited improvement. Finally, we explore a simple role-graph prompting strategy that encourages structured reasoning over participant interactions, leading to consistent gains on compositional reasoning. Our findings highlight that reliable aggressive-scene understanding remains a major challenge for current MLLMs and motivate future research on role-aware video reasoning beyond coarse violence detection.

We apply offline reinforcement learning to large-scale warehouse consolidation, an application that requires ranking tens of thousands of candidates at each decision epoch with an architecture that scales gracefully with variable-cardinality action spaces. Since the available reward signal is only an approximation of the true target-domain objective, the policy must also transfer robustly. We propose a two-stage framework, Asymmetric Implicit Q-Learning (AIQL) and its adaptive cross-domain extension A$^2$IQL. First, AIQL learns a scoring policy from fixed offline consolidation logs by decoupling a per-candidate scoring actor from an aggregate-statistic critic, keeping value estimation tractable and within the support of logged data. Second, A$^2$IQL adapts the source-domain-trained policy to a new target objective using scarce target-domain data: a Bayesian reward predictor supplies an uncertainty-penalized conservative bonus that modifies only the advantage-weighted actor extraction, leaving the source critic unchanged. On a single-domain benchmark, AIQL improves throughput by 20% over a heuristic baseline using offline data alone. When adapted to a new target objective via A$^2$IQL, the method outperforms source-only training, data pooling, and off-dynamics baselines by up to 23% under a $30{:}1$ source-to-target data ratio while achieving the lowest variance across all methods.

Graph domain adaptation (GDA) has emerged as an important problem in graph machine learning when the distribution of the source graph used for training differs from that of the target graph used for testing. While much of the prior work on GDA has focused on aligning node representations across source and target domains, recent studies show that such approaches can be suboptimal in the presence of graph structure shift, where the underlying connection patterns between nodes change across domains. In this work, we develop a unified pairwise distribution matching framework for mitigating conditional structure shift (CSS), a specific form of graph structure shift in which conditional edge distributions vary across domains. The framework recovers existing GDA methods as instances of moment matching and motivates PLSA, a new likelihood-based method that uses calibrated probabilistic predictors on connected target node pairs. Theoretically, we establish finite-sample guarantees for both the likelihood-based and moment-matching estimators under the contextual stochastic block model. Our analysis uses Bernstein-type concentration bounds for edge-weighted U-statistics, leading to error bounds that reflect both the effective number of observed edges and the conditioning of the corresponding likelihood or moment-matching problem. We complement our theoretical results with empirical studies that demonstrate the effectiveness of the proposed framework.

Federated learning and analytics are often described as collections of separate protocols, even when they share the same mathematical form: client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor language that formalizes this structure. The language distinguishes federated tensors, whose records are partitioned across clients along a tracked record axis, from shared tensors, which are available globally. Its semantics are defined by comparison with a virtual global tensor, used only as a reference object. The main result is a shared-state factorization theory. We show that typed one-round programs factor through fixed-dimensional shared state whose size is independent of the number of clients and records, computed from client-local tensor expressions and merged across clients. We also prove a converse representability result; factorizations whose encoders and decoders are expressible in the language are realized by typed one-round programs, and the correspondence extends to iterative programs whose cross-round state is shared. This gives a formal account of the computations in the language that can be expressed as encode, merge, and decode procedures. We then develop a differentiable fragment for learning. If a per-record loss and its per-record gradient are represented by client-local tensor expressions, the global gradient is represented by record-axis summation of the federated gradient tensor. This yields typed iterative programs for server-side gradient descent and shared-linear-algebra second-order updates. The framework characterizes a broad class of federated learning computations whose communication passes through fixed-dimensional shared state.

Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astronomical observations across diverse modalities remains untested. We present AstroVLBench, a benchmark of over 4,100 expert-verified instances across five tasks spanning optical imaging, radio interferometry, multi-wavelength photometry, time-domain light curves, and optical spectroscopy. We systematically evaluate six state-of-the-art frontier models and find that performance is strongly modality-dependent and uncorrelated with general benchmark rankings. Mechanistic ablations reveal three concrete bottlenecks. First, prompts that ground attention in physical mechanisms (why a feature matters) yield more balanced classifications than phenomenological prompts (what to look for). Second, presenting one-dimensional measurements as numerical tables rather than rendered plots improves accuracy by up to 13 percentage points, indicating that plot rendering can obscure physically relevant signal. Third, a qualitative reasoning analysis reveals a pervasive “right-answer-wrong-reason” phenomenon: models routinely reach correct predictions through physically invalid logic, showing that accuracy alone is insufficient for trustworthy scientific deployment. AstroVLBench provides the first systematic, multi-modal baselines for VLMs in observational astronomy and identifies the representation, grounding, and reasoning bottlenecks that must be addressed before VLMs can be trusted as autonomous scientific interpreters.

Grid cells in the medial entorhinal cortex and place cells in the hippocampus together support spatial navigation. The two regions are reciprocally connected, and there is a chicken-and-egg problem for how both arise and reinforce each other during development. Current computational accounts either derive one type from the other or use network dynamics to model the emergence of one type in isolation. We introduce a unified recurrent network model that instantiates Dale's Law (every neuron is either excitatory or inhibitory), and is trained to predict the next sensory observation from masked previous sensory observations and egocentric motion. To our knowledge, this is the first single-objective model in which grid and place cells co-emerge without supervision of either type, or reliance on pre-existing spatial-cell representations. The two kinds of spatial codes coexist across 1,000 different training configurations, with their balance set by the amount of sensory noise and masking. Without retraining, the network qualitatively reproduces experimentally observed grid fragmentation in hairpin mazes, grid merging after wall removal, lattice alignment across connected rooms, locally ordered 3D fields observed in freely flying bats, as well as the developmental order in which place cells precede grid cells. We interpret these results in terms of two complementary encoding pressures within a single sensory-prediction objective: (1) correcting errors or reconstructing missing components of sensory observations, and (2) prediction of the next sensory state during navigation. Our results suggest a circuit-level account of the co-emergence of grid and place cells, and experimentally testable predictions for the two kinds of spatial codes.

We consider the problem of prediction with expert advice for ``easy'' sequences. We show that a variant of NormalHedge enjoys a second-order $\epsilon$-quantile regret bound of $ O\big(\sqrt{V_T \log(V_T/\epsilon)}\big) $ when $V_T > \log N$, where $V_T$ is the cumulative second moment of instantaneous per-expert regret averaged with respect to a natural distribution determined by the algorithm. The algorithm is motivated by a continuous time limit using Stochastic Differential Equations. The discrete time analysis uses self-concordance techniques.

Neural dynamics are a central feature of biological neural systems. In the visual cortex, the functional role of neural dynamics is poorly understood, particularly for static stimuli. The statistics of still natural images have long been used by models that successfully predict firing in the early visual cortex. Those models assume that neurons represent inferences about latent image features, but they often ignore the dynamics of said inference. Here, we test if inferential dynamics for images explain visual-cortical dynamics. To do so, we present a biologically plausible model of cortical computation that assumes the inference of features is coupled with inference about how those features are organized into meaningful parts, also known as segmentation. Given an input image, our model iterates between inferring the features and the segments, through recurrence, thus inducing dynamics in the feature representation. We make theoretical and exact predictions for the induced neural dynamics. Our model predicts both gain and variability in evoked responses to natural images, and we show that it captures classical single-neuron experimental findings such as gain dynamics and variability decay during stimulus presentation. The model also predicts that those metrics are more heterogeneous than previously thought, reflecting the complexity of inference for natural scenes, and that their population-level organization reflects the inferred segments. Finally, our analytical formulation provides a clear interpretation of those results. Insights from our normative model could generalize to segmentation problems in other experimental modalities, and help address the cost of inference in artificial networks.

Understanding how neural population responses represent sensory information is a central problem in systems neuroscience. One approach is to define a representational geometry on stimulus space in which distances reflect how reliably stimuli can be distinguished from neural activity. However, different constructions of these distances can lead to qualitatively different conclusions about the neural code. Here, we show that a unique Riemannian representational geometry emerges from first principles governing how distances contract as stimulus resolution is lost through coarse-graining. This results in a multi-scale extension of the Fisher information metric, capturing encoding structure from fine stimulus details to coarse global distinctions. The resulting geometry is exactly related to the mutual information encoded by the population: well encoded stimulus directions -- those contributing more to mutual information -- are expanded, whereas poorly encoded directions are contracted. The metric tensor can be estimated using diffusion models, making the framework practical for large neural populations and high-dimensional stimuli. Applied to visual cortical responses to natural images, the eigenvectors of the metric tensor identify stimulus variations that contribute most to information transmission, yielding interpretable features that are robust to modelling choices. Together, these results provide a principled, information-theoretic framework for characterising neural population codes.

Humans, animals, and modern machine learning models exhibit impressive abilities to learn complex behaviors and generalize these behaviors to unseen situations. This ability requires us to learn rules and regularities that allow for such generalizations. At the same time, in most complex environments, any rule will have its exceptions. How do learning systems balance between learning general regularities and memorizing exceptions? We argue that a lack of task paradigms has hindered the study of this essential ability. To address this gap, we introduce a novel task, transitive inference with exceptions, that tests for relational generalization and memorization of an exception to the relational rule. We then analytically characterize the behavior of a simple, theoretically tractable model of neural network learning (kernel ridge regression) across a broad family of representations and task parameters. We find that these models can balance between relational generalization and memorization, but unlike for transitive inference without an exception, successful generalization is sensitive to the specific representational geometry. We explain why this task is more challenging mechanistically by drawing on our analytical theory. Finally, we validate our theoretical insights in pretrained language models that are finetuned on ordered relations, finding that these models successfully generalize according to the transitive rule, but also make the kinds of systematic mistakes predicted by our theory. Overall, our theory shows how learning systems can balance between relational generalization and memorization, explains how this can go wrong, and emphasizes the need for new task paradigms designed to probe this ability.

Foundation-model-based predictors of perturbation response are evaluated primarily by aggregate accuracy, yet lack principled per-prediction reliability criteria. We audit the state-transition (ST) decoder~\citep{adduri2025predicting} shared by recent predictors across three encoders and four cell lines under CRISPRi perturbation, and identify a rank-$1$ attention bottleneck whose leading direction maps in gene space to the same cycling/p53 stress-response program in every cell line tested. The bottleneck implies a distance-defined reliability boundary: per-prediction quality degrades smoothly with the gene-space distance from a model's predicted perturbation response ($\Delta$, the predicted change relative to control) to its nearest training $\Delta$, and biological-family identity, not training-pool size, drives held-out quality. We propose \emph{predicted-NN-distance}, this same distance computed from model output alone, as a per-prediction trust signal that tracks per-perturbation quality at Spearman $|\rho| \approx 0.81$ (oracle $0.88$) and admits a single pooled threshold that transfers across cells and encoders under leave-one-out evaluation. The signal outperforms a simpler predicted-magnitude baseline in most cases. Per-prediction reliability is bounded by a low-rank decoder geometry and by gene-space coverage of training perturbations, not by aggregate benchmark accuracy.

The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biological and artificial agents. To make such comparisons effective, though, we need embodied models that actually move like animals. Previous work has developed virtual animal bodies and techniques for training realistic motion through imitation. However, to-date, there is no system for embodied animal modeling that produces natural movements both during trained, goal-directed behavior, and during exploration driven by random generation. Here, we introduce Simulated Control via Augmented Motor Priors for Embodied Reinforcement learning (SCAMPER), a system for training an embodied virtual rat model, enabling natural movements during both goal-directed behavior and random exploration. SCAMPER uses a variational architecture with two distinct priors, a motion prior for control and a behavior prior for exploration. We show that combining both of these priors allows SCAMPER to generate realistic behavior during exploration and exploit the task structure effectively. As well, SCAMPER can learn from sparse rewards as a result of the diverse movements induced by the behavior prior, allowing the system to model realistic sparse reward tasks from experimental neuroscience. Altogether, SCAMPER provides the neuro-AI community with an effective new system for training embodied models of rodents on realistic tasks.

Kernel methods, and Gaussian Processes (GPs) in particular, require a conditionally negative-definite (CND) distance measure to guarantee positive semi-definiteness (PSD) of the kernel matrix — a condition that fails for many natural input spaces, including smooth manifolds and spaces of probability distributions. We propose the Sparse Landmark Embedding (SLE) kernel, which eliminates this requirement entirely. Each input is embedded into a sparse feature vector via compactly supported bump functions centered at all training points $|\mathcal{D}|$; applying any standard PSD kernel in this embedding space yields a kernel that is provably PSD for arbitrary distance measures. The compact support automatically controls embedding sparsity, keeping kernel matrices well-conditioned and computationally tractable despite the high ambient dimension. We provide theoretical guarantees on PSD, sparsity, stability, and universal approximation, and demonstrate, using geodesic and Wasserstein distances, that the SLE kernel matches or substantially exceeds domain-specific baselines in both predictive accuracy and uncertainty quantification.

We derive closed-form ODEs for the learning dynamics of a linear recurrent policy network trained with REINFORCE on sparse-reward tasks in a high-dimensional teacher–student setting. Under alignment assumptions between student and teacher networks, the dynamics reduce to a finite system of coupled order-parameter ODEs whose reward terms are governed by trajectory-level orthant probabilities of a structured correlated Gaussian process. The theory predicts that recurrence can accelerate escape from sparse-reward plateaus by accumulating task-relevant memory, and that each episode length admits an optimal recurrent scale that minimizes learning time. Our results provide a quantitative theory of how recurrence, and sparse reward interact during learning.

Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading, yet existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it. GazeWorld autoregressively predicts the latent representation of the next fixated patch from all previously visited ones, while a spatial-completion branch covers unvisited regions. At inference, GazeWorld generates a sequence of patch representations from the image alone without requiring real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy across all nine supervised settings on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, as well as the highest zero-shot accuracy on all three benchmarks. On the GazeSearch benchmark, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16\% in ScanMatch and 22\% in SED, despite not being explicitly trained to predict gaze. GazeWorld demonstrates that modeling how experts read, not just what they conclude, offers a promising pretraining paradigm for medical imaging AI.

Anchored fixed point and monotone equation methods, including Halpern iteration, extra anchored gradient, and their relatives, add a vanishing pull toward a reference point to obtain last-iterate guarantees. Existing anchored variants often achieve sharp last-iterate guarantees, but from the update-level perspective the placement of the anchor can be algorithm-specific and conceptually opaque. We show that anchoring admits a single operator-side construction: regularize the operator queried by the base method with a vanishing Tikhonov term, then run the unmodified base method. Applied to the Picard iteration, this recipe reproduces the Halpern iteration; applied to the forward step, extragradient, and Popov methods, it yields three variants whose anchor placements inherit the base method's query pattern. The four analyses share a residual recurrence, recovering the \(O(1/k)\) Halpern residual-norm convergence rate, giving \(O(1/\sqrt{k})\) for the regularized forward step, and giving \(O(1/k)\) for the regularized extragradient and Popov variants in the unconstrained monotone Lipschitz setting.

A wide range of methods have been proposed for interpreting language models, delivering important insights into their inner workings. However, different methods and their resulting insights stand in relative isolation: what could the underlying structure of language models be, such that they give rise to all our interpretations? In this work, we propose using Tensor Product Representations (TPRs; Smolensky 1990) as a unifying hypothesis. TPRs give a concrete proposal for how compositional structure can be represented in vector space. We show, both mathematically and empirically, that TPRs can unify several prior interpretability methods: additive analogies, linear probing, sparse autoencoders, and activation patching. Mathematically, we show that these methods can all be derived from TPRs. Empirically, we apply the derivations to a range of different models — from small toy models to LLMs — to construct instances of each of the above interpretability methods; these constructed variants perform comparably to their standard variants. We view this work as a step toward what interpretability will ideally provide: a unified account of the nature of neural networks, corroborated not just by individual observations but also by an explanation of the connections between them.