NeurIPS 2026 论文列表

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.

Phase transitions occur when a physical system undergoes a dramatic transformation as it crosses a transition. We study whether regression methods trained only in one phase can predict the physical properties that characterize the unobserved phase. Our test sets consist of the analytically tractable Heisenberg model and angle-resolved photoemission spectroscopy measurements of a venerable high transition temperature superconductor. We find that polynomial regression and kernel methods recover the unobserved phase structure, while standard neural networks fail. To bound the error, we adopt the generalized eigenvalue problem (GEVP) into an upper bound on the worst-case transfer coefficient, defined as the ratio of mean squared error (MSE) loss on the extrapolation region to the MSE loss on the training region. Empirically, we find that our GEVP bound stays approximately constant with respect to the empirical transfer coefficient across training-set sizes and distance between the training and extrapolation region for both datasets, providing a principled framework for studying extrapolation feasibility.

Despite success on standard benchmarks, vision language models display persistent failures on tasks involving processing of multi-object scenes, including many tasks that are relatively easy for humans. Recent work has found that these failures may stem from a basic inability to accurately bind object features in-context, a challenge that is referred to as the ‘binding problem' in cognitive science and neuroscience. The human visual system is thought to solve this binding problem via serial processing, attending to individual objects one at a time so as to avoid interference from other objects. Recent work has proposed `pointing' -- the use of explicit spatial coordinates to refer to objects -- as an analogous solution for vision language models, and found that it improves performance on challenging multi-object tasks. However, it is unclear \textit{why} (i.e., on a mechanistic or representational level) this approach improves performance, and how directly this relates to serial processing in human vision. Here, we investigate this question. We find that learning to point-via-text induces an internal visual search routine, and we characterize the mechanisms that support this procedure. We also find that pointing behavior can be generalized to new tasks via fine-tuning, and that doing so eliminates binding errors and enables compositional generalization. These results provide a proof-of-principle that serial processing can solve the binding problem for vision language models just as it does for biological vision.

Graph Neural Networks (GNNs) are an important tool for fMRI-based prediction, where empirical functional connectivity matrices serve both as node features and as the basis for graph construction. However, recent studies (and our own experiments) show that these methods often fail to outperform simpler graph-agnostic baselines. We propose a statistical explanation for this failure by formulating connectome prediction as an errors-in-variables (EIV) problem: the true connectome is latent, while the observed covariance or correlation matrix is a noisy finite-time-series estimate. In a linear asymptotic setting, we decompose the effect of graph convolution into three mechanisms: smoothing-induced bias and variance reduction, graph estimation error, and a double-dipping effect arising when the same noisy matrix determines both topology and features. Under a latent community model adapted to this neuroscience problem, we identify a narrow favorable regime in which message passing can improve prediction: the regression target must be community-smooth, and the time-series length must lie in a window between the graph-estimation and raw-noise limits. Simulations validate this decomposition by isolating oracle, independently estimated, and same-sample graphs, while real-data experiments illustrate that standard connectome GNN pipelines often fall outside this regime. Our results clarify when graph convolutions can help brain connectome prediction, and why they often do not.

Multinomial logit models (MNLs) are ubiquitous in machine learning, particularly in recommendation systems. While these models are typically evaluated using global metrics over a full catalog of items (such as $\ell_2$-error), practical applications often involve making predictions on small, curated subsets or ``slates'' of items. A model that minimizes global error may nevertheless yield very poor predictions on specific sub-universes. Yet, evaluating performance across all possible subsets remains computationally challenging. In this paper, we present a set of new efficient algorithms to evaluate the small-slate performance of MNL models. Using these tools, we conduct an empirical study comparing standard non-adaptive and adaptive learning algorithms. We demonstrate that while methods that compute the Maximum Likelihood Estimate (MLE) suffer from poor worst-case performance on small slates, specialized adaptive algorithms that dynamically sample difficult subsets can mitigate this issue. Conversely, we find that non-adaptive algorithms often achieve competitive average-case performance; we provide a theoretical model explaining this phenomenon. We conclude with recommendations for practitioners regarding when to utilize adaptive sampling based on the specific robustness requirements of their application.

Cognitive primitives such as planning, exploration, and backtracking are widely regarded as core to competent web agents. Yet existing studies are largely retrospective: agents are evaluated on domain-based tasks, and trajectories are inspected post hoc for evidence of these behaviors. Because such tasks are not designed to require specific primitives, observed behaviors do not reliably reflect true capability demands. This raises a key question: Can we design realistic tasks where success provably depends on a chosen cognitive primitive? WEBSTRESS addresses this by casting capability evaluation as a controlled comparison. For each task, we construct a paired variant in which a target capability becomes necessary (e.g., transforming a direct path into an obstructed one requiring exploration). The performance gap between the pair measures the agent’s reliance on that capability. Across seven environments and seven capabilities, this yields 519 paired tasks. Evaluating six frontier agents reveals consistent weaknesses: all suffer 18-28\% drops on capability-demanding variants, with 75\% of failures being belief failures, where agents incorrectly declare success. In contrast, humans exhibit only a 5.7\% performance drop under the same paired evaluation. These results highlight that, despite strong overall performance, substantial gaps remain between human and agent cognitive behavior, and that our approach provides a principled way to measure them.

Modern end-to-end autonomous driving systems often suffer from fragile out-of-distribution (OOD) generalization and limited decision transparency, largely due to their reliance on raw sensory observations and susceptibility to spurious visual correlations. Although memory-augmented approaches have been explored to improve generalization, existing methods typically store raw observations, symbolic rules, or entangled latent features, making retrieval sensitive to surface-level similarity rather than decision-relevant driving structure. To address this limitation, we propose DriveMem, a plug-and-play framework that moves beyond raw observations by distilling and storing invariant driving memories for generalizable driving systems. DriveMem integrates two core components: Invariant Feature Abstraction that first distills perturbation-stable, decision-relevant representations from visual tokens, and Prototype Memory Bank that then stores and retrieves prototypical driving experiences grounded in invariant representations. Extensive open-loop and closed-loop experiments demonstrate that DriveMem improves OOD generalization and safety-related planning metrics across diverse unseen scenarios. Notably, under severe sensor disruptions, DriveMem achieves up to a 39.5\% relative reduction in Collision Rate compared with a strong domain-generalization baseline. These results suggest that building prototype memory in an invariant representation space is a promising approach for improving robustness, while offering prototype-grounded traceability for model behavior.

Large language model agents are increasingly used as synthetic participants in social, behavioural, and decision-making research, yet it remains unclear whether they can express graded cognitive diversity rather than collapsing toward generic competence or stylised persona descriptions. We introduce a cognition-suite evaluation for LLM persona simulation, testing whether models reproduce population-level variation across structured reasoning tasks, self-report cognitive/personality measures, cross-construct associations, and downstream behavioural reasoning. Agents are conditioned on synthetic or human-derived trait profiles spanning cognitive reflection, Need for Cognition, and Big Five personality, then evaluated on CRT-style reasoning items, trait questionnaires, and Behavioural Reflection Task scenarios requiring evidence weighting, risk evaluation, and socially embedded justification. Across four matched simulation runs, agents partially recover questionnaire-style Need for Cognition and Big Five structure, especially under numeric prompting, but fail to preserve cognitive-reflection fidelity: CRT-style scores collapse toward correctness, intuitive-lure errors are almost eliminated, and reflection-related association structure weakens. Downstream BRT responses show the same pattern of flattening: open-ended decisions compress into over-regularised behavioural profiles rather than preserving human-like variation. The evaluation reframes persona simulation as a problem of cognitive-diversity fidelity: useful synthetic populations must preserve not only demographic or personality descriptors, but the structured variation in reasoning style, error type, and behaviour that those descriptors are meant to organise.

Comparing whether two dynamical systems implement the same computation despite differences in coordinates or measurements is a central problem in neuroscience and machine learning. Dynamical Similarity Analysis [DSA; Ostrow et al., 2023] addresses this problem by aligning finite-dimensional Koopman approximations of the two systems through an orthogonal similarity transformation. Here we show that orthogonal alignment is neither necessary nor sufficient for topological conjugacy: genuinely conjugate systems may be related by a non-orthogonal basis-transfer matrix that DSA cannot capture, while non-conjugate systems may have orthogonally equivalent Koopman operators that DSA will fail to distinguish. We then use this observation to formulate \emph{Conjugacy-based Similarity Analysis} (CSA), which restricts alignments to those induced by candidate state-space bijections rather than arbitrary orthogonal matrices. We prove that CSA's fitted alignment is the finite-data projection of the composition operator associated with the candidate bijection, and use controlled examples to show why this distinction matters when observable dictionaries are chosen explicitly or implicitly from data. Together, these results clarify what Koopman-based similarity measures must ensure to support claims of identifying conjugacies between computational systems.

EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical and brain-computer interface tasks. Existing models typically discretize the continuous multi-channel EEG waveform into patches or codebook tokens and train a transformer with masked self-supervision. Recognizing that this discretization fragments continuous brain rhythms and obscures fine-grained temporal dynamics, we present B[FM]$^2$ (Brain Foundation Model via Flow Matching), which drops discretization and pretrains directly on the raw signal using continuous-time flow matching without patches, tokenization, or masking. However, multi-channel EEG signals pose an architectural challenge for flow matching: time is densely sampled and highly autocorrelated (thousands of timepoints), while the electrode axis is short (tens of channels) at distinct scalp positions. To address this time-electrode asymmetry, we introduce SplitUNet, a velocity network that factorizes each block into separate 1D temporal and 1D electrode convolutions and downsamples only along time, preserving electrode topology throughout the hierarchy. B[FM]$^2$ sets a new state of the art on $7$ of $9$ standard downstream EEG classification tasks, using a pretraining budget of only $36{,}895$ segments ($\approx 307$\,h), a fraction ($\approx 3.3$\%) of that required by existing EEG foundation models. It also produces synthetic EEGs that two board-certified neurologists cannot distinguish from real EEGs (Cohen's $\kappa = -0.096$).

Understanding how the brain integrates information across sensory modalities under naturalistic settings remains a central challenge in cognitive computational neuroscience. Despite recent progress in multimodal brain encoding driven by the advancement of deep learning, many existing approaches combine representations from pretrained modality-specific models using static aggregation or linear readouts, and therefore do not explicitly capture how information is selectively integrated across cortical regions. Here, we introduce Multimodal Transformer Brain Encoder (M-TBEn), a neural encoding framework that models multimodal integration as a routing problem. M-TBEn extends prior vision-based brain encoding approaches by introducing a cross-attention mechanism for multimodality, in which learnable parcel-level queries dynamically select and aggregate modality-specific representations from visual, auditory, and linguistic inputs. This design is not only parameter-efficient but also yields parcel-specific multimodal representations that support a flexible linear readout for predicting brain responses at various spatial resolutions. We evaluate M-TBEn on naturalistic video-viewing data, including both in-distribution and out-of-distribution stimulus conditions. The model is able to accurately predict parcel-level and voxel-level fMRI responses and generalizes across stimulus distributions. Furthermore, the learned attention patterns provide a structured descriptive basis for analyzing modality integration across cortical parcels, enabling a connection between parcel-specific routing profiles and established functional brain networks. Together, these results suggest that attention–based routing offers a principled computational framework for modeling multimodal integration in the human brain.

Matrix-vector products (MVPs) are a key primitive in numerical linear algebra and machine learning. However, the naive quadratic running time for exact computation is prohibitive for large matrices, motivating approximate methods. In this paper, we study approximate MVPs under a natural ``lightness'' assumption, which bounds the total $\ell_1$-mass of a $n \times n$ input matrix $A$ by $\gamma n$. For general matrices, we give an algorithm for approximating $Ax$ up to additive $\epsilon \|x\|_2$ error in $O(\gamma n^{1.5}/\epsilon)$ query time and polynomial preprocessing. We extend this algorithm to kernel matrices by establishing a black-box reduction to Kernel Density Estimation data structures and analyzing a noisy entry sampling scheme. For kernel matrices, our algorithm does not require any preprocessing and improves the best-known running times for Gaussian kernel matrices of [Indyk, Kapralov, Sheth, Wagner; ICLR `25] by polynomial factors in $n$ and $1/\epsilon$, and also provides the first algorithms leveraging the lightness assumption for other kernel functions as well.

Human motion change is currently hindered by the lack of large-scale datasets, comprehensive benchmarks, and robust algorithms capable of handling diverse motion requirements. To address these gaps, we propose AnyMotion, a novel open-world paradigm for highly controllable and identity-consistent human motion editing across arbitrary scenes and actions. Our contributions are two-fold. First, we establish a hierarchical label taxonomy covering six major categories and 527 fine-grained classes, and curate HME-1M, a large-scale dataset containing 1.04 million editing quadruplets via systematic data balancing, cleaning, and reverse construction. Second, we introduce the Skeleton Chain-of-Thought (SCoT) framework, a two-stage pipeline consisting of motion reasoning and guided generation. In the reasoning stage, a multimodal large language model serves as a cognitive planner to derive target skeletal keypoints and MetaQueries. These outputs subsequently function as explicit structural and semantic priors, providing the necessary guidance for the diffusion transformer to perform high-fidelity motion generation. Finally, built upon our label taxonomy, we present Human Motion-Bench, the first motion-centric benchmark equipped with multi-category fine-grained evaluation metrics. Extensive experiments demonstrate that AnyMotion achieves state-of-the-art performance across diverse editing scenarios.

Online forecasters sometimes know a regime boundary before any post-boundary labels arrive. Standard adaptive conformal methods still react through later coverage errors, so they can pay a detection-delay cost even when the boundary is public. We study conformal prediction with an announced partition of the stream and separate two post-break audits. The reset empirical quantile in segmented split-CP attains the minimax per-time probability-gap rate $\Theta(M^{-1/2})$ after $M$ post-reset scores, with a matching Le Cam lower bound. A pure bounded constant-step announced-break ACI recursion attains an $O(M^{-1})$ time-averaged empirical-frequency gap by telescoping, but this is a realized-frequency statement, not a guarantee for the next interval. The practical AB-ACI implementation used in experiments adds burn-in and projection, so its rate claim includes explicit correction terms and is not unconditional. Synthetic experiments recover both slopes and show short-window gains when the break is large. Two real-data applications are null under leakage-free protocols. ERCOT RTC+B gives 0.863 post-reform coverage for AB-ACI versus 0.880 for ACI, and FOMC yield breaks give 0.892 versus 0.900. The message is diagnostic: use the reset empirical quantile for per-prediction reliability, use AB-ACI only for long-run frequency audits, and expect gains only when the realized score shift is large enough to make detection delay costly.

Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Generation, which treats factor discovery as isolated events, and Iterative Factor Evolution, which focuses on local parent-child refinements. However, both paradigms lack a global structural view, often treating factor pools as unstructured collections or isolated single-lineage paths, which leads to redundant search and inefficient allocation of optimization effort. To address these limitations, we introduce AlphaPROBE, (**Alpha** Mining via **P**rincipled **R**etrieval and **O**n-graph **B**iased **E**volution), a framework that reframes alpha mining as the strategic navigation of a Directed Acyclic Graph (DAG). By modeling factors as nodes and evolutionary links as edges, AlphaPROBE treats the factor pool as a dynamic, interconnected ecosystem. The framework consists of two core components: a Bayesian Factor Retriever that identifies high-potential seeds by balancing exploitation and exploration through a posterior probability model, and a DAG-aware Factor Generator that leverages the full ancestral trace of factors to produce context-aware, non-redundant optimizations. Extensive experiments on major Chinese and US stock market datasets against 8 competitive baselines demonstrate that AlphaPROBE significantly gains enhanced performance in predictive accuracy, return stability and training efficiency. Our results confirm that leveraging global evolutionary topology is essential for efficient and robust automated alpha discovery. We have open-sourced our implementation at https://anonymous.4open.science/r/AlphaPROBE-A103.

Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal representations and decision criteria align with the latent structure of human cognition. In this work, we propose a novel framework to align MLLMs with the multidimensional mental representations underlying human similarity judgements. We build on a previously established behavior-derived semantic embedding space, in which objects are represented along 66 interpretable dimensions derived from 4.70 million human Odd-One-Out (O1O) judgements. For each triplet, we infer the most salient decision dimension and convert it into a dimension-grounded linguistic rationale. This enables MLLMs to learn both the final choice and a justification consistent with the inferred behavioral criterion. Experimental results demonstrate that our approach improves model consistency with human similarity judgements while retaining competitive performance on standard multimodal benchmarks. Furthermore, using searchlight Representational Similarity Analysis (RSA) on an independent fMRI Natural Scenes Dataset (NSD), we observe increased representational alignment between model and human. Ultimately, our findings point to a data-driven route for incorporating human cognitive structure into MLLMs alignment.

LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable in the available environment. In such cases, a reliable agent should recognize that further interaction is unlikely to help and abstain from additional tool calls. We define Agentic Abstention, the problem of deciding when an agent should stop acting under uncertainty. Unlike standard LLM abstention, which is usually evaluated as a single-turn answer-or-abstain decision, agentic abstention is a sequential decision problem: an agent can answer, abstain, or gather more information at each turn, and the need to abstain may only become clear after interacting with the environment. We study this problem across web shopping, terminal environments, and question answering, evaluating 13 LLM-as-agent systems and 2 agent scaffolds on more than 28,000 tasks. Our results show that the main challenge is not only whether agents can abstain, but also when they abstain. Some agents never abstain when they should, while others do so only after many unnecessary interactions. This gap is especially large on tasks where the instruction appears feasible until the environment reveals otherwise (e.g., no valid result matches the instruction). We further find that model scale, reasoning, and agent scaffolding affect abstention in different ways, with larger or more capable models not always performing better at timely abstention. Finally, we introduce CONVOLVE, a context engineering method for improving agentic abstention that distills full interaction trajectories into reusable stopping rules. On WebShop, CONVOLVE substantially improves timely abstention without updating model parameters, raising Llama-3.3-70B's timely recall rate from 26.7 to 57.4. % , and overall recall from 83.2 to 100.0. Our dataset and code are available at https://anonymous.4open.science/r/agentic-abstention-A908

Many everyday robot manipulation skills are affordance-dependent, with success determined by whether the robot contacts the functional object region required by the subsequent action. Current simulation data generators obtain contacts from generic grasp estimators or per-object manual contact annotations, but generic estimators rank stable grasps without task semantics and often select contacts that are misaligned with the downstream action, while manual contact annotations must be rewritten for each new object and task. To solve these challenges, we introduce AffordSim, a scalable data generator and benchmark that integrates open-vocabulary 3D affordance prediction into simulation-based trajectory generation. Given a natural-language task description, AffordSim synthesizes a task-relevant scene, emits affordance queries, grounds them on object surfaces, samples region-conditioned grasps, and selects executable candidates with motion planning. It further randomizes object pose, texture, lighting, image noise, and cross-viewpoint backgrounds for sim-to-real transfer. We instantiate AffordSim as a 50-task benchmark across diverse manipulation skills, five robot embodiments, and 500+ rigid and articulated objects. AffordSim achieves $93\\%$ of the trajectory collection success rate of manual contact annotations on affordance-critical tasks and $89\\%$ on hard composite tasks. Vision-language-action policies trained on AffordSim data transfer zero-shot to a real Franka FR3, reaching $24\\%$ average success. The data generator, benchmark, and code are available at \href{https://anonymous.4open.science/r/AffordSim-8D94/}{project page}.

Reliable prediction of material properties from atomistic structures requires machine learning models that respect SE(3) symmetry. Frame-based methods address this challenge by constructing equivariant coordinate systems that align SE(3)-equivalent structures into unified representations. While attractive for their flexibility and efficiency, existing frame-based methods face two major limitations: a single frame type is often insufficient for heterogeneous atomic environments, and frame constructions can become unstable near highly symmetric or degenerate configurations. To address these issues, we propose the Multi-Frame Adaptive Network (MFAN), which leverages multiple frame types and introduces a local adaptive mechanism to combine them according to atomic environments, enabling different geometric references across atoms and complementary local-global information for each atom. Building on this multi-frame architecture, we further incorporate a frame-quality-aware weighting scheme that downweights unreliable frames before degeneration, thereby improving the continuity of the resulting representation. Experiments on crystal property prediction benchmarks demonstrate the leading performance of MFAN, with consistent gains from adaptive multi-frame learning. Additional force prediction experiments show improved robustness in continuity-sensitive settings.