NeurIPS 2026 论文列表

Open‑vocabulary segmentation (OVS) enables models to segment a variety of concepts specified in natural language, removing the need for fixed label sets and fine-tuning. While recent OVS approaches have shown strong performance on natural images, they transfer poorly to Earth observation (EO) data due to differing semantics, scales, sensing modality, and geographic context. To date, large‑scale EO‑specific datasets and benchmarks for OVS remain scarce. We, therefore, introduce TerraMesh-Masks, a large-scale open-vocabulary segmentation dataset for EO, comprising 16 million binary masks aligned with nine million multimodal TerraMesh samples and annotated using OpenStreetMap and Overture-derived semantics. In addition, we provide an expert-reviewed evaluation set of 523 samples covering over 200 classes to enable robust evaluation. To demonstrate and analyze the impact of TerraMesh-Masks, we build an open-vocabulary version of TerraMind. Our results demonstrate significantly improved performance over general-purpose state-of-the-art models reaching 35.15% IoU versus 16.29% for DINOv3.txt and 11.33% for SAM-3. We release the dataset publicly under a permissive license at https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh-Masks.

Transformers for temporal sequences require mapping sampled signals into sequences of vectors, called tokens. This is done by partitioning the sequence into local temporal windows (patches). We show that the tokenization step induces an approximation--estimation tradeoff. Smaller windows yield higher-variance statistical estimates because each token is supported by fewer samples, while larger windows stabilize estimation but increase the amount of information that must be summarized. Motivated by this tradeoff, we propose a lightweight tokenizer that constructs multiple estimates for each token and sums them, without changing the Transformer backbone. Across 12 datasets spanning four modalities, our method improves performance and reduces sensitivity to temporal scale across tasks.

Therapeutic target identification---determining which genes or proteins to modulate for a desired clinical effect---is a foundational step in drug discovery. However, existing computational methods that rank candidates at genome scale face three persistent challenges: (i) static feature design, which discards mechanistic knowledge locked in biomedical literature; (ii) lack of interpretability, offering no auditable gene-level rationale for individual predictions; and (iii) the false-negative problem, where unlabeled but potentially druggable genes are treated as definitive negatives. We propose TargetSage, an LLM reasoning agent for therapeutic target identification built on positive-unlabeled (PU) learning, with three modules. First, Agentic Profiling enriches each gene with LLM-gathered biomedical evidence, distilling 2,261 attributes into a 23-dimensional reasoning vocabulary. Second, Guided Reasoning produces gene-specific rationales and explicit reasoning attributes through a GRPO-trained policy rewarded by downstream Adjusted $F_1$. Third, PU-Aware Scoring integrates structured biological features, explicit reasoning attributes, and implicit reasoning embeddings via gated fusion under an LLM-informed class-prior to produce genome-wide druggability scores. Evaluated on 15 benchmark tasks spanning 19,032 human protein-coding genes, TargetSage consistently outperforms ten baselines, surpassing the strongest by 37% in macro-average Adjusted $F_1$ and generalizing to independent held-out benchmarks. Using only 2021 training data, TargetSage outperforms all ten baselines at ranking genes that gain clinical approval by 2025. On an independent validation set of Phase 2/3 clinical candidates, it further achieves a 51% higher enrichment odds ratio at the top-25 shortlist than the runner-up baseline. TargetSage provides an evidence-grounded, continuously updatable foundation for interpretable target prioritization at genome scale.

How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interactions from the physical costs required to execute them, while artificial life models traditionally study the environments that lead to replication without considering the strategic decisions behind the evolved mechanisms. In contrast, we introduce **Autopoietic Game Theory**, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving. We study these dynamics using a computational substrate of randomly initialized programs in Z80 machine code, showing empirically and theoretically that embedding a social dilemma directly into the physics of computation can favor the emergence of self-replicating, cooperative strategies. When resources are scarce, our analysis shows that *defection can become self-limiting even in well-mixed populations*: parasitic stealing destroys shared energy, slows execution, and can prevent reliable replication. Empirically, evolved programs suppress stealing across several Z80 environments, while spatial assortment further supports structural complexity and task performance. We further show that the framework can incorporate exogenous pressures, such as math tasks structured as sequential social dilemmas, when rewards are tied to computation budgets. These results suggest that coupling an agent's capacity for computation to its available energy transforms cooperation into a dominant scaffolding for building complex, sustainable, and self-organizing systems.

Long-term time-series forecasting (LTSF) underpins applications such as energy-load prediction and traffic-flow forecasting, yet model performance often deteriorates as the horizon extends. In particular, errors accumulate with lead time and systematically amplify toward the end of the predicted sequence, a phenomenon known as tail deterioration. We posit that this instability is driven by weakly constrained fusion of cross-temporal and cross-scale context: aggressive mixing can propagate noise and early-stage bias into distant horizons, ultimately degrading tail accuracy. To address this issue, we propose TailFix, a controllable cross-scale mixing framework for long-horizon forecasting. TailFix selectively gates interactions among multi-scale representations, derives scale-level correction terms from contextual summaries, and injects them via gated residual connections to suppress error diffusion and improve long-horizon stability. We further incorporate adaptive channel enhancement and a lightweight tail-correction module to explicitly refine the error-prone tail region. Finally, we introduce the Tail Amplification Factor (TAF) to quantify tail error amplification relative to the early portion of the sequence. Across diverse benchmarks, TailFix consistently mitigates tail error amplification and improves performance on multiple long-horizon forecasting tasks.

We introduce Tadpole, a novel foundation model for three-dimensional partial differential equations (PDEs) that addresses key challenges in transferability, scalability to high dimensionality, and multi-functionality. Tadpole is pre-trained as an autoencoder on synthetic 3D PDE data generated by an efficient online data-generation framework. This enables large-scale, diverse training without storage or I/O overhead, demonstrated by scaling to an equivalent of hundreds of terabytes of training data. By autoencoding single-channel spatial crops, Tadpole learns rich and transferable representations across heterogeneous physical systems with varying numbers of state variables and spatial resolutions. Although pre-trained solely as an autoencoder, Tadpole can be efficiently applied for multiple downstream tasks beyond reconstruction, including dynamics learning and generative modeling. For dynamics learning, we propose a novel parameter-efficient fine-tuning strategy that integrates low-rank adaptation, latent-space transformations, and reintroduced skip connections, achieving accurate temporal modeling with a minimal number of trainable parameters. Tadpole demonstrates strong fine-tuning performance across various downstream tasks, highlighting its versatility and effectiveness as a foundation model for 3D PDE learning.

Probabilistic neural surrogates for PDEs enable ensemble forecasting, but often exhibit miscalibration when used for uncertainty quantification, especially in compute-constrained, few-step inference regimes. Existing generative approaches typically rely on fixed global stochasticity, which cannot adapt predictive uncertainty to the local dynamical state. We propose TORNADO, a latent probabilistic forecasting framework that formulates next-step prediction as stochastic transport between VAE encoder posteriors. Unlike existing approaches with globally prescribed stochasticity, TORNADO learns a SDE with state-dependent diffusion, enabling the predictive uncertainty to adapt to the local latent dynamics. The model is trained using analytic conditional endpoint targets derived from a Gaussian interpolant, enabling simulation-free learning. An additional energy distance regularization term improves distributional alignment. Empirically, TORNADO improves uncertainty calibration over representative generative baselines, including stochastic interpolants, diffusion models, and flow matching, while maintaining comparable predictive accuracy. Our results show that adapting stochasticity to the latent state, rather than prescribing it globally, is an effective mechanism for improving calibration in practical neural PDE forecasting settings.

Atrial fibrillation (AFib) is the most common sustained cardiac arrhythmia and a major risk factor for stroke, yet it often remains undetected until a first clinical event. Existing deep learning approaches typically estimate AFib risk from a single, cross-sectional ECG, discarding the longitudinal information contained in serial recordings. We propose a transformer-based survival model, SurviFormer, that leverages sequences of ECG representations to model patient-specific AFib risk over time. Our approach encodes irregular inter-visit time gaps as pairwise relative attention biases within a causally masked transformer, and outputs a discrete hazard function trained with a multi-landmark strategy. We train and evaluate our model on the CODE dataset (140k patients, 517k ECGs), with external validation on MIMIC-IV. Compared to static baselines (DeepSurv and DeepHit applied to ResNet-50 embeddings) and a dynamic competing-risk model (DynamicDeepHit), our model achieves a C-index of 0.853, a mean TD-AUC of 0.867, and an IBS of 0.030 on the internal test set, and a C-index of 0.805, a mean TD-AUC of 0.848, and an IBS of 0.106 on the external cohort, demonstrating that longitudinal ECG trajectories provide prognostic value beyond a single recording.

Contact-rich robot dynamics are hybrid: a single observation can match several latent states and contact regimes (free, impact, stick--slip). A standard amortized filter that places no probability on a feasible contact transition will permanently lose the branch the robot actually follows. We introduce VHYDRO, a variational hybrid dynamics learner that prevents this branch loss. At each step, VHYDRO mixes the learned proposal with a feasible transition law before sampling and importance weighting. This keeps every feasible contact alternative covered. VHYDRO jointly infers a continuous latent state and a discrete contact mode, and fits a sparse port-Hamiltonian law to each recovered regime. On top of this, three guarantees connect: support coverage stabilizes filtering, the stabilized filter concentrates the discrete contact posterior on coherent regimes, and mode-pure segments admit sparse port-Hamiltonian recovery. The recovery error separates cleanly into filtering, derivative, mode-impurity, and physics-residual parts. Three empirical findings track the same mechanism. Under heavy occlusion the support-safe filter stays usable while a non-defensive proposal collapses. On ManiSkill demonstrations and on four canonical Sawyer/BridgeData task families the discrete state forms temporally coherent contact-regime segments that post-hoc segmenters and mode-free baselines miss. On hybrid systems with known equations the mode-conditioned sparse fit recovers the active physical terms; purely predictive baselines do not.

Protein optimization remains a longstanding goal in life sciences. Existing machine learning–assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose **StructEvo**, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior state-of-the-art methods by 9.2% and 16.3% on two challenging optimization benchmarks, and further identifies an experimentally validated epistasis pattern in GFP, highlighting the importance of structural guidance for effective protein directed evolution.

Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We introduce Stein kernelized molecular dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for the active learning and fine-tuning of MLIPs. SKMD corresponds to a stochastic variant of Stein variational gradient descent that is adapted for molecular dynamics by incorporating asynchronous particle updates and a kernel of global atomic descriptors, which provides a symmetry-aware measure of configurational similarity. Unlike other enhanced samplers used in molecular dynamics, SKMD preserves the Boltzmann distribution as the asymptotic distribution of the dynamics. This property enforces a balance between the exploration of diverse configurations and attraction toward high-probability regions of the energy landscape. We further propose an approach to efficient online data acquisition using an adaptive stopping criterion that selects non-redundant training data over the course of simulation. We demonstrate SKMD for the active learning of a neural network model of the Muller-Brown potential and the fine-tuning of a MACE interatomic potential for alanine dipeptide. Compared to active learning baselines, our method achieves higher model accuracy in fewer training iterations, with the same number of acquired training samples.

Long-horizon forecasting of time-dependent partial differential equations (PDEs) is critical for characterizing the sustained evolution of physical systems. While neural operators have emerged as efficient surrogates, they typically learn implicit finite-time transitions from discrete observations. When deployed autoregressively, such propagators often suffer from rapid error accumulation and dynamic drift. To address this, we propose a neural forecasting framework that reformulates PDE rollout as learning a Structured Spectral Propagator (SSP) in a propagation-oriented latent space. Following an analysis-propagation-synthesis design, our framework: (i) maps physical states into a shared, time-consistent spatial representation; (ii) projects this space into a compact propagation state to isolate recurrent dynamics from fine-grained spatial details, thereby decoupling reconstruction fidelity from rollout regularity; and (iii) evolves retained spectral modes using a frequency-conditioned linear backbone complemented by a nonlinear spectral closure to account for truncated interactions. This explicit structuring endows the propagator with a strong inductive bias for coherent modal evolution. Extensive experiments demonstrate that SSP significantly outperforms state-of-the-art baselines, reducing relative $L_2$ errors by up to 48.9\% and exhibiting improved stability in temporal extrapolation beyond the supervised horizon.

Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edits, and therefore fail to capture end-to-end workflows in realistic business settings. We introduce SpreadsheetBench 2, a workflow-level benchmark for spreadsheet agents that covers three task categories: generation, debugging, and visualization. The benchmark is constructed from authentic business data, including financial reports and corporate filings, and is annotated and validated by domain experts. The benchmark contains 321 tasks; each instance averages 11.8 worksheets and requires 593.5 cell modifications, reflecting large multi-sheet workbooks with cross-sheet dependencies. We evaluate eight frontier large language models under a unified multi-turn agent scaffold, and additionally include several LLM-based spreadsheet products as complementary baselines. Results show that current systems remain far from reliable on real-world workflows: the best model achieves 34.89% overall task accuracy, and debugging accuracy is as low as 12.00%. Trajectory analysis and a failure taxonomy further indicate that insufficient spreadsheet inspection and incorrect target-cell selection are the dominant bottlenecks. Together, these findings position SpreadsheetBench 2 as a challenging testbed for advancing reliable spreadsheet automation.

In neural inverse reconstruction, the forward model is typically treated as a fixed simulator that maps a neural scene representation to measurements. This work studies how the parameterization of the forward operator itself shapes the optimization landscape of coherent inverse problems. We introduce Moray, a reconstruction framework built on spectrally parameterized forward operators that bypass conventional time-domain synthesis and instead evaluate measurements directly through closed-form spectral kernels. This parameterization induces a substantially shorter and better-conditioned differentiable graph compared to the time-domain approaches. We further introduce Phase-Coherent Manifold Parameterization, a scene representation that jointly learns scene reflectivity and a deformable surface manifold, allowing the reconstruction to adapt to geometric deviations from the assumed imaging plane and thereby reducing common artifacts in coherent reconstruction. We instantiate these ideas for 77 GHz radar imaging using real measurements from a synthetic-aperture mmWave system. Across multiple challenging scenes, Moray consistently improves reconstruction fidelity, suppresses background artifacts, and degrades more gracefully under data limitations.More broadly, the results suggest that, in neural inverse problems, exposing appropriate analytical structure of the sensing physics to the optimizer can be as important as the choice of neural representation itself.

Hyper-Connections (HC) extend residual connections into multiple streams, employing residual matrices for cross-stream mixing to enrich model expressivity. However, unconstrained mixing disrupts the identity mapping property intrinsic to the residual connection, causing unstable training. To address this, Manifold-Constrained Hyper-Connections (mHC) and its variants restrict these matrices to be doubly stochastic via Sinkhorn-Knopp (SK) algorithm or permutation-based parameterizations. We reveal three limitations of this doubly stochastic constraint: (1) identity degeneration, where learned matrices collapse around the identity initialization and diminish cross-stream interactions, (2) an expressivity bottleneck via spectral collapse, where the doubly stochastic constraint induces feature homogenization, and (3) parameterization inefficiencies, manifesting as unstable SK iterations or the factorial-scaling overhead of permutation-based parameterizations. To overcome these flaws, we propose Spectral-Sphere-Constrained Hyper-Connections (sHC). By confining residual matrices to a spectral norm sphere, sHC prevents spectral collapse and enables selective feature diversification. This shift eliminates unstable SK iterations and factorial parameterization, enabling expressive, non-degenerate residual matrices while preserving training stability.

Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both sparse observations and the governing PDE typically requires per-instance test-time optimization, which becomes a major bottleneck in time-critical applications. To alleviate this, amortized reconstruction using generative models has recently been proposed; however, such approaches rely on full-field HF supervision during training, which is often impractical in real-world settings. From a more realistic perspective, we propose the *Physics-Informed Conditional Schrödinger Bridge* (**PICSB**), which transports an informative LF prior toward an auxiliary-energy-defined, observation-conditioned HF endpoint law without any additional inference-time guidance. To enable learning without HF endpoints, **PICSB** employs an iterative surrogate-endpoint refresh scheme, and directly incorporates PDE residuals into the training objective while enforcing observations via hard conditioning throughout sampling. Experiments on fluid PDE benchmarks demonstrate that **PICSB** enables extremely fast spatiotemporal field reconstruction while achieving strong accuracy under sparse HF supervision.

PDE foundation models (PDE-FMs) promise general-purpose surrogate simulation, yet deploying a pretrained model to a new physical regime inevitably introduces distribution mismatch, demanding task-specific post-training. The dominant approach, supervised fine-tuning (SFT), is fundamentally off-policy: the model trains on pre-generated states but is evaluated autoregressively on its own. In chaotic regimes, this mismatch is catastrophic as per-step errors compound exponentially along the Lyapunov spectrum, and exact pointwise trajectory matching beyond the predictability horizon becomes an ill-posed training signal. To resolve this, we introduce **Solver-as-Teacher (SaT)**, an on-policy post-training framework for PDE-FMs. During training, the student rolls out autoregressively; a numerical solver branches multi-step correction targets from each student-visited state; and the student is updated against these solver corrections, learning to correct its own rollout dynamics. We instantiate SaT in a unified post-training suite with SFT, physics-informed temporal alignment, and learned-teacher on-policy distillation. Across seven 2D/3D PDE benchmarks and six pretrained PDE-FMs, SaT variants achieve the lowest 50-step rollout RMSE in 23 of 24 in-distribution model--task pairs. The best SaT variant reduces RMSE over SFT up to 90.0\%, and SaT gives the lowest RMSE on all seven OOD parameter-shift benchmarks.

We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We establish non-asymptotic convergence guarantees via a KL differential inequality, showing that slowdown improves tracking through contraction of intermediate targets and the complexity of the path. Motivated by training-free guided generation with pretrained score-based generative models, we further introduce Velocity-Aware SALD (VA-SALD), which explicitly incorporates the underlying marginal distributions of the pretrained model and uses slowdown to correct the additional deviation induced by guidance. This yields a principled framework for training-free guided generation for diffusion-based and related generative model families, together with convergence guarantees that clarify the roles of intermediate functional inequalities and guidance bias.

The Gromov--Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical and computational scaling remains an issue for high-dimensional problems. Slicing techniques offer an appealing route to scalability, but, unlike Wasserstein distances, GW problems do not generally admit closed-form solutions in one-dimension. We resolve this problem for the GW problem with inner product cost (IGW), propose a sliced IGW distance that enjoys a natural rotational invariance property, and comprehensively study its structural and computational properties. Numerical experiments validating our theory are presented, followed by applications to heterogeneous clustering of text data and language model representation comparison.