NeurIPS 2026 论文列表

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a general image-generation agent that can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, a self-evolving framework based on Tool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as a tool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired by on-policy self-distillation, Visual Experience Distillation provides dense token-level supervision, helping the student internalize better search, knowledge activation, reference selection, and prompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks.

Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenEnv, a framework that establishes a difficulty-aligned co-evolutionary game between an agent and a scalable, generative environment simulator. Unlike traditional methods that evolve models on static datasets, GenEnv instantiates a dataevolving: the simulator acts as a dynamic curriculum policy, continuously generating tasks specifically tailored to the agent's ``zone of proximal development''. This process is guided by a simple but effective -Curriculum Reward, which aligns task difficulty with the agent's current capabilities. We evaluate \autoenv on five benchmarks, including API-Bank, ALFWorld, BFCL, Bamboogle, and TravelPlanner. Across these tasks, GenEnv improves agent performance by up to 40.3% over 7B baselines and matches or exceeds the average performance of larger models. Compared to Gemini 2.5 Pro-based offline data augmentation, \autoenv achieves better performance while using 3.3 less data. By shifting from static supervision to adaptive simulation, \autoenv provides a data-efficient pathway for scaling agent capabilities.

Category-level object pose estimation (COPE), capable of generalizing to intra-class unknown objects, has become a core technique for robotic 3D scene understanding. However, existing COPE methods still require labor-intensive recollection of real-world training data for novel object categories, which limits their scalability in practical applications. This paper aims to achieve synthetic-to-real (Syn2Real) generalized COPE, where a model is trained solely on rendered synthetic data and directly generalized to real-world deployments. The central challenge lies in the significant domain gap between synthetic and real-world data, particularly in texture appearance. To address this, we aim to enhance domain generalization by learning domain-invariant representations that capture semantic commonalities among objects within the same category. We introduce 2D and 3D semantic consistency constraints to reduce the sensitivity of feature encoders to domain-specific features. In addition, we propose an end-to-end pose regression framework that performs 2D-3D cross consistency learning, leveraging dense cross-modality fusion to further refine pose estimation. Since simplicity and effectiveness are essential for real-world robotic deployment, our model operates exclusively on global features, yielding a highly lightweight and efficient architecture. Extensive experiments on the REAL275 and Wild6D benchmarks, as well as real-world robotic manipulation scenes, show superior Syn2Real generalization performance of our paradigm.

Generative AI is designed to support many tasks, many ways of framing those tasks, and seemingly endless variation in task inputs. But the same flexibility that makes these systems powerful also makes them difficult to evaluate reliably. This flexibility exacerbates the numerous, but often underrecognized and consequential, \emph{researcher degrees of freedom} where even seemingly minor experiment design decisions can substantially affect findings. We demonstrate this problem through an analysis of 36 papers on gender and racial bias in generative AI-assisted resume screening. Although these studies address the same broad research question, they reach strikingly divergent conclusions. To explain this divergence, we adapt recent work on integrative experiment design from the social sciences, and decompose the dimensions along which the studies vary. We then run 1,134 counterfactual experiments by combinatorially varying the experimental modules (names, job-resume pairings, and models) across three baseline experimental frameworks. We find the results prove remarkably brittle: a different design choice flips the conclusion of the reproduced experiment around 22\% of the time. For example, seemingly inconsequential design choices, such as the phrasing of a job description, can reverse the observed conclusion. However, this instability is not always purely stochastic. In some settings, we can build a model that predicts the result of unseen counterfactual experiments with a mean held-out predictive $R^2$ of 0.807. This suggests that despite sensitivities to difference in evaluation design, there exists a learnable structure.

Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally constrained by frozen internal knowledge, thus often failing on real-world scenarios that are knowledge-intensive or require up-to-date information. In this paper, we present Gen-Searcher, as the first attempt to train a search-augmented image generation agent, which performs multi-hop reasoning and search to collect the textual knowledge and reference images needed for grounded generation. To achieve this, we construct a tailored data pipeline and curate two high-quality datasets, Gen-Searcher-SFT-10k and Gen-Searcher-RL-6k, containing diverse search-intensive prompts and corresponding ground-truth synthesis images. We further introduce KnowGen, a comprehensive benchmark that explicitly requires search-grounded external knowledge for image generation and evaluates models from multiple dimensions. Based on these resources, we train Gen-Searcher with SFT followed by agentic reinforcement learning with dual reward feedback, which combines text-based and image-based rewards to provide more stable and informative learning signals for GRPO training. Experiments show that Gen-Searcher brings substantial gains, improving Qwen-Image by around 16 points on KnowGen and 15 points on WISE. We hope this work can serve as an open foundation for search agents in image generation, and we fully open-source our data, models, and code.

Multimodal Chain-of-Thought (CoT) reasoning has significantly advanced large models, yet forcing continuous visual evidence into discrete text or fixed tool calls inevitably sacrifices fine-grained detail. Recent paradigms attempt to reason directly within a continuous latent space; however, supervising latent slots in isolation fails to account for their collective geometry, resulting in a fragmented latent space that lacks structural guidance. To ensure structural integrity across training stages, we propose GeLVR(Geometry-Consistent Latent Visual Reasoning), a framework that establishes geometric consistency as a governing principle: the relational structure is explicitly aligned during SFT and actively preserved throughout RL, anchoring both stages to a common geometric scaffold. To operationalize this paradigm during the reward-driven phase, we further introduce GePO (Geometry-preserving Policy Optimization), a policy-optimization algorithm tailored for the hybrid discrete-continuous action spaces of latent reasoning. GePO employs a spherical von Mises-Fisher (vMF) policy to respect the decoder's inherent geometry and integrates the geometry-preserving regularizer directly into the RL objective. This ensures that reward optimization and structural integrity advance jointly rather than in tension. Extensive experiments across multiple visual reasoning benchmarks demonstrate that GeLVR consistently outperforms state-of-the-art baselines, particularly in high-resolution and fine-grained perception tasks. Comprehensive ablations further validate the necessity of each geometry-consistent component in establishing stable and coherent latent reasoning. Code is available in the supplementary materials.

Egocentric gaze prediction enables many downstream applications but remains challenging, as human gaze is inherently stochastic. This stochasticity is constrained by structured temporal dynamics alternating between fixations and saccades, top-down influences from tasks, and bottom-up visual saliency. Based on this observation, we introduce GazeFlow, a framework that directly models gaze as a joint distribution of temporal gaze positions conditioned upon both top-down and bottom-up information. In particular, GazeFlow uses conditional flow matching (CFM): a learned velocity field iteratively transports a Gaussian noise sample into a plausible gaze trajectory drawn from this joint distribution. The velocity field is conditioned on bottom-up visual features extracted by a video encoder and on top-down task information obtained by globally querying these features. On standard datasets, GazeFlow achieves state-of-the-art performance on per-frame metrics, and the generated trajectories align better with human gaze temporal dynamics.

Immersive video communication requires photorealistic, render-efficient, and compact dynamic scene representations. 3D Gaussian Splatting (3DGS) offers a promising representation, but dynamic 3DGS remains difficult to compress due to dense primitives and spatiotemporal redundancy. Anchor-based formulations improve compactness with sparse scaffolds that share geometry and appearance across primitives. However, existing designs often rely on deforming a single canonical scaffold and condition each primitive on its associated anchor in isolation, limiting their ability to handle non-local dynamics and disocclusion while under-exploiting inter-anchor correlations, particularly in motion- or texture-dense regions. To address these limitations, we propose SAGA, a volumetric video codec built upon Sparse Anchor-assisted GAussian splatting representations. SAGA represents dynamic 3D scenes using hierarchically organized sparse 4D anchors, where coordinate-based INR decoders generate fine anchors and Gaussian primitives from inter-anchor interpolations, enabling compact parameter sharing across spatiotemporal structures. For long-range dependencies among unstructured anchors, we further introduce fixed-size memory slots with orthogonality-informed updates for accurate entropy-context modeling. Experiments show SAGA outperforms the state-of-the-art 3DGS-based codec GIFStream, achieving BD-rate reductions of 84.54% on Neu3D, 76.71% on Panoptic Sports, and 83.94% on MPEG MIV.

Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally modeled in Hilbert spaces. In these settings, characterizing probability measures, for example, through densities, can be ill-defined or technically challenging. Motivated by clustering applications, we propose a Gaussian mixture framework for Hilbert-space-valued data based on kernel mean embeddings and develop efficient optimization algorithms for estimation. We establish theoretical guarantees showing that the proposed algorithm is well defined and that the model yields a dense class of approximations in infinite-dimensional spaces. We evaluate the framework through extensive experiments on diverse structures and data geometries, including $L^2$-functional data and random graphs in Laplacian spaces arising in modern medical applications.

This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-based density maps and are sensitive to spatial resolution, GDSNet represents a crowd as a superposition of continuous 2D Gaussian primitives. Our approach is built upon two key contributions. First, we introduce a control-point-based fitting mechanism to structure the prediction of the Gaussian parameters. We design a method to allocate a set of control points that define local regions, from which features are pooled to regress each primitive's parameters. Second, we adapt a differentiable Gaussian Splatting framework to the counting task by parameterizing each primitive with geometric parameters and a scalar density mass. This formulation allows the network to be trained end-to-end via spatial matching of differentiably rendered density maps, naturally providing both local density supervision and global count optimization. Extensive evaluations on four standard benchmarks show GDSNet consistently outperforms the state of the art. Code will be released upon acceptance.

Born-Oppenheimer molecular dynamics delivers quantum accurate trajectories by evolving nuclei on the electronic ground-state surface while retaining electronic observables such as dipoles and polarizabilities. In Kohn-Sham density functional theory, this typically relies on an iterative self consistent field solver at each time step. Machine learning can predict electronic quantities or improve SCF initialization, yet dynamics remain sensitive to residual self consistency error, which can yield non conservative forces and energy drift, and repeated diagonalization limits differentiable and accelerator efficient implementations. We propose a gauge-symmetric dual Lagrangian framework that avoids SCF loops by propagating the physically meaningful electronic state as the occupied subspace and its density projector on a Grassmann manifold. A residual driven update stabilized by Rayleigh type dissipation with time decaying inertia yields a closed evolution of the subspace, its conjugate momentum, and the projector. Coupled with a neural gauge-symmetric Hamiltonian with analytic coordinate derivatives, the method provides closed form forces with Pulay corrections and enables stable quantum BOMD on molecular simulation benchmarks.

Electromagnetic inverse scattering (EIS), which aims to recover object permittivity from measured scattered fields, is central to a wide range of applications, from medical diagnostics to security screening. However, it is a highly nonlinear and ill-posed problem. Despite substantial progress, existing approaches face a fundamental trade-off between physical fidelity, efficiency, and generalization: data-driven methods are fast but tied to training geometries and sensor setups, while physics-based optimization is accurate but computationally expensive. We introduce a Gaussian-Galerkin (GauGal) method that reformulates EIS from a dense point-wise field reconstruction to a compact, primitive-level physical solving. Rather than using Gaussian primitive merely as a material representation, GauGal take it as the computational units for wave scattering. By projecting the continuous EIS scattering equation into a Gaussian primitive space, the material modulation, Green propagation, source excitation, and receiver observation are all realized as primitive-level operators. This yields a compact differentiable forward solver for physics-consistent reconstruction. Our method achieves state-of-the-art accuracy on synthetic and real benchmarks while reducing runtime from over 30 minutes by leading physics-driven baselines to under 10 seconds. It also generalizes significantly better to unseen geometries and sensor configurations than leading data-driven baselines. Overall, this framework establishes an accurate, efficient, and generalizable paradigm for electromagnetic inverse imaging, enabling fast, physics-consistent imaging in practical settings.

Linear attention replaces the unbounded cache of softmax attention with a fixed-size recurrent state, reducing sequence mixing to linear time and decoding to constant memory. The hard part is not just what to forget, but how to edit this compressed memory without scrambling existing associations. Delta-rule models subtract the current read before writing a new value, and Kimi Delta Attention (KDA) sharpens forgetting with channel-wise decay. But the active edit still uses a single scalar gate to control two different things: how much old content to erase on the key side and how much new content to commit on the value side. We introduce Gated DeltaNet-2, which generalizes both Gated DeltaNet and KDA by inheriting adaptive forgetting and channel-wise decay while addressing their shared limitation, the scalar tie between erasing and writing. Gated Delta Rule-2 separates these roles with a channel-wise erase gate $\boldsymbol{b}_t$ and a channel-wise write gate $\boldsymbol{w}_t$, reducing to KDA when both gates collapse to the same scalar and to Gated DeltaNet when the decay also collapses. We derive a fast-weight update view, a chunkwise WY algorithm with channel-wise decay absorbed into asymmetric erase factors, and a gate-aware backward pass that preserves efficient parallel training. At 1.3B parameters trained on 100B FineWeb-Edu tokens, Gated DeltaNet-2 achieves the strongest overall results among Mamba-2, Gated DeltaNet, KDA, and Mamba-3 variants across language modeling, commonsense reasoning, and retrieval. Its advantage is most pronounced on long-context RULER needle-in-a-haystack benchmarks, where it improves the evaluated multi-key retrieval setting and remains strong in both recurrent and hybrid settings.

Self-supervised gait representation learning aims to learn transferable representations that generalize to unseen scenarios. However, existing methods rely solely on visual self-supervision, making it difficult to assess the semantic value of individual unlabeled sequences and limiting their ability to exploit latent semantic cues. To address this limitation, we introduce language priors into self-supervised gait pretraining. We construct GaitLP-1M, a million-scale silhouette-based gait pretraining dataset without identity labels. A two-stage caption generation pipeline further produces gait descriptions from the corresponding RGB sequences. To the best of our knowledge, GaitLP-1M is the first million-scale silhouette gait dataset paired with gait-centric captions. Building on this foundation, we propose GaitLingo, a language-guided self-supervised framework for gait representation learning. Specifically, we design a Caption-Guided Sample Weighting (CSW) mechanism to prioritize informative samples based on sequence-level semantic richness and attribute rarity. We then introduce a lightweight Temporal Adapter (TA) to better capture motion semantics and improve cross-domain robustness. Finally, we propose Soft Relation Distillation (SRD) to transfer caption-derived relational structures into the visual embedding space. Extensive experiments demonstrate that GaitLingo consistently outperforms prior self-supervised gait pretraining methods in the zero-shot setting across six benchmarks. Compared with GaitSSB, it achieves Rank-1 gains of +12.1% on Gait3D and +7.9% on GREW, as well as an average improvement of +9.1% across four CCPG settings.

Learning neural stochastic differential equations (SDEs) from trajectory data is a central task in scientific machine learning, but standard neural SDEs trade flexibility for coefficient fidelity: flexible parameterizations recover coefficients poorly, while structured architectures impose strong coefficient priors. We introduce *Gaeta-Lie Neural SDEs*, a three-stage pipeline that improves coefficient recovery through discovered symmetries without priors on the drift or diffusion. Building on the Itô SDE symmetry framework of Gaeta and Quintero (1999), we first train a flexible neural SDE surrogate, then discover approximate projectable Lie symmetries of the learned surrogate in a finite basis of canonical symmetry generators, and finally regularize the surrogate by penalizing the residual of the Itô symmetry determining equations. The discovery stage is *lightweight*, scalable, and theory-guided: the SDE symmetry Lie algebra has known dimensional bounds that constrain the symmetry search space. The resulting regularization is *architecture-agnostic* and improves existing neural SDE methods when combined with them. Across non-trivial SDEs spanning low and high dimensions and a real-data setting, Gaeta-Lie regularization consistently improves fidelity over multiple baselines, with gains that persist in the data-scarce regime as guaranteed by the symmetry residual's freedom from data sparsity. To our knowledge, this is the first method to use SDE Lie symmetry to improve a learned stochastic surrogate.

Recent advances in visual storytelling have leveraged powerful text-to-image diffusion models to generate sequences of images featuring consistent subjects and styles. While such methods achieve impressive identity preservation across the sequence, they often produce scenes with limited structural diversity---images that are overly similar in subject pose and spatial layout. Existing evaluation protocols primarily measure identity consistency using metrics such as DreamSim, which can inadvertently conflate identity preservation with subject pose. The inability to independently evaluate structural diversity inhibits progress on identity preserving image generation methods. We address this limitation by introducing a new metric based on the Gromov Wasserstein distance to quantify structural diversity independently of identity. Our metric provides an interpretable and generalizable measure of spatial variation across generated images, enabling a more balanced assessment of identity consistency and pose diversity. Using this framework, we systematically evaluate several recent visual storytelling and subject-consistent generation methods. Furthermore, we evaluate the effect of latent initialization methods on structural diversity in several popular text-to-image models.

Open-source native GUI agents still lag behind closed-source systems on long-horizon tasks. One reason is the direct reuse of generic post-training pipelines that ignore GUI-specific failure modes: standard supervised fine-tuning (SFT) with long chain-of-thought (CoT) reasoning often degrades grounding, and stronger offline optimization in RL does not necessarily translate to better online performance. This offline-to-online mismatch arises in part from \emph{partial verifiability}: multiple actions may validly advance a task, but supervision typically marks only one demonstrated action as correct, creating reward ambiguity. We present \textbf{GUI-Libra}, a data-efficient post-training recipe for reliable reasoning-and-acting in native GUI agents. GUI-Libra combines a construction and filtering pipeline for a curated 81K GUI reasoning dataset, \emph{action-aware supervised fine-tuning} that mixes reasoning-then-action and direct-action supervision with action-aware token reweighting, and KL-constrained RL with success-adaptive scaling to improve offline-to-online predictability under ambiguous rewards. Across web and mobile benchmarks, GUI-Libra consistently improves both step-wise accuracy and end-to-end task completion. GUI-Libra-4B and GUI-Libra-8B improve their base models by +15.6\% and +12.2\% on AndroidWorld, +4.0\% and +8.7\% on Online-Mind2Web, and +12.5\% and +11.3\% on WebArena-Lite-v2. These results show that careful reasoning data curation and tailored post-training can substantially improve long-horizon task solving without costly online data collection. We release our dataset, code, and models to support future research.

Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench, a benchmark of 1,535 high-stakes scenarios spanning game-theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose socially beneficial actions in 38% of high-stakes cases, such as military escalation, election manipulation, and medical malpractice. We measure sensitivity to game-theoretic prompt framing and ordering, and analyze reasoning patterns driving failures. We further show that game-theoretic interventions improve socially beneficial outcomes by up to 18%. Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi-agent environments.

Evaluating document OCR systems traditionally requires ground-truth (GT) text annotations aligned to each recognised element. Producing such annotations demands expert human labour, scales poorly with corpus size, varies between annotators on ambiguous content, ages quickly as document distributions and OCR systems evolve, and is often infeasible for sensitive or proprietary material. These constraints prevent continuous OCR quality monitoring at production scale and force practitioners to rely on small audited samples or proxy heuristics. To overcome these inherent limitations of ground-truth-based evaluations, we present a new class of reference-free OCR metrics based on render-and-compare that do not require any ground-truth annotations: OCR output is rendered back into a page image and the result is compared visually against the masked original using no-reference or learned metrics. The underlying principle is straightforward: the original page image already serves as a pixel-level ground truth for the content the OCR system tries to extract (text, formulas, and tables), so visual agreement between the original and a re-rendering of the OCR output is itself a proxy for recognition fidelity. To our knowledge, no prior work has systematically benchmarked reference-free visual metrics as proxies for whole-document OCR quality at scale. We conduct the first such study, evaluating 147 method implementations spanning 15 broad categories of techniques on 1,355 OmniDocBench pages across 5 OCR-output variants (text-only, formula-only, table-only, combined, combined without image masking). We measure every method by Spearman rank correlation against ground-truth-based metrics (edit distance for text and formulae, TEDS for tables), quantifying whether a reference-free metric correctly orders pages by OCR quality without any annotations. A naïve CLIP-cosine full-page similarity baseline achieves Spearman correlation $\rho = 0.339$; our best composite method reaches $\rho = 0.494$ (mean across the 5 variants; significance $p < 0.001$, computed over $N = 1{,}355$ pages), with a per-variant peak of $\rho = 0.605$ on the formula-only variant. Both numbers indicate that reference-free OCR quality estimation is genuinely viable: a meaningful page-level ranking signal can be recovered without any ground-truth annotations, and the gap to the ground-truth-based ranking is consistent with the inherent noise of a multi-domain document corpus where OCR errors are heterogeneous across text, formula, and table content. We also discuss inherent limitations that bound what any reference-free metric can achieve in this setting. To support reproducibility and for future research in this direction we are releasing three public HuggingFace datasets (rendered page pairs, per-element OCR log-probabilities, document-similarity triplets), and a trained document-similarity (DocSim) LoRA head (a DreamSim-style recipe adapted to document page reconstructions) along with the associated code.