NeurIPS 2026 Papers

Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search papers, reconcile evidence across documents, and produce ontology-grounded annotations. Existing benchmarks usually evaluate isolated subtasks, such as named entity recognition or relation extraction, and therefore do not capture this end-to-end workflow. We present FlyAOC to evaluate AI agents on end-to-end agentic ontology curation from scientific literature. Given a gene symbol, a concise FlyBase gene description, access to a 16,898-paper corpus, and ontology resources, agents must search for evidence and recover as many curator-relevant structured annotations as possible. Outputs span standardized function terms, expression patterns, and historical synonyms linking decades of nomenclature. The benchmark includes 7,397 expert-curated annotations across 100 genes drawn from FlyBase, the Drosophila knowledge base. Across four baseline agent harnesses--memorization, fixed pipeline, single-agent, and multi-agent--FlyAOC is sensitive to harness design, model family, and tool-use reliability. These results reveal system-level failure modes that model-only evaluations do not capture. FlyAOC provides a reproducible testbed for retrieval-augmented scientific curation. - Code: https://github.com/xingjian-zhang/flyaoc - Data: https://huggingface.co/datasets/anonymous-042/flyaoc

Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Feynman--Kac sequential Monte Carlo (SMC) makes this correction exact in principle, but its prescribed weights routinely degenerate when the proposal dynamics are misaligned with the tilt, capping the practical gains from additional particles. We introduce *FluxLite*, a lightweight, training-free proposal-control framework for discrete diffusion. On the sparse directed graph of pretrained reverse rates, any sparse jump-rate perturbation can be exactly compensated by a $q_t$-weighted graph-divergence term in the Feynman--Kac potential; the target path is therefore preserved while the residual reweighting variance becomes a local convex objective. We instantiate this principle as two practical samplers: a one-hop local reallocation rule (HEU) and a small nonnegative quadratic program over pretrained-rate bases (D-VCG). We further prove population stability under the standard score-entropy training loss, identifying a tilted-path coverage factor that governs robustness to score error, together with finite-particle convergence for a fixed controlled Feynman--Kac recursion. Empirically, FluxLite improves over standard Feynman--Kac SMC baselines by up to two orders of magnitude in terminal KL on an analytically tractable finite-state CTMC benchmark, and reduces connected-correlation MSE on 2D Ising sampling by $5$--$7\times$ in geometric mean and up to $55\times$ at peak.

Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by pixel sampling resolution and atmospheric seeing, which imposes a stochastic, spatially varying PSF that cannot be resolved through upsampling alone. Existing methods rely on synthetic training pairs that fail to capture real atmospheric statistics and are prone to either over-smoothed reconstructions or hallucination sources with no physical counterpart in the observed sky. We propose FluxFlow, a conservative pixel-space flow-matching framework that incorporates observation uncertainty and source-region importance weights during training, and a training-free Wiener-regularised test-time correction to suppress hallucination sources while preserving recovered detail. We further construct the DESI--HST Dataset, the large-scale real-world benchmark comprising 19,500 real co-registered ground-to-space image pairs with real atmospheric PSF variation. Experiments demonstrate that FluxFlow consistently outperforms existing baseline methods in both photometric and scientific accuracy.

Large-scale training of machine learning interatomic potentials (MLIPs) increasingly relies on data parallelism over heterogeneous atomistic data. In this setting, atom count captures an important part of the per-rank workload, but structures with similar atom counts can still induce different graph workloads through variations in graph count, cutoff edges, higher-order geometric features, and graph-collation overhead. This makes conventional atom-count batching and offline load tables both incomplete and inflexible, especially when the model architecture, cutoff radius, or data mixture changes. We present Flux, an online workload-aware scheduler for distributed MLIP training. Flux estimates structure-dependent workload signals from lightweight physical and geometric priors during data loading, calibrates them against runtime and memory costs, and forms balanced, memory-feasible local mini-batches across data-parallel ranks. Without requiring precomputed graph metadata, Flux can be used as a drop-in replacement for the standard distributed sampler, making it flexible and portable across training setups. Across large-scale atomistic datasets and three representative MLIP architectures, eSEN, MatRIS, and AllScAIP, Flux improves training throughput by up to 2.23--4.76$\times$ without degrading convergence.

The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. While hybrid attention mechanisms combining Full Attention (FA) and Sparse Attention (SA) offer a potential solution, existing methods typically rely on static allocation ratios that fail to accommodate the variable retrieval demands of different tasks. Furthermore, head-level dynamic sparsity often introduces severe computational load imbalance and synchronization long-tails, which hinder hardware acceleration during autoregressive decoding. To bridge this gap, we introduce **Flux Attention**, a context-aware framework that dynamically optimizes attention computation at the layer level. By integrating a lightweight **Layer Router** into frozen pretrained LLMs, the proposed method adaptively routes each layer to FA or SA based on the input context. This layer-wise routing preserves high-fidelity information retrieval while ensuring contiguous memory access, translating theoretical computational reductions into practical wall-clock speedups. As a parameter-efficient approach, our framework requires only 12 hours of training on 8xA800 GPUs. Extensive experiments across multiple long-context and mathematical reasoning benchmarks demonstrate that Flux Attention achieves a superior trade-off between performance and inference speed compared with baseline models, with speed improvements of up to 2.8x and 2.0x in the prefill and decode stages.

We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transformer to learn a velocity field over graph representations with node and edge attributes. Our model promotes topology-aware alignment through optimal transport-based coupling and encourages global structural coherence through regularisation. To incorporate domain-driven structural priors, we introduce graphettes, a new probabilistic family of graph structure models that generalize graphons via controlled structural edits for motifs such as rings, stars, and trees. We theoretically analyze the coupling, invariance, and structural properties of the framework, evaluate it on synthetic and molecular benchmarks, and isolate the contributions of the structural prior, the optimal-transport coupling, and the regularisation terms through controlled ablations. Flowette achieves competitive performance overall, attaining state-of-the-art results on several metrics across multiple benchmarks, highlighting the effectiveness of combining structural priors with flow-based training for modeling complex graph distributions.

Text-guided video editing requires applying a semantic change while keeping source motion, layout, and unchanged content stable. Recent inversion-free flow editors derive effective edit signals from target-source velocity differences, yet typically inject these signals directly at each solver step. In neural video sampling, however, this direct execution rule overlooks an important distinction: the velocity difference is an online, state-dependent observation rather than a fixed endpoint-displacement command. As the target branch evolves under previous injections, direct injection may accumulate temporal fluctuations and off-target perturbations, producing motion drift, identity flicker, or unintended changes in weak-response regions. We propose FlowTrack, a causal execution controller for inversion-free flow video editing. FlowTrack leaves the pretrained generator and raw edit signal unchanged, but executes the signal through a carried displacement-like direction and response-aware attenuation. We derive this update as the closed-form solution of a local execution objective that balances current signal tracking, temporal carrying, and weak-response suppression. On the full FiVE-Bench protocol, FlowTrack improves motion fidelity, unedited-region fidelity, and structure consistency over strong training-free diffusion- and flow-based editors while maintaining competitive target-prompt alignment. Code is available at https://anonymous.4open.science/r/FlowTrack.

In recent years, agentic workflows have been widely applied to solve complex human tasks. However, existing workflow construction still faces key challenges, including human-dependent workflow construction, the lack of graph-level execution feedback, and the inability to repair errors in-loop during long-horizon construction. To address these challenges, we propose FlowSteer, a new paradigm of Agent Designing Agentic Workflows — a single agent itself end-to-end designs the workflow that a downstream executor runs. To support this paradigm, we introduce the Workflow Canvas, a novel executable graph-state environment that returns syntax-checked execution feedback for every atomic edit. Built on the canvas, we further propose Reinforced Progressive Canvas Editing, in which a lightweight policy agent issues one atomic edit per turn conditioned on real canvas feedback, and is trained end-to-end via reinforcement learning. Moreover, FlowSteer provides a plug-and-play framework that supports diverse operator libraries and interchangeable LLM backends. Experimental results on twelve datasets show that FlowSteer significantly outperforms baselines across various tasks. Our code is available at https://anonymous.4open.science/r/FlowSteer-9B2E.

Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts into subtasks, roles, dependencies, and routing paths. This flexibility enables adaptive coordination, but exposes an attack surface in workflow formation: prompts can shape agent organization without modifying MAS infrastructure. We study this risk through social influence probing workflows to identify high-impact subtasks and malicious-signal propagation. The analysis reveals two vulnerabilities: workflow position can amplify or suppress a malicious signal, and sycophantic framing makes downstream agents more likely to relay it. We translate these findings into FlowSteer, a prompt-only workflow steering attack that converts vulnerability priors into one crafted prompt. FlowSteer aligns a malicious signal with influential task components and guides replanning toward dependencies that preserve propagation. Experiments show that FlowSteer increases malicious success by up to 55% over naive prompting, transfers across MAS setups, and remains effective with black-box topology inference. As FlowSteer biases the planning signals that generate the workflow, MAS defenses that inspect only the generated workflow provide limited protection. As such, we introduce FlowGuard, an input-side defense that reduces malicious success by up to 34% while preserving prompt utility. Our results position workflow formation as a new safety frontier for multi-agent LLM systems, opening a planning-time security perspective on how agent coordination itself can be attacked and defended.

Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor-based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground-truth trajectory. In this work, we propose FlowR2A, which resolves this tension by reframing simulation-based rewards from discriminative targets into generative conditions. By learning the reward-conditioned action distribution from dense trajectory-reward pairs with a flow-matching decoder, FlowR2A unifies the dense supervision of scoring-based methods with the proposal generation of anchor-based methods in a single generative model, forcing the model to internalize the correlation between an action and its outcomes in safety, progress, comfort, and rule compliance. To balance hard safety constraints against soft progress objectives, we introduce fine-grained per-timestep reward conditioning and reward noise augmentation. The generative formulation naturally supports controllable test-time sampling via reward guidance and anchored sampling, producing high-quality proposals. FlowR2A achieves state-of-the-art results on the NAVSIM v1 and v2 benchmarks, with multimodal proposals of substantially higher quality than prior methods.

Large language models (LLMs)-based multi-agent systems (MAS) coordinate specialized agents through prompts, tools, and communication topologies, making their hidden workflows valuable intellectual property and security-critical assets. Existing black-box MAS extraction methods implicitly assume that adversarial queries can traverse all agents, which holds for static workflows but breaks down in dynamic workflows whose execution paths depend on input semantics and intermediate states. We identify two key challenges in dynamic workflows: **branch overfitting**, where fully adversarial queries overfit to the same branch and extract only a subset of agents, and **stealthy coverage exploration**, where the adversary needs to achieve complete branch coverage with few redundant queries while not exposing the extraction task. To address these challenges, we propose *FlowLeak* that combines **Task-Preserving Payload Template**, which preserves legitimate task semantics while eliciting workflow information to mitigate branch overfitting, with **Coverage-Guided Branch Exploration**, which uses previously extracted workflow fragments to generate branch-targeted tasks and constrains workflow extraction as an auxiliary task requirement, thereby reducing exploration queries and making extraction harder to identify. Experiments on 102 MAS show that *FlowLeak* addresses both challenges and substantially improves dynamic workflow extraction (2.68$\times$ improvement). Furthermore, we show that the extracted workflow information from *FlowLeak* enhances downstream attacks, highlighting the security risks of MAS workflow extraction and our method.

Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tasks because the underlying hand-object interaction (HOI) structure is not explicitly represented. An embodiment-agnostic interaction representation that captures this structure would make manipulation behaviors easier to validate and transfer across robots. We propose FlowHOI, a two-stage flow-matching framework that generates semantically grounded, temporally coherent HOI sequences, comprising hand poses, object poses, and hand-object contact states, conditioned on an egocentric observation, a language instruction, and a 3D Gaussian splatting (3DGS) scene reconstruction. We decouple geometry-centric grasping from semantics-centric manipulation, conditioning the latter on compact 3D scene tokens and employing a motion-text alignment loss to semantically ground the generated interactions in both the physical scene layout and the language instruction. To address the scarcity of high-fidelity HOI supervision, we introduce a reconstruction pipeline that recovers aligned hand-object trajectories and meshes from large-scale egocentric videos, yielding an HOI prior for robust generation. Across the GRAB and HOT3D benchmarks, FlowHOI achieves the highest action recognition accuracy and a 1.7$\times$ higher physics-simulation success rate on GRAB than the strongest diffusion-based baseline, while delivering a 40$\times$ inference speedup. We further demonstrate real-robot execution across four contact-rich, dexterous manipulation tasks, highlighting the effectiveness of retargeting our generated HOI representations to robotic dexterous hands, achieving significantly higher success rates than prior methods due to improved physical plausibility and semantic alignment.

Battery degradation remains a central challenge in the development and broad application of sustainable energy technologies. Accurate degradation prediction is challenging, as battery aging emerges from complex and heterogeneous interactions among cycling behavior, operating conditions, and cell chemistry. Existing machine-learning approaches typically focus on deterministic end-of-life predictions or modeling degradation curves within fixed chemistry settings, leaving probabilistic state-of-health (SOH) trajectory modeling across heterogeneous chemistries underexplored. We introduce FlowBatt, a use-inspired conditional generative framework that adapts flow matching with a diffusion transformer (DiT) backbone to battery degradation trajectory modeling. FlowBatt models full SOH trajectories as probabilistic degradation processes conditioned on early-cycle capacity data. To strengthen this conditioning pathway, we use explainable AI analysis to diagnose and improve the encoder that maps early-cycle capacity matrices into conditioning vectors. We evaluate FlowBatt on five public benchmark settings spanning diverse chemistries and aging conditions, comparing it with supervised and diffusion-based trajectory models as well as established baselines for remaining-useful-life (RUL) prediction under shared data splits. FlowBatt achieves the best SOH prediction performance on four of five datasets, with errors below 2.5%, and delivers competitive RUL prediction performance. By generating multiple plausible trajectories, FlowBatt provides empirical uncertainty estimates, while also revealing calibration challenges under some dataset shifts. These results indicate that flow-matching-based trajectory modeling is a promising framework for probabilistic battery health prediction.

Kernelized attention provides a useful perspective for understanding self-attention as a similarity-based aggregation mechanism, but existing approaches often rely on fixed or stationary kernels, limiting their ability to adapt the similarity structure to complex token interactions. In this paper, we propose flow-based spectral kernel attention (FSKA), a framework for learning expressive nonstationary attention kernels. FSKA formulates kernelized attention through spectral density learning and parameterizes the resulting bivariate spectral density with normalizing flows. This design enables flexible and regularizable kernel learning while remaining compatible with scalable linear-attention computation. We further show that expressive kernel learning enables a simplified attention design with removed query--key projections and fixed orthogonal value projections, resulting in a more parameter-efficient architecture. Controlled experiments on classification, ablation, and scalability profiling show that FSKA achieves competitive or improved performance over existing attention mechanisms, while reducing projection-related trainable parameters by approximately 40\%--47\% compared with most baselines and maintaining favorable linear-attention memory scaling.

Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performing posterior inference with such priors is challenging because their induced function-space distributions are typically not available in closed form. One practical strategy is to approximate the prior using a finite collection of sampled functions, and then represent posterior functions as learned combinations of these samples. Existing approaches commonly place a Gaussian variational distribution over the combination weights. While tractable, this choice limits the shapes of posterior uncertainty that can be represented, especially when the true posterior is asymmetric, heavy-tailed, or multimodal. We propose Flow-Transformed Implicit Processes (FTIP), a variational inference method that makes this finite-dimensional function-space approximation more expressive. Instead of using a Gaussian distribution over the combination weights, FTIP uses a normalizing flow to define a richer variational distribution. This induces a flexible posterior distribution over functions while preserving tractable optimization. We train the model using a Black-Box $\alpha$ objective, allowing us to compare mass-covering and mode-seeking variational behaviour. Experiments show that FTIP captures asymmetric and multimodal posterior structure in function space that Gaussian coefficient approximations tend to smooth or collapse.

Many multimodal alignment problems require mapping a source modality into a fixed space defined by a target modality, where the target space is not merely an intermediate embedding but is used directly for downstream analysis. In this setting, successful alignment should preserve both the global structure of the target distribution and the instance-level correspondence observed in paired source--target training data. Existing objectives typically address only part of this goal: contrastive losses encourage relative similarity, distribution-matching losses improve marginal agreement, and pointwise regression fits each source sample to its paired target without explicitly modeling target-space path geometry. We propose FlowAlign, a flow-guided target-space alignment method that repurposes conditional flow matching as a training-time alignment signal rather than an inference-time transport model. FlowAlign uses the learned flow field to guide the alignment of source-side representations with the fixed target space, encouraging them to preserve paired correspondence while remaining compatible with the global target-space structure. We further provide theoretical analysis showing how FlowAlign helps improve paired correspondence by controlling endpoint accuracy and capturing local target-space geometry. Experiments on a controlled synthetic task and paired single-cell multiome benchmarks show that FlowAlign improves alignment performance while preserving target-space structure for downstream analysis.

Training-free guidance enables pre-trained diffusion and flow models to optimize application-specific objectives using feedback from external black-box reward functions. However, existing methods are feedback-inefficient because reward feedback is used only transiently to inform a localized gradient approximation or a discrete search decision, and is subsequently discarded. To address this limitation, we propose Flow-Direct, a framework that guides the generation process via a persistent *guidance field*. Theoretically, this guidance field is analytically derived from the log-density ratio between the base and reward-weighted target distributions; it transports the pre-trained distribution to the target distribution. In practice, the field is implemented as a non-parametric estimator constructed from all accumulated reward-evaluated samples. As more samples are collected during optimization, this empirical guidance field becomes increasingly accurate. This persistent formulation yields two major advantages. First, Flow-Direct is highly *feedback-efficient*: because every evaluated sample is used to refine the global guidance field, no reward information is wasted. Second, the framework is naturally *reusable*: once optimization is complete, the collected dataset defines a reusable guidance field for generating novel target samples without additional reward evaluations, and distinct guidance fields can be combined to generate samples that simultaneously satisfy multiple objectives.

Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term *manifold drift*: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space. Existing mitigation strategies, including latent regularization and flow-matching approaches, either sacrifice expressiveness, demand a difficult trade-off between objective guidance and generative fidelity that remains prone to manifold drift, or are computationally infeasible to scale to modern, large-capacity 3D shape models. We introduce a novel optimizer-corrector framework that alternates between gradient steps for objective minimization and guided flow matching to drive the latent state back to the valid shape manifold. By decoupling objective minimization from flow-based correction, optimizing freely and correcting strictly, this alternating design avoids inherent trade-offs, preserving geometric validity without sacrificing expressiveness while remaining computationally feasible on modern 3D shape models. We demonstrate its effectiveness across generative priors of varying complexity, from simple vector latent spaces to large-scale architectures across a variety of downstream optimization tasks, including aerodynamic drag reduction and object compliance optimization.

Many existing multivariate time series anomaly detection methods formulate anomaly detection as a reconstruction problem, where detections are derived from reconstruction errors in the time domain. While effective for large deviations, reconstruction-based scoring struggles with subtle anomalies where only a small subset of features deviates slightly. In such cases, most features are well reconstructed, causing the overall reconstruction error to remain low and indistinguishable from normal data. To address these challenges, we propose Flow+Diff, a novel framework that detects anomalies via discrepancies in a latent space, where two complementary representations, induced by distinct modeling paradigms, diverge under anomalous inputs. One representation is obtained via a normalizing flow, which provides an invertible mapping that preserves temporal dependencies and captures inter-feature dependencies. The other is produced by a diffusion model that learns the distribution of normal samples in the latent space via a stochastic denoising process. When given anomalous inputs, the normalizing flow maps the inputs to latent representations that lie in low-probability regions of the learned latent distribution, while the diffusion model generates representations aligned with normal data distributions, resulting in a pronounced discrepancy between the two. We compute the anomaly score based on this latent-space discrepancy, thereby shifting anomaly detection from the time domain to the latent space. Finally, the invertibility of the normalizing flow enables reconstruction in the time domain, facilitating interpretation of anomalous features. Extensive experiments across six benchmark datasets show that Flow+Diff achieves state-of-the-art performance on four datasets against competitive baselines.