NeurIPS 2026 Papers

We present FRUC, a feed-forward 3D Gaussian splatting framework for dynamic scene reconstruction from uncalibrated collaborative driving views. Existing multi-agent reconstruction frameworks are often hindered by rigid prerequisites, demanding precise spatial calibration and slow per-scene optimization. In this paper, we rethink this task by conceptualizing a distributed multi-vehicle network as a spatio-temporally unstructured ego-centric multi-camera system, where the core challenge lies in enhancing ego-centric occluded geometry through collaboration without degrading the ego's accurately observed visible geometry, while preserving reconstruction efficiency. For efficient reconstruction, FRUC is built upon a visual grounded geometric Transformer backbone to enable one-shot, calibration-free inference from a flexible number of multi-vehicle views. To achieve non-destructive geometric supplementation under uncalibrated cross-agent misalignment, FRUC first introduces an ego-centric causal occlusion field that explicitly derives occlusion evolution as latent priors by modeling agent-wise spatio-temporal correlations. Guided by these occlusion priors, it further formulates cross-agent integration as a deterministic residual denoising process via zero-initialized injection, turning challenging cross-agent fusion into bounded residual learning for robust collaborative blind-spot completion. Through extensive evaluations on the real-world V2XReal and UrbanIng-V2X datasets, FRUC is shown to be a new state-of-the-art for the scene reconstruction of dynamic collaborative driving environments, significantly outperforming existing methods in both rendering quality and efficiency.

Gradient-based attribution methods are model-faithful and scalable, but Integrated Gradients (IG) can be brittle because explanations depend on heuristic baselines, straight-line paths, discretization, and saturation. We propose Fisher--Rao Integrated Gradients (FRInGe), which defines both the reference and interpolation schedule in predictive distribution space. FRInGe replaces input baselines with a maximum-entropy predictive reference and follows a Fisher--Rao geodesic on the probability simplex. The corresponding input-space trajectory is realized through the pullback Fisher metric and stabilized by KL and Euclidean trust regions; attributions are obtained by integrating input gradients along this trajectory. Across six ImageNet architectures, FRInGe most clearly improves calibration-oriented attribution metrics, especially MAS scores, while remaining competitive on perturbation AUC and infidelity.

Asynchronous pipeline parallelism can accelerate distributed model training, but suffers from instability caused by inconsistent gradient computations. We identify a fundamental source of this inconsistency: error signals are coupled to forward-time features, not just parameter states. This means that parameters may evolve as long as they preserve forward-equivalence. Leveraging this insight, we introduce FRESCO, which frames asynchronous updates as a constrained optimization problem: it finds the minimal update modification that acts trivially on the activation subspace. Thus, it serves as a principled generalization beyond the usual synchronous--asynchronous dichotomy: subspace-level protection preserves gradient validity without synchronization stalls, unconstrained-asynchrony instability, or memory overhead that works against pipeline scaling. We demonstrate that FRESCO consistently outperforms representative asynchronous baselines in LLM pre-training, with the gains most pronounced under deep pipelines and high inconsistency levels. Moving beyond the current limitations, FRESCO establishes a new foundation for high-utilization pipeline parallel training, providing a scalable path for large-model development on diverse, resource-constrained systems.

We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step. When such knowledge is never revisited, these losses compound into long-term forgetting—the classical failure mode of continual learning. We introduce FOGO, a scalable optimizer that continuously detects and resolves gradient interference across both regimes. FOGO spectrally orthogonalizes momentum updates to prevent dominant directions from monopolizing optimization, then stores representative past directions in a compact codebook memory built on random projection, where pairwise distances are provably preserved in low-dimensional space. At each step, conflicts between the current update and stored directions are resolved via lightweight orthogonal correction and lifted back through a proximal step, with minimal overhead and no data storage. Across class-imbalanced classification, continual visual learning under domain and class shifts, continual fine-tuning of LLaVA-7B, and GPT-2 pretraining, FOGO consistently improves convergence and knowledge retention, outperforming Adam and Muon.

Progress in AI-based retinal image analysis has accelerated with the adoption of foundation models, yet evaluating their real-world reliability remains challenging. Performance reported on a single dataset does not capture how models behave under dataset shift, across clinical definitions, or for different patient subgroups. This limitation is particularly critical in in medical imaging analysis, where robustness, calibration, and fairness are essential for safe deployment. We introduce FOCUS (Foundation Ophthalmic Cross-Dataset Understanding under Shift), a cross-dataset benchmark for evaluating retinal fundus models that considers vision-only encoder models (VM), vision-language dual-encoder models (VLM), and multimodal large language models (MLLM). FOCUS harmonizes binary diabetic retinopathy, referable diabetic retinopathy, and glaucomatous optic neuropathy tasks across ten public datasets spanning diverse geographies, acquisition conditions, and label protocols. The benchmark evaluates models through a unified analysis layer that measures ranking performance, calibration, subgroup disparities, and image-quality robustness. We present a large-scale evaluation covering 532 base configurations and 228 low-rank adaptation LoRA-adapted MLLMs trained with supervised fine-tuning (SFT). Results show that no model family consistently dominates across tasks and datasets: general VM encoders achieve the strongest average ranking performance, medical MLLMs are competitive but variable, and dual encoder VLMs benefit substantially from lightweight adaptation. Fine-tuning improves in-domain performance but exhibits heterogeneous transfer to external datasets, particularly in calibration. These findings demonstrate that retinal model evaluation is inherently multidimensional. FOCUS provides a practical framework and public benchmark to assess generalization, reliability, and robustness beyond single-dataset leaderboards.

Second-order optimizers (\eg Shampoo, SOAP) have emerged as compelling alternatives to Adam/AdamW and promise faster per-iteration convergence than AdamW in transformer training. However, they still remain costly in practice: even SOAP still pays substantial optimizer-state memory and eigendecomposition overhead. We ask whether the benefits of SOAP can be retained at nearly the efficiency of AdamW. In this paper, we answer yes with a lightweight yet improved SOAP, Factored One-sided Adam-Moment (FOAM). The path to practical impact is twofold: first, FOAM employs input-only preconditioning, dropping the output-side Kronecker factor to reduce memory and remove one eigendecomposition. A per-row Hessian decomposition identifies this as the right one-sided choice when output curvature is approximately isotropic. Second, FOAM replaces SOAP's dense rotated second moment with a factored Adam-moment estimator based on row-by-column statistics. Under the K-FAC Fisher assumption, this estimator is further proven to be pointwise equivalent to SOAP's update. Our empirical results support the claim that FOAM matches strong SOAP and Muon baselines in validation loss across a diverse scale of large language model (LLM) pre-training, uses optimizer state closer to AdamW, runs within Muon's wall time, and retains robustness across a five-fold learning-rate range. Scaling to 760M parameters, FOAM demonstrates that SOAP's gains need not require SOAP's cost.

AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing to tree search and evolutionary optimization, yet which strategy choices drive performance remains unclear. Answering this question requires a benchmark that separates agent strategy (e.g., search topology) from execution infrastructure (e.g., code editor), so that performance differences are attributable to strategy rather than infrastructure, and that provides process-level metrics beyond final scores to analyze exploration behaviors. Existing benchmarks offer limited support. We propose FML-Bench, a benchmark of 18 fundamental ML research tasks across 10 domains that separates agent strategy from execution infrastructure and defines 12 process-level behavioral metrics. Evaluating six representative agents, we find that: (1) strategy complexity alone does not guarantee strong performance: a simple greedy hill-climber nearly matches the best-performing tree-search agent, both well above the remaining agents; (2) our analysis suggests this pattern relates to improvement opportunity structure: greedy search tends to be more effective when opportunities are dense, while tree-search and evolutionary strategies tend to be more effective when opportunities are sparse; an adaptive agent built on this insight switches to broader exploration upon detecting improvement stagnation and outperforms the other six agents, lending initial support to this observation; and (3) process-level analysis reveals that early convergence and directionally focused exploration are significantly associated with final performance, while solution diversity and compute cost are not. Our benchmark is available at: https://anonymous.4open.science/r/Anonymous-78B6.

We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_\theta(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only segmentation and enabling the model to capture temporal evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and segmentation loss. Experiments on remote sensing benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.

Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method that adapts the large model with few trainable parameters. Federated LoRA extends LoRA to federated learning (FL), enabling clients to collaboratively fine-tune a shared model without sharing their raw data by exchanging LoRA parameters. However, due to distributed data heterogeneity, local LoRA updates are highly inconsistent across clients, hindering robust global generalization. Moreover, common direct aggregation introduces aggregated bias, yielding noisy updates and further harming the global generalization. Existing bias-mitigation approaches often rely on symmetrically alternating LoRA modules, overlooking matrix distinct characteristics during training. Therefore, we propose FLoRA-Chef, an asymmetric Federated LoRA that decides when and how to aggregate LoRA components. FLFire adaptively selects the next aggregation target by tracking cross-clientupdate dispersion. FLSauce applies matrix-distinct reweighting to control diversity and prioritize stability. Experiments on various scenarios demonstrate consistent improvements, yielding an average gain of 6.94% over counterparts, validating the benefit of adaptive asymmetric aggregation for federated LoRA.

Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal state, or developmental stage. These processes are often observed only as unpaired longitudinal snapshots: the same cells, neurons, or animals are not tracked as matched trajectories, even though population states are sampled across successive stages. This creates two coupled challenges. First, trajectories must respect curved low-dimensional manifolds embedded in high-dimensional biological measurements. Second, the model must identify when the transport mechanism itself changes. We introduce FLUX (FLow matching for Unpaired longitudinal data with miXture-of-experts), a geometry-aware longitudinal flow-matching framework for joint transport modeling and unsupervised regime discovery. FLUX learns a data-dependent metric from pooled labeled and unlabeled observations, uses that metric to construct geometry-aware conditional paths between adjacent marginals, and decomposes the resulting velocity field into sparse expert vector fields selected by a Straight-Through Gumbel-Softmax router. Across manifold controls, a regime-switching Lorenz system, widefield cortical calcium imaging during associative learning, and embryoid body single-cell differentiation, FLUX reconstructs longitudinal transport while recovering interpretable regime structure. In neural data, the router separates early/intermediate training from late training, coinciding with the behavioral divergence of CS+ and CS- lick indices. In embryoid body differentiation, FLUX separates expression-evolution regimes associated with pluripotent and more differentiated cell populations. Ablations show that mixture-of-experts routing alone is insufficient: FLUX without geometric learning can fit local transport but fails or weakens regime discovery when regimes are encoded in local dynamics. These results suggest that geometry-aware velocity decomposition provides a general strategy for discovering latent biological state transitions from unpaired longitudinal snapshots.

Generative models such as diffusion and flow matching have become dominant paradigms for visuomotor policy learning, yet their reliance on iterative denoising incurs high inference latency incompatible with real-time robotic control. We present a Fast Legendre-polynomial Action policy via Sparse History-anchored flow (FLASH), which replaces discrete action-chunk generation with continuous Legendre polynomial trajectory representation. Specifically, by fitting expert demonstrations under sparse temporal sampling, FLASH enables a single inference to cover a significantly extended action horizon. To further accelerate generation, FLASH initiates the flow matching process from history polynomial coefficients rather than uninformative Gaussian noise, shortening the transport distance and enabling accurate single-step inference. Moreover, analytic polynomial differentiation directly provides desired velocity feed-forward signals to the torque controller without numerical approximation. Extensive experiments on five simulated and two real-world manipulation tasks demonstrate that FLASH achieves state-of-the-art success rates ($\ge 92\%$ across all tasks), a per-episode inference time of $31.40\,ms$ (up to $175\times$ faster than diffusion policies and $18\times$ faster than prior flow matching policies), up to $4\times$ faster training convergence than ACT, and $5\times$ to $7\times$ reduction in controller tracking error compared to discrete-action baselines.

Mixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge lies in designing efficient MILP formulations. Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them. To ensure correctness, we need robust methods to compare formulations. However, existing approaches evaluate formulations numerically and fail to reason about the behavior on general problem instances. We resolve this limitation by introducing a constructive notion of MILP *reformulation* that can be formalized in Lean and *machine-checked*. We develop `FLARE`, a method that uses an LLM-based agent and the Lean proof assistant to verify proposed reformulations against a reference. To evaluate our approach, we introduce *FormulationBench*, a challenging dataset of 20 problems and 116 formulations. `FLARE` outperforms existing methods with **96.9%** accuracy. For cases where formal guarantees aren't necessary, we introduce `FLARE-NL` , an LLM proxy that achieves **99.3%** accuracy. These methods enable reliable verification in automated optimization modeling.

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. Through adapting variants of Legendre Memory, i.e., translated Legendre (LegT) and scaled Legendre (LegS), in the Encoding and Decoding phases, FLAME can effectively capture the inherent inductive bias within data and make efficient long-range inferences. To enhance the accuracy of probabilistic forecasting while keeping efficient, FLAME adopts a Normalizing Flow based forecasting head, which can model the arbitrarily intricate distributions over the forecasting horizon in a generative manner. Comprehensive experiments on four well-recognized benchmarks, including TSFM-Bench, ProbTS, TFB, and GIFT-EVAL, demonstrate that FLAME is a strong out-of-the-box tool of decision intelligence.

Maximum entropy reinforcement learning (MaxEnt-RL) enables robust exploration, yet practical implementations often restrict policies to simple Gaussians. While recent approaches incorporate expressive generative policies via importance-weighted supervised learning, they are prone to importance weight collapse, which limits their scalability in high-dimensional action spaces. Our key insight is to mitigate this limitation by localizing the sampling region, avoiding the weight degeneracy induced by importance sampling over the entire action space. To instantiate this insight, we introduce **FLAG** (**F**low policy with **L**atent-**A**ugmented **G**uidance). FLAG augments the state space with a flow latent variable and optimizes a provably consistent proxy MaxEnt-RL objective. We empirically demonstrate that FLAG enables expressive policy optimization with limited importance samples and scales to high-dimensional control tasks. Furthermore, FLAG achieves state-of-the-art performance across challenging benchmarks.

Multivariate time-series forecasting requires reliable cross-series interaction, yet many attention- and mixing-based models couple heterogeneous variables without explicit entry-stage alignment. Such uncalibrated early interaction can lead to poorly conditioned optimization and degraded generalization when cross-series statistics drift. We propose First-layer Statistical Calibration (FISC), a simple framework that imposes statistical calibration constraints only on the first cross-series coupling layer while leaving deeper coupling layers learnable without repeated calibration. FISC estimates training-set cross-series statistics, converts them into a statistical affinity matrix, and convexly fuses this matrix with learnable coupling parameters. A row-wise simplex projection then yields a nonnegative row-stochastic mixing operator, providing bounded and interpretable entry-layer interaction. By restricting calibration to the first layer, FISC calibrates the entrance interaction without repeatedly constraining deeper feature transformations. To reduce temporal redundancy, FISC further combines a time-domain branch with a data-adaptive orthogonal-domain branch derived from training-time temporal statistics. Experiments on standard long- and short-term forecasting benchmarks show that FISC improves forecasting accuracy while maintaining favorable efficiency. Ablations and non-stationarity analyses further show that first-layer-only calibration consistently outperforms no calibration, last-layer calibration, and all-layer calibration, supporting a simple principle for robust cross-series learning: constrain the entrance interaction with training-set statistics, while letting deeper representations remain data-adaptive.

LiDAR-based 3D Object Detection (L3OD) is a fundamental 3D perception task, yet its performance is often compromised by domain shifts arising from diverse sensor configurations and environmental conditions. Existing Test-Time Adaptation (TTA) methods primarily leverage self-supervision in the spatial domain, yet they frequently confound semantic content with domain style due to the sparse, non-Euclidean nature of point clouds. To overcome this limitation, we propose FIND (Frequency INvariance Disentanglement), a novel TTA framework that precisely disentangles domain-invariant content from domain-specific interference within the frequency domain. Our methodology utilizes B-Spline fitting to construct locally adaptive filters integrated into a dual-stream architecture: an Invariant Stream extracts robust features to guide pseudo-labeling, while a Specific Stream enforces consistency against domain perturbations. By combining invariance-driven alignment with stability-guided regularization, our approach dynamically extracts robust domain-invariant features while suppressing domain-specific interference. Extensive experiments on cross-dataset adaptation and robust corruption benchmarks demonstrate that FIND significantly outperforms state-of-the-art methods.

On-device machine learning exposes deep neural networks as white-box artifacts, making them vulnerable to model-stealing attacks. Trusted Execution Environments (TEEs) mitigate this risk by isolating model execution, but running entire models inside TEEs incurs prohibitive overhead. To balance security and efficiency, prior work proposes TEE-Shielded DNN Partitioning (TSDP), which executes privacy-insensitive components on GPUs while confining sensitive layers to TEEs. We demonstrate that existing TSDP schemes remain vulnerable because exposed GPU weights provide an effective warm start. This allows adversaries to exploit inevitable information leakage and reconstruct high-fidelity surrogates using only a fraction of the training data. To address this vulnerability, we propose FILOsofer, a principled defense that strategically obfuscates exposed weights using Fisher Information. By deliberately rendering leaked weights misleading, FILOsofer steers an adversary’s initialization away from the true optimum. To recover user-side accuracy, we introduce a novel cross-layer LoRA mechanism that efficiently restores performance while storing only lightweight LoRA parameters inside the TEE. Extensive evaluation in real-world settings shows that FILOsofer achieves black-box–equivalent security in the worst case, while reducing computational overhead by more than $50\times$ compared to prior approaches.

The quadratic computational complexity of attention during prefilling remains a primary bottleneck for the efficiency of long-context LLMS. While sparse attention methods mitigate this cost, existing dynamic approaches rely on computationally expensive $O(N^2)$ scoring, which undermines their practical efficiency gains. In this paper, we observe that long-context attention matrices frequently exhibit prominent diagonal stripe patterns, indicating that query-key pairs separated by specific relative distances yield consistently high correlation. Motivated by this, we propose FFTSparse, a hybrid block-sparse attention framework. By formulating the identification of these dominant attention lags as cross-correlation in the frequency domain via the Fast Fourier Transform (FFT), FFTSparse reduces the cost to identify the sparsity pattern of diagonal stripes to $O(N \log N)$. Additionally, we introduce a lightweight identification module that categorizes the patterns of attention heads and then applies tailored masking strategies accordingly. Extensive evaluations demonstrate that FFTSparse incurs a negligible mask construction overhead of merely 0.02\% relative to full attention. Furthermore, it maintains competitive downstream performance on long-context benchmarks while achieving up to a $7.4x$ prefilling speedup at a 128K sequence length, offering a highly efficient solution for long-context inference of LLMS.

Enabling language model agents to autonomously adapt to new environments remains a fundamental challenge. While recent memory-augmented approaches have achieved promising results, they are largely heuristic in design and treat task execution and knowledge construction as decoupled processes. Inspired by how biological systems rapidly adapt through principled acting and learning, we propose \textbf{FEP-Agent}, a framework that grounds LLM agent self-evolution in the \textit{Free Energy Principle (FEP)}. Our system instantiates the two components of Active Inference into: a Worker agent that minimizes \textit{Expected Free Energy (EFE)} during online interaction, balancing goal-directed exploitation with curiosity-driven exploration to probe uncertain environmental dynamics; and a Builder agent that minimizes \textit{Variational Free Energy (VFE)} post-execution, updating a knowledge base while controlling complexity through counterfactual validation. To support scalable knowledge in open-ended environments, we introduce a structured semantic memory that employs associative linking and overlapping communities for efficient retrieval. The excellent results across multiple LLM-agent benchmarks demonstrate that FEP-Agent achieves principled, self-evolving systems.