NeurIPS 2026 Papers

Detecting abnormal changes in server operational data is an important task in modern computing systems, as unexpected shifts in traffic, workload, or performance metrics may indicate service degradation, abnormal access patterns, system failures, or potential security risks. However, server monitoring data are often naturally observed as functional time series, with complex temporal patterns, high dimensionality, and heterogeneous structures, making traditional change-point detection methods difficult to apply reliably in practice. To address this problem, we propose a general change-point detection framework based on a Functional Embedding Neural Network (FENet), designed to provide robust detection across different data types and structural conditions without relying on strong prior assumptions. The proposed method is motivated by the connection between the classical functional CUSUM (FCUSUM) statistic and neural network representations, which allows FENet to learn effective detection rules directly from data while retaining useful statistical intuition. We provide theoretical analysis to formalize this connection and study key properties of the proposed approach. Extensive simulation studies show that FENet achieves competitive performance compared with existing methods and remains robust under limited training data and distributional mismatch between training and test settings. We further apply FENet to real-world server operational metrics, demonstrating its practical value in detecting and localizing abnormal changes in complex server monitoring systems.

Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning method for Large Language Models (LLMs), and practical systems usually need to serve many concurrent LoRA adapters to process different tasks. This is challenging due to the inherent tension between \textit{fairness} and \textit{efficiency}, i.e., achieving fairness requires frequent switching among the adapters such that they have similar service quality, while good efficiency requires to minimize adapter switching to reduce memory movement overheads. Existing schedulers can only achieve a fixed trade-off between fairness and efficiency, and their trade-offs are often suboptimal. To tackle this problem, we propose \textbf{F}airness-aware \textbf{E}fficient \textbf{L}oRA \textbf{P}riority \textbf{S}cheduling (FELPS), which explicitly decomposes fairness and efficiency considerations. In particular, for fairness, FELPS adapts the classic proportional fairness and ensures that the amount of service received by each adapter is proportional to its workload. For efficiency, FELPS prioritizes adapters that are already loaded to GPU memory. To conduct scheduling, FELPS combines to the fairness and efficiency terms with an adjustable weight to achieve arbitrary trade-offs between fairness and efficiency. Evaluations on real workloads show that FELPS achieves a superior trade-off between fairness and efficiency than all baselines.

Light-field (LF) spatial super-resolution hinges on exploiting cross-view correspondences to recover high-frequency details while preserving geometric structure. However, existing methods often rely on implicit feature interaction or coarse disparity maps, which suffers noticeable performance degradation in regions with large disparities. In this paper, we propose Fine-Grained Epipolar Attention Diffusion (FEAD), which adopts epipolar attention as an explicit mechanism to characterize cross-view geometric correspondence along epipolar lines. To enable more accurate feature alignment and long-range spatial–angular interaction, we introduce a diffusion-based epipolar attention refinement strategy that progressively improves the initial attention. With the refined attention as guidance, cross-view features are explicitly warped and fused to enforce geometric consistency and aggregate complementary details across views for fine-grained reconstruction. Extensive experiments demonstrate that FEAD achieves state-of-the-art performance, with particularly strong gains in large-disparity scenarios.

Vision-Language-Action (VLA) models have shown strong potential for general robotic manipulation, but contact-rich tasks still require timely action refinement using force/torque feedback. Existing force-aware VLA models usually align all modalities to a single low operating frequency, which discards high-frequency contact cues that are important for reactive action correction. Additionally, they use static fusion, which cannot adaptively balance visual context and force feedback across manipulation processes. To mitigate these issues, we propose FAVLA, a force-adaptive multi-rate VLA model that explicitly separates low-frequency visual-language reasoning from high-frequency force-conditioned action refinement. Based on the $\pi_0$-style VLM-action expert architecture, FAVLA uses a low-rate VLM to encode visual-language-force context and predict near-future force statistics, while a high-rate action expert refines action chunks using the latest force observations. To decide \emph{when} to update actions, we introduce a Force-Adaptive Multi-rate Inference (FAMI) mechanism, which schedules the action expert's inference rate from predicted future force variance, keeping low-rate reasoning in stable phases and increasing update frequency near contact transitions. To decide \emph{how} to use force, we design a Force-Guided Dynamic Fusion (FGDF) module, which injects high-frequency force features into the action expert and dynamically balances visual-semantic and force cues across manipulation stages. Extensive real-robot experiments on high-precision and contact-rich tasks show that FAVLA outperforms force-aware VLA baselines, achieving an average success rate of 88.8\% and lower peak contact forces during manipulation.

Large language model training with sequence-parallel data-parallel (SP-DP) is bottlenecked by the interplay between intra-step activation redistribution and inter-step gradient synchronization. Existing infrequent-communication methods like FedAvg keep states local or reset them lack convergence guarantees and are unstable under long-context sequence-parallel regimes where each device holds only a partial activation shard; Local Adam synchronizes all states jointly and is convergent but triples the collective payload, and its uniform synchronization period cannot distinguish the semantically mandatory intra-step All-to-All from the temporally deferrable inter-step AllReduce. We propose FAUST (Federated Asynchronous Update with Staggered Timescales), a compiler-orchestrated training system that composes compile-time sequence-parallel graph rewriting with tiered runtime synchronization, assigning independent averaging periods to parameters ($K_x$), first moments ($K_u = 3K_x$), and second moments ($K_v = 6K_x$) according to their impulse-response half-lives, while preserving convergence under the orthogonal collective constraint that intra-step All-to-All must retire before inter-step AllReduce begins. Our analysis shows that while first-moment drift dominates the convergence rate in-distribution, high-fidelity convergence guarantees require at least periodic synchronization of the second moment. Experiments on language models up to 1.3B parameters show that FAUST incurs $303{\times}$ less wall-clock communication overhead than standard DDP and $1.78{\times}$ less than the previous state-of-the-art DES-LOC, while achieving perplexity within $4.6\%$ of the fully-synchronized baseline in $27\%$ less wall-clock time. On bandwidth-constrained links, FAUST delivers $48$--$303{\times}$ speedup over DDP.

Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference methods primarily optimize trajectory smoothness, but neglect the critical latency in reacting to environmental changes. By rethinking the notion of reaction in action chunking policies, this paper presents a systematic analysis of the factors governing reaction time. We show that reaction time follows a uniform distribution determined jointly by the Time to First Action (TTFA) and the execution horizon. Moreover, we reveal that the standard practice of applying a constant schedule in flow-based VLAs can be inefficient and forces the system to complete all sampling steps before any movement can start, forming the bottleneck in reaction latency. To overcome this issue, we propose Fast Action Sampling for ImmediaTE Reaction (FASTER). By introducing a Horizon-Aware Schedule, FASTER adaptively prioritizes near-term actions during flow sampling, compressing the denoising of the immediate reaction by tenfold (e.g., in $\pi_{0.5}$ and X-VLA) into a single step, while preserving the quality of long-horizon trajectory. Coupled with a streaming client-server pipeline, FASTER substantially reduces the effective reaction latency on real robots, especially when deployed on consumer-grade GPUs. Real-world experiments, including a highly dynamic table tennis task, prove that FASTER unlocks substantially improved real-time responsiveness for generalist policies, enabling rapid generation of accurate and smooth trajectories.

Modeling resting-state functional magnetic resonance imaging (rs-fMRI) data is crucial for understanding brain-wide neural activity. However, traditional methods struggle to capture complex temporal dynamics over long horizons, to account for the brain's anatomical spatial structure, and to model high-dimensional ambient signals that lie on a low-dimensional intrinsic subspace. We propose FAST-Brain, a unified flow-aligned spatio-temporal surrogate brain model that addresses all three challenges. At its core is a flow-aligned generative framework that directly predicts the clean blood-oxygen-level-dependent (BOLD) signal, paired with a graph convolutional network that captures spatial structural constraints and a Transformer that models long-range temporal dependencies. Theoretically, we show that under a low-dimensional subspace assumption, the approximation error of our model scales with the intrinsic dimension rather than the ambient dimension, which justifies our direct modeling of the BOLD signal. Extensive experiments on synthetic and Human Connectome Project datasets demonstrate that FAST-Brain achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional signal subspace.

Recent development of large language models (LLMs) has empowered multi-agent systems (MAS) through external skills and inter-agent discussion to solve complex tasks. Although such a paradigm improves the capability of LLM systems, it also introduces substantial efficiency overhead. Existing approaches mainly reduce this overhead through software-level optimization. However, these methods overlook how to exploit the graph-level structure of MAS discussion and its suitability for hardware-aware acceleration, thus causing latency, especially when the number of agents, discussion rounds, or concurrent sessions increases. In this paper, we propose FASD, an FPGA-algorithm co-design framework for acceleration towards graph dynamic system agent discussion. We argue that MAS discussion can be formed as a graph-coupled dynamical system and thus agent activation pruning can be optimized through phase-coupled dynamics method. Based on these insights, we designed the FPGA-aware optimization for the problem. FASD mainly consists of the following parts. Firstly, we profile representative MAS workflows and construct a phase profile for dynamic system modeling. Secondly, the hardware-friendly optimization progress, given the profile, is implemented on FPGA. Through extensive experiments on multi-agent discussion workloads, we demonstrate that FASD improves discussion efficiency while preserving task performance, with stronger benefits as the agent graph becomes larger and more complex.

Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates a practical challenge: a provider may pass governance certification with one generator and later silently switch to a cheaper and lower-quality one for deployment, compromising public trust or even safety in high-stakes domains. We study integrity auditing at deployment time and propose FARE (Forensic Acceptance Region Estimation). A certified generator is enrolled by training FARE on images sampled from that generator. After deployment, FARE can determine whether a generated image is consistent with the enrolled generator-using only that image. FARE's features are based on image generator-specific artifacts that have been proposed for forensic applications. FARE amplifies these features during training by finding hard samples that tighten the acceptance region and increase sensitivity to subtle changes in the certified generator. Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under adaptive attacks that actively manipulate images to evade swap detection.

Diffusion Transformers (DiTs) have established themselves as the preeminent paradigm for high-fidelity generative modeling across various modalities, yet their practical deployment is hindered by the severe computational overhead of iterative sampling. While recent feature-caching methods mitigate this latency through feature reuse or forecasting based on integer-order operators, they remain limited in adapting to the non-stationary and highly curved dynamics of DiT latent trajectories. In this work, we reveal that DiT feature evolution exhibits anomalous diffusion characteristics and a certain degree of spatiotemporal heterogeneity, rendering static, integer-order forecasting operators inadequate and prone to error accumulation. To address this, we propose Fractional Anomalous Dynamics Extrapolation (FADE), a unified framework for training-free diffusion transformers acceleration. FADE models DiT feature evolution via anomalous diffusion and employs a Mittag--Leffler fractional operator to better adapt to high-curvature and non-linear latent trajectories of DiTs. Leveraging the empirical observation that these complex dynamics maintain cross-sample stability, we further construct a Look-Up Table with multiple dimensions through offline profiling. This enables zero-overhead, spatiotemporally adaptive parameter retrieval during online inference. Extensive evaluations across diverse architectures and modalities demonstrate that FADE achieves state-of-the-art acceleration, preserving exceptional generation fidelity under aggressive skip intervals with negligible latency overhead. Our code is anonymously available at https://anonymous.4open.science/status/FADE-8CB5.

Three-dimensional (3D) anomaly detection underpins quality assurance in advanced manufacturing and engineering, capturing subtle defects and deviations that 2D inspection struggles to resolve due to occlusion, viewpoint, and appearance confounds. Existing 3D anomaly detection methods either rely on reconstruction errors or stored normal representations to detect anomalies. While both paradigms have driven substantial progress, they share a fundamental limitation: both operate entirely in the visual domain, missing the rich semantics encoded in natural language, which also typically constrains them to per-category models. We propose FACETS, among the first frameworks that leverage a 3D vision--language model for 3D anomaly detection, opening a new paradigm beyond reconstruction- and memory-bank-based methods. The key idea is to explicitly retain native point-level features that enable reasoning at both patch and point granularities, and further leverage language grounding and cross-granularity geometric modeling along two complementary axes, linguistic semantics and geometric saliency, so that coarse semantic cues and fine-grained geometric details jointly support anomaly detection. FACETS enables unified multi-category anomaly detection, avoiding the per-category models used by most prior methods. Notably, we provide a mathematical analysis of our loss function, offering valuable insights into the substantial improvement FACETS achieves in anomaly localization. Extensive experiments on popular benchmarks reveal that FACETS establishes new SOTA performance across datasets and metrics for 3D anomaly detection, substantially and consistently outperforming existing methods. Code is provided as supplementary material.

Mathematical autoformalization can fail before proof generation, when an informal concept is grounded to the wrong Lean/Mathlib object. Multilingual terminology makes this step non-monotonic: aliases can recover missing concepts, but under collision stress they can also pull retrieval toward nearby, non-interchangeable anchors. We introduce FACBench, a targeted dependency-light benchmark and audit protocol for multilingual formal-anchor grounding before proof generation. FACBench combines a five-language concept inventory with controlled collision queries, Mathlib-graph stress tests, no-label ablations, theorem-like probes, and Lean-facing anchor checks. In controlled collision settings, naive multilingual alias union can place the expected anchor in the candidate list while ranking a nearby distractor first. A simple rule-based source/context guard improves top-1 anchor accuracy over naive multilingual retrieval by about 11 percentage points on controlled collisions and 28 percentage points on a Mathlib-graph-mined controlled holdout. Less scaffolded and theorem-like probes show where this guard stops helping and point to the need for role-aware grounding. FACBench provides reusable collision-focused evaluation and regression-test scenarios for retrieval-augmented autoformalization systems.

This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views. This task is highly ambiguous due to intra-class variations, symmetries, and partial observations. Current solutions struggle to balance reliability and efficiency: deterministic methods are prone to error propagation from intermediate structures, while diffusion models suffer from high inference latency and complex post-hoc candidate extraction. To address this, we present F2G-Pose, a real-time direct, single-pass framework that predicts category-level pose, metric size, and camera-frame completed shape from a single RGB-D observation in one feed-forward pass. F2G-Pose lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and integrates object-level structure with a Mixture-of-Experts Transformer. Joint size prediction and camera-frame shape completion encourage metric consistency through dense geometric supervision. Trained only on synthetic SOPE data, F2G-Pose achieves state-of-the-art accuracy on SOPE and ROPE while running at 28 FPS, and further demonstrates strong camera-frame geometry on HouseCat6D and real-object transfer on HANDAL. Analyses of latency, robustness, stability, shape completion, and GenPose++ candidate behavior further demonstrate the efficiency and reliability of F2G-Pose, with real-world manipulation experiments demonstrating deployment potential under segmented RGB-D inputs.

Vision-language models (VLMs) have rapidly evolved into general-purpose multimodal reasoners with strong zero-shot generalization. In this context, VLMs could greatly benefit the analysis of human gaze and attention, a central task in human behavior understanding that requires reasoning about the physical scene as well as the activity, interactions, and social context. However, the extent to which VLMs can reliably understand human gaze and related attentional behaviors remains largely unexplored. In this work, we present EyeVLM, a systematic evaluation framework for gaze understanding in VLMs across two complementary dimensions: tasks and models. To assess gaze understanding capabilities, we focus on two core tasks. The first, gaze following, i.e., predicting the 2D location where a person is looking, has a geometric and visual processing focus, requiring a precise understanding of the human face, attention direction, 3D scene structure, and spatial grounding of attended targets. The second, social gaze prediction, requires social and relational reasoning over multi-person interactions (e.g., mutual gaze and shared attention), and may benefit more from the LLM semantic reasoning capabilities within VLMs. Regarding models, EyeVLM evaluates these tasks in two ways: a zero-shot setting with a diverse set of state-of-the-art open- and closed-source VLMs, exploring different prompting strategies; and a fine-tuning approach based on task-specific QA pairs, studying the impact of model scale and data scale. As benchmarks, we rely on existing gaze understanding datasets and perform a systematic comparison with state-of-the-art purely visual models. Overall, our results show that current VLMs lack precise gaze understanding capabilities. While standard training helps reduce the gap with visual models, significant improvements are still needed.

Extremely sparse-view computed tomography (xSV-CT) is critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections. In many such scenarios, the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable. This exposes a gap in current sparse-view approaches: Optimization-based methods, which use the sparse measurements directly but lack data-driven priors to suppress artifacts, and learning-based methods, which use strong priors but usually require densely scanned training volumes acquired outside the xSV-CT regime. We study this train-sparse / infer-sparse setting by learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency for tomographic reconstruction. On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.

Linear interpolation between fine-tuned checkpoints has been shown to trace the Pareto front between competing objectives, but whether extrapolative weight averaging can extend such frontiers to new checkpoints useful at inference time, without additional RL training, remains unclear. We study this question in RL for competitive programming, where hidden unit tests under time and memory limits enforce both functional correctness and computational efficiency. Starting from a shared initialization, we train checkpoints under nested unit-test coverage: low-coverage rewards require passing smaller-input tests, while high-coverage rewards require passing progressively larger tests up to the full suite. This sweep reveals the emergence of a correctness–efficiency frontier: on hard problems, higher-coverage reward reduces optimization failures but increases correctness failures, leaving solve rate nearly unchanged. Interpolation between low- and high-coverage checkpoints recovers this frontier, while extrapolation extends it beyond the trained endpoints. Both the frontier and its extrapolative continuation appear across three inference settings, pure reasoning, tool use, and agentic coding, and across two model scales, 32B and 7B. At the problem level, moving along the frontier changes which problems are solved, making extrapolated checkpoints complementary policies in inference-time scaling: ensembles with extrapolative weight averaging broaden coverage and improve pass@250 on LCB/hard by 3.3\% over the best single checkpoint at matched sample budget. These results show that nested unit-test coverage in code RL induces a frontier that extrapolative weight averaging can navigate, extend, and exploit.

Air quality prediction at unmonitored locations is challenging because historical AQI observations are unavailable, while conventional spatiotemporal models largely rely on target histories to learn spatial dependencies. Meanwhile, widely available weather covariates provide useful station-specific evidence for target-absent inference. In this paper, we propose \textbf{ExtrapAir}, a lightweight weather-bridged framework for air quality inference at unmonitored locations. ExtrapAir preserves variable-specific weather patterns through per-variable weather encoding and learns adaptive weather and AQI correlations for spatial transfer. To support robust inference, we introduce \textbf{Activation Attention with Prior Inductive Biases}, which enables non-competitive spatial aggregation, incorporates geographic and semantic priors for station-pair guidance, and avoids full station-to-station attention computation for scalability. Extensive experiments on five real-world air-quality datasets against \textbf{32} baselines show that ExtrapAir reduces MAE by up to \textbf{15.24\%} overall and \textbf{16.01\%} on unmonitored stations, while cutting training time by \textbf{84.9\%} and memory usage by \textbf{76.23\%}, achieving a strong accuracy--efficiency trade-off.

Cognitive Load Theory (CLT) shows that human problem-solving is affected by both intrinsic load, arising from task difficulty, and extraneous load, arising from how task information is presented. LLMs show analogous effects, where presentation alone can affect model performance, but these effects have not yet been systematically isolated from task difficulty. To address this gap, we introduce CogLoadBench, a controlled benchmark that varies five CLT-inspired presentation factors across three reasoning task families. Across models, we find that higher extraneous load conditions often reduce accuracy by either adding competition or complicating the reasoning path. To support diagnosis and mitigation of load-related effects, we propose the Extraneous Load Score (ELS), a prompt-time metric computed from activations that estimates a model's extraneous load, and show that it generalizes across reasoning task types and load levels. We further show that shifting model representations toward lower-ELS directions at inference time can significantly improve model performance without changing prompt content.

Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive models. However, diffusion language models (DLMs) can denoise masked tokens at arbitrary positions. Thus, prefix-only probing reveals only one facet of memorization in DLMs and significantly underestimates the risk of training-data extraction. In order to realistically model extractability of training data in DLMs, we introduce infilling extraction, a data-extraction protocol parameterized by an arbitrary binary mask that subsumes prefix-only probing and accounts for the bidirectional inductive bias of DLMs. Instantiating it on LLaDA-8B and Dream-7B across five extraction modes, three training pipelines, and three corpora covering verbatim and partial leakage, we find that mask geometry governs extractability: edge-conditioned masks extract up to three times more verbatim sequences than prefix-conditioned ones, and bidirectional access opens channels inaccessible in autoregressive models. In particular, we show that a realistic adversary with access to training data where personally identifiable information has been redacted, can even achieve higher recall on extracting redacted email addresses from DLMs than from scale-matched autoregressive models. Tunable parameters for decoding measurably affect extraction performance, while a follow-up supervised finetuning stage does not eliminate the prior memorization.