We propose a robust texture optimization framework that handles inconsistent AI-generated targets by operating directly within non-differentiable production pipelines. Standard optimization approaches struggle with inconsistent targets, often producing blurry textures and ghosting artifacts. Moreover, existing inverse rendering methods rely on differentiable renderers, causing a rendering gap when assets are deployed in production engines. To address the inconsistencies, we introduce a robust formulation that jointly optimizes a deformation grid and an uncertainty map. This formulation effectively decouples geometric misalignment from semantic hallucinations. To avoid the rendering gap, we leverage finite-difference gradient estimation to operate entirely within standard rasterizers. Extensive experiments demonstrate that our approach recovers sharp, high-fidelity textures from inconsistent AI-generated targets, achieving higher visual quality than existing methods. This makes our technique a practical tool for film production, gaming, and virtual reality, where flexibility and visual quality are paramount.
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We present a compact yet highly expressive design space for shellular metamaterials that support both interactive exploration and inverse design. With only a few dozen charges, our representation generates a wide family of periodic shells, spanning from simple planar configurations to complex TPMS-like morphologies. To enable rapid evaluation, we introduce an efficient GPU-based homogenization pipeline that computes the effective elastic tensor of a candidate design in near real time (∼ 0.4 s), making interactive shellular design practical. Across a large set of synthesized structures, our design space exhibits geometric diversity and spans a broad spectrum of mechanical responses, covering a wide range of effective material properties. This fast evaluation further enables inverse design for target macroscopic properties. In the low-solid-volume regime, the resulting shellular structures achieve performance competitive with state-of-the-art shell-based metamaterials in multiple material properties. Finally, we validate manufacturability by fabricating tiled prototypes via additive manufacturing, demonstrating the potential of our approach for real-world engineering applications.
Inverse sculpting modeling aims to decompose a sculpted mesh into an underlying base shape and reusable geometric details, enabling non-expert users to inherit professional sculpting effort. We present InvSculpt, a novel inverse sculpting framework that decomposes a sculpted mesh into a high-fidelity underlying shape and reusable geometric details represented as a vector displacement field (VDF). Our approach combines semantic priors from text-guided 2D image editing with a 3D rectified flow model to perform inversion-based, mask-free detail removal, recovering an underlying shape that preserves the identity of the source mesh. To represent sculpted details in a lossless and transferable manner, we extract a VDF defined on the surface of the recovered underlying shape and learn a continuous neural representation for geometry-aware transfer. We observe that standard conditional sampling after inversion often suffers from trajectory drift, leading to identity shift and low-frequency distortion. To address this issue, we introduce a trajectory correction strategy that constrains early sampling steps to follow the inversion path, effectively stabilizing subsequent conditional guidance. This design enables robust detail removal and precise extraction of the VDF. Extensive experiments demonstrate that InvSculpt achieves significantly higher-quality mesh decomposition than prior methods and supports a wide range of applications, including geometry redesign and high-fidelity geometric detail transfer.
Off-policy evaluation (OPE) is widely used to compare contextual bandit policies in recommender systems. While there a lot of recent methodological developments, suggesting novel OPE schemes, they are typically validated in the synthetic environments, which not necessarily possess the structure of the real-world datasets. In this paper, we consider the inverse propensity score (IPS) method and its modifications, and study how empirical conclusions inferred from the data depend on evaluation pipelines. We show, that even in the synthetic environments, rankings of different estimators are sensitive to random seeds, log generators, and sample size. Using the popular benchmark, the Open Bandit Dataset, we analyze logging behavior and data characteristics that may violate the i.i.d. assumptions of the log generation.
Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large language models (LLMs) possess structured reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that leverages LLM-guided semantic manipulation under embedding-space similarity constraints. This alignment enforces visual-semantic consistency while selectively perturbing watermark-relevant semantics, ultimately inducing detector misclassification. Extensive empirical results show that CSI consistently outperforms prevailing attack baselines against content-aware semantic watermarking, revealing a fundamental security weakness of current semantic watermark designs when confronted with LLM-driven semantic perturbations.
Recommendation systems play a central role in modern services, yet often treat item cover images as static attributes, overlooking their influence on user decisions. We introduce the task of cover recommendation and study few-shot, interaction-free selection using multimodal user interest profiles. To address cold-start and sparsity challenges in traditional methods, we propose Multimodal Cover Recommendation (MCRec), a framework that leverages Vision-Language Models (VLMs) for multimodal feature extraction. Our approach includes: (1) a Text-Guided Visual Interest Aggregation network (TGVIA) integrating visual and textual representations; (2) multimodal interest embeddings fused via templated prompts; and (3) a multimodal-driven textual inversion technique enabling training-free generalization to new scenarios. We further propose MCRec+, a fine-tuning variant using hybrid sampling. To support evaluation, we construct three benchmarks and propose two new metrics. Extensive experiments show our methods significantly outperform baselines across datasets, especially with average gains of 3.72% in Recall@1, 1.70% in APMS and 1.25% in MPMS on MCRec. Code and data are publicly available from https://github.com/WeixinZhengRec/MCRec.
Large Language Models (LLMs) have been widely adopted in ranking systems, specifically for reranking tasks. Despite the effectiveness, the auto-regressive decoding of LLMs leads to inference latency due to the memory-bandwidth-bound. To alleviate this bottleneck, prior studies have explored single token decoding as an approximation, but they suffer from performance degradation at the tail positions of ranking. In this paper, we propose a Single Forward Pass (SFP)-based method to pre-verify multiple rankings using tree attention, approximating auto-regressive decoding by relevant sub-rankings at each step. However, verifying all possible ranking permutations will lead to factorial-level token computation (N!), making it intractable within SFP. To this end, we first reduce item ranking permutations to combinations (2N), based on the empirical observation that the LLM's next-item generation is less sensitive to the exact ordering of preceding items. Furthermore, we divide the full ranking into K sub-rankings and aggregate their individual probabilities, which further decreases the verification space to 2⌈N/K⌉ • K << 2N. However, naïvely aggregating sub-ranking probabilities leads to inaccurate estimates in listwise ranking. To overcome this, we introduce a Möbius inversion model that explicitly decompose the individual contribution of subsets within a complete lattice, as verified by tree attention. Then, we learn their higher-order effects with a hierarchical self-attention model to reconstruct the full ranking probability. Experiments on both information retrieval and recommendation tasks show the effectiveness of our proposed method.
In real-world web applications, especially those involving sensor networks and Internet of Things (IoT) devices, time series data are often incomplete due to network delays, device failures or logging constraints. Such missing data can severely affect downstream tasks including anomaly detection, recommendation, and A/B testing, making imputation a critical step for reliable web analytics. Diffusion models have recently achieved strong performance for time series imputation. As relevant research progresses, the spectral nature of time series has received increasing attention. However, most ''frequency-aware'' diffusion variants modify either the input or network architecture, but the variance schedule in the forward process remains unchanged, injecting noise with the same variance into every frequency bin. This limitation prevents diffusion from adapting to real data, where spectral energy varies irregularly across frequencies rather than following a simple high–low split. To address these issues, we propose Frequency-Shaped Diffusion (FSDI), which replaces the uniform variance schedule with a data-driven schedule in the frequency domain. Frequency bin variances are estimated from the spectral energy distribution of the data, allocated as inverses of that energy, and then Parseval-calibrated so the total noise energy exactly matches standard diffusion, preserving training stability and ensuring fair comparison. Experiments on real-world datasets demonstrate that FSDI achieves state-of-the-art performance. All code have been made publicly at https://github.com/decisionintelligence/FSDI.
Source localization has attracted increasing attention in recent years due to its vital role in governing the harmful propagation. However, existing localization methods do not fully consider the temporal characteristics in propagation and struggle to leverage the continuous-time information of real-world propagation scenarios. Moreover, the aggregation ability of GNN based localization models is limited by the structural noise commonly present in complicated real-world topologies. To address these challenges, a Spectral Neural Ordinary Differential Equation (SNODE) is proposed to infer the source in real-world continuous-time scenarios. First, the forward propagation is formulated as a flow based ODE system, and the source localization problem is transformed into an inverse ODE modeling task. Second, a neural process based on a graph variational autoencoder is introduced to encode global latent propagation patterns as a conditioning variable for the ODE system. Third, a spectral graph optimization is performed to suppress topological noise by filtering out high-frequency components that degrade the quality of graph aggregation in the neural process. Comprehensive experiments demonstrate that SNODE not only outperforms the optimal baseline in real-world continuous-time propagation scenarios with an average performance improvement of 43.8%, but also achieves consistently superior performance in synthetic discrete-time datasets with an improvement of 4.5%, highlighting its strong generalization ability in different propagation settings. Our code is available at https://github.com/cgao-comp/SNODE.
Federated Unlearning (FU) has emerged as a promising paradigm for effectively removing the influence of specific data of clients from the global model in federated learning. It can further enhance personal data privacy for individual clients and eliminate the impact of malicious attacks like poisoning. Due to these benefits, many FU methods have been analyzed and proposed. Yet, they largely overlook external threats against FU systems, such as gradient inversion attacks that reconstruct client data from shared gradients, posing serious privacy risks to other participating clients. Motivated by this, we propose DIARY, a Differential prIvacy IntegrAted fedeRated recoverY framework to address these dual threats. DIARY presents a privacy budget allocation method, whose insight lies in adaptively assigning appropriate privacy budgets to various training and recovery phases to fully utilize the global privacy budget of each client, balancing the trade-off between privacy and utility. Furthermore, DIARY introduces a novel Federated noise-Immune aNomaly Detection (FIND) module. The deep integration of FIND with two-level selective storage and model rollback mechanisms contributes to model recovery, while significantly reducing the associated overhead. Finally, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods are conducted to validate the effectiveness of DIARY.
Adapter-based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full-parameter fine-tuning for web-scale applications while preserving user privacy. By freezing the backbone and training only compact low-rank adapters, these methods appear to limit gradient leakage and thwart existing Gradient Inversion Attacks (GIAs). Contrary to this assumption, we show that low-rank adapters create new, exploitable leakage channels. We propose the Unordered-word-bag-based Text Reconstruction (UTR) attack, a novel GIA tailored to the unique structure of adapter-based FedLLMs. UTR overcomes three core challenges—low-dimensional gradients, frozen backbones, and combinatorially large reconstruction spaces—by: (i) inferring token presence from attention patterns in frozen layers, (ii) performing sentence-level inversion within the low-rank subspace of adapter gradients, and (iii) enforcing semantic coherence through constrained greedy decoding guided by language priors. Extensive experiments across diverse models (GPT2-Large, BERT, Qwen2.5-7B) and datasets (CoLA, SST-2, Rotten Tomatoes) demonstrate that UTR achieves near-perfect reconstruction accuracy (ROUGE-1/2 > 99), even with large batch sizes—settings where prior GIAs fail completely. Our results reveal a fundamental tension between parameter efficiency and privacy in FedLLMs, challenging the prevailing belief that lightweight adaptation inherently enhances security. Our code and data are available at https://github.com/shwksnshwowk-wq/GIA
Distributed Parameter-Efficient Fine-Tuning (PEFT) has emerged as a promising framework for personalizing Large Language Models (LLMs) by leveraging a collaboration between cloud servers and edge devices. However, this paradigm harbors a critical privacy vulnerability: the cloud can perform inversion attacks on the intermediate results (e.g., embeddings) sent from the edge to reconstruct the user's raw input prompt. Existing privacy-preserving techniques present a difficult trade-off: they are either too computationally expensive or they degrade model performance. To address this challenge, we introduce PrivSplit, a novel and lightweight protocol for prompt privacy in the Distributed PEFT framework. The core idea of PrivSplit is a ''Split and Compensate'' strategy: the edge device splits the input embedding into a public part sent to the cloud and a private part retained locally. The influence of this private part is then perfectly restored using a precise, lossless compensation mechanism during the interaction. We theoretically prove that PrivSplit is secure against both reconstruction and distinguishability attacks. Crucially, our method is perfectly lossless, ensuring that the model's performance is mathematically identical to the non-private baseline. This allows the security parameters to be arbitrarily strengthened without any impact on utility, breaking the conventional privacy-performance trade-off.
Machine learning models for steel property prediction routinely report high-quality metrics with R² > 0.85, yet these results rely on random splits that allow similar grades in both train and test sets. We present SteelAgent, an interactive system that exposes a critical generalization gap: the same models drop from R² > 0.85 to R² = 0.11 on unseen steel families, revealing more than 7 times higher quality degradation. Similarly, conformal prediction coverage degrades from 91% to 38% under distribution shift induced by holding out substantial data sources. SteelAgent combines physics-informed features grounded in classical metallurgy and interpretable models with conformal uncertainty quantification, and an LLM orchestrator that coordinates six domain-specific tools. The system supports property prediction with specification compliance checking, competitive steel comparison, and cost-aware inverse alloy design over 3,741 heat treatment records spanning 1,234 grades. All predictions are traceable through explicit tool calls, ensuring that all physical quantities are computed, not generated. We made the code and data freely accessible to the community.
Autonomous AI agents on embedded platforms require real-time, risk-aware scheduling under resource and thermal constraints. Classical heuristics struggle with workload irregularity, tabular regressors discard structural information, and model-free reinforcement learning (RL) risks overheating. We introduce GraphPerf-RT, an AI technology achieving deep learning accuracy at heuristic speeds (2-7ms). GraphPerf-RT is, to our knowledge, the first graph-grounded infrastructure unifying task DAG topology, CFG-derived code semantics, and runtime context (per-core DVFS, thermal state, utilization) in a heterogeneous graph with typed edges encoding precedence, placement, and contention. The architecture supports multi-task evidential heads with Normal-Inverse-Gamma uncertainty; we validate on makespan prediction for risk-aware scheduling. Experiments on three ARM platforms (Jetson TX2, Orin NX, RUBIK Pi) achieve R^2 = 0.81 on log-transformed makespan with Spearman rho = 0.95 and conservative uncertainty calibration (PICP = 99.9% at 95% confidence). Integration with four RL methods demonstrates that multi-agent model-based RL with GraphPerf-RT as the world model achieves 66% makespan reduction and 82% energy reduction versus model-free baselines, with zero thermal violations.
Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer latent source parameters from sparse, noisy readings. We introduce a belief-contraction-driven approach that unifies inference, stopping, and control. An attention-augmented particle filter stabilizes Bayesian belief updates through ESS-based resampling, feature-aware sparse attention smoothing, and Metropolis–Hastings rejuvenation that preserves the filtering posterior. Belief contraction (posterior dispersion) defines both a termination rule and a goal-aligned intrinsic reward, enabling reinforcement learning without distance-to-source shaping. Across seven field modalities, spatial out-of-distribution tests, and nonstationary source shifts, our agent (ATT-PFRL) achieves higher completion, faster convergence, and more accurate localization than planning and RL+Bayes baselines under similar computation. Fixed-trajectory studies also show improved ESS and lower RMSE, isolating the benefit of the inference layer.
Fluorescence molecular tomography (FMT) serves as a pivotal modality for preclinical tumor screening. While single-view FMT offers distinct advantages in data acquisition efficiency and cost-effectiveness, the scarcity of projection views severely exacerbates photon scattering-induced depth ambiguity, rendering 3D volumetric recovery a highly ill-posed inverse problem. To address these challenges, we propose a physics-inspired spectral topology aware reconstruction network (STAR-Net). Specifically, STAR-Net establishes a synergistic framework: initially, a frequency domain decoupling strategy is introduced to simulate the physical characteristics of diffuse light fields; building on this, a differentiable inverse spectral gating (DISG) mechanism is utilized to explicitly impose low-pass spectral regularization for precise depth recovery; and further, a dual-domain synergistic module is integrated to dynamically fuse spatial and frequency features, achieving high-fidelity detail preservation. Extensive experiments on the Digimouse benchmark demonstrate that the proposed STAR-Net achieves the highest dice coefficient under single-view conditions, validating that explicit spectral topology modeling is a powerful paradigm for mitigating depth ambiguity.
Semantic watermarking methods, embedding identity into the initial latent noise, provide an imperceptible identity traceability for diffusion models in copyright protection and source verification. However, existing methods are highly vulnerable to adversarial attacks, especially geometric transformations (e.g., rotation, cropping) and latent-space manipulations via proxy models, limiting the reliability of watermark verification in practical deployment. To address this issue, we propose a robust and fully reversible, flow-based watermarking framework with dual encoding paths, which preserves high visual fidelity of watermarked image while ensuring resilient identity recovery under adversarial attacks. Specifically, a dual-path network is proposed to encode watermark information into both the generated image and the owner’s secret key. This network leverages Mutual Information Redundancy to recover compromised information under single-path attack, ensuring robust verification. To enhance verification credibility without degrading generation quality, we introduce a joint training strategy that suppresses false positives on negative samples through contrastive learning under fidelity constraints. Furthermore, we employ a backward Euler iteration scheduler for rectified flow models, which facilitate accurate inversion mapping, to enable effective watermark verification, which accurate inversion. Extensive experiments show that our method achieves superior robustness against various adversarial attacks while maintaining high visual quality across diverse generative models.
Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove the influence of previously used training data upon request. Retraining a federated model from scratch is prohibitively expensive, motivating federated unlearning (FU). However, existing FU methods suffer from high unlearning overhead, utility degradation caused by entangled knowledge, and unintended relearning during post-unlearning recovery. In this paper, we propose FedCARE, a unified and low overhead FU framework that enables conflict-aware unlearning and relearning-resistant recovery. FedCARE leverages gradient ascent for efficient forgetting when target data are locally available and employs data free model inversion to construct class level proxies of shared knowledge. Based on these insights, FedCARE integrates a pseudo-sample generator, conflict-aware projected gradient ascent for utility preserving unlearning, and a recovery strategy that suppresses rollback toward the pre-unlearning model. FedCARE supports client, instance, and class level unlearning with modest overhead. Extensive experiments on multiple datasets and model architectures under both IID and non-IID settings show that FedCARE achieves effective forgetting, improved utility retention, and reduced relearning risk compared to state of the art FU baselines. The source code can be found at https://github.com/thechosenchu/FedCARE.
Real-world systems increasingly require coherent reasoning and generation over diverse data modalities simultaneously. Current generative frameworks rely on complex, multi-stage training, resulting in low efficiency due to iterative inference and high computational cost. They also struggle with unified multimodal representation, failing to balance fine-grained details with global structures and long-term dependencies, which limits generative quality and practical usability. To overcome these limitations, we introduce the Inverse Heat Mean Flow (IHMF), a general-purpose solver that is compatible with a wide range of model backbones. Without requiring pre-training, IHMF directly learns an average velocity field through an inverse heat formul ation. By exploiting the inherent scale-space properties of the inverse heat process, IHMF explicitly decomposes multi-scale complexities, thereby simplifying trajectory learning and enabling adaptive topological alignment. Extensive experiments show that IHMF achieves a competitive performance with state-of-the-art methods, providing a robust and unified mathematical framework for various generative tasks. The code is publicly available at https://github.com/CHBonline/IHMF.