The eigendecomposition of the Laplace–Beltrami Operator (LBO) is fundamental to geometric analysis, yet computing its low-frequency eigenmodes remains a significant bottleneck due to the high cost of iterative solvers on large-scale data. To amortize this cost, we introduce the Neural Eigenspace Operator (NEO), a feed-forward framework designed to predict the spectrum directly from point clouds. Crucially, NEO circumvents the ill-posed nature of standard eigenvector regression, which suffers from intrinsic sign flips and rotation ambiguities, by learning the stable, invariant low-frequency subspace instead. Specifically, the network predicts a redundant set of basis functions whose span robustly covers the target eigenspace, allowing for the recovery of accurate eigenpairs via a lightweight Rayleigh–Ritz refinement. To handle irregular sampling, we propose a mass-aware neural operator that incorporates per-point area weights into attention-based aggregation, improving robustness to non-uniform densities and enabling zero-shot generalization across resolutions. Our approach achieves near-linear runtime scaling and substantial wall-clock speedups over iterative solvers at comparable accuracy, and exhibits strong zero-shot transfer to high-resolution point clouds. The resulting eigenpairs support standard spectral geometry tasks, while the raw basis functions provide effective point-wise features for downstream learning. Code: https://github.com/Adversarr/NEO.
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Eigenanalysis of partial differential operators is essential in reduced-order modeling for physical simulation, providing eigenmode representations in elasticity, acoustics, and transient heat transport. While recent neural, mesh-free, and geometry-agnostic approaches enable differentiable eigenanalysis over continuously parameterized shape spaces, boundary conditions of eigenfunctions are limited to Neumann-type (natural) conditions, hindering their applicability in scenarios where boundary conditions must be precisely controlled or optimized. In this work, we focus on Laplace-type operators and extend shape space neural eigenanalysis to handle boundary conditions beyond natural Neumann settings. Building on the same variational, energy-based formulation, we show that Dirichlet, Robin, and mixed boundary conditions can be incorporated without altering the underlying eigenvalue optimization principle. In our formulation, boundary configurations—including boundary placement and Robin coefficients modeling boundary exchange processes—are treated as first-class parameters rather than fixed constraints. When combined with shape-parameterized domains, this leads to a joint shape–boundary space formulation, allowing eigenfunctions and spectra to be evaluated consistently across variations in both geometry and boundary configuration. We conduct experiments on representative applications such as boundary-driven spectral optimization for rigid-walled cavity resonance tuning, reduced-order simulation with changing supports, and analysis of transient thermal behavior under varying boundary exchange conditions. By elevating boundary conditions from fixed constraints to operator-wise parameterization, our approach broadens the applicability of Laplace-type neural eigenanalysis to physical systems where boundary constraints serve as critical design and control variables.
Despite recent progress, text-to-image models still struggle to generate semantically diverse and compositionally accurate multi-person interaction scenes, often collapsing to repetitive layouts, stereotypical poses, and poorly grounded interactions. In this work, we bridge this gap by introducing a dual pose–image representation that brings person-centric structural priors into pretrained diffusion transformers. Our model jointly predicts a 2D pose visualization image and its corresponding RGB image, enabling structure and appearance to co-evolve during learning. At its core, a cross-modal alignment scheme binds text, pose, and image representations, ensuring consistent grounding across modalities. Furthermore, we design an iterative scene construction scheme, progressively generating complex multi-human interactions while effectively decomposing the overall generation complexity. Extensive experiments demonstrate that our method substantially improves prompt alignment and scene diversity in multi-person image generation.
Trying different fabrics on existing garments is a widely applicable problem in digital fashion and computer graphics. A comprehensive transformation involves both material reflectance and geometric deformation from fabric drape. In this work, we focus on the visual aspects of this challenge and simplify fabric try-on to a re-texturing task that replaces garment materials while preserving the original geometry and illumination. Prior approaches perform garment re-texturing via 3D or UV-space reconstruction and rendering, making them sensitive to reconstruction accuracy and rendering fidelity. Recent diffusion-based material transfer methods either lack fine-grained geometric and material control or suffer from domain gaps due to training on synthetic rendered data. We propose a fabric try-on framework that leverages the generative priors of modern image editing models. Motivated by the in-context generation capability of Multimodal Diffusion Transformers, we reformulate garment re-texturing as a two-stage process consisting of fabric removal and fabric application via an intermediate material-normalized image. We further introduce a real-image data curation pipeline and a context-aware tile augmentation strategy, enabling coherent and photorealistic fabric try-on from a single image. Extensive experiments show that our method achieves high-quality, controllable fabric transfer while preserving garment geometry and illumination, without requiring costly reconstruction or rendering pipelines. Our project is available at: https://style3d.github.io/fabric_tryon.
Large Language Models (LLMs) offer significant potential for assisting with the design and implementation of social platform moderation. This study evaluates their efficacy as both a replacement to and an augmentation for human moderators. Using Reddit as a case study, we first demonstrate that LLMs can effectively replicate human moderation decisions, achieving 83.9% agreement. Through a mix of LLMs and human annotations, we then evaluate real moderator decisions, uncovering substantial error rates: 15.2% of removals and 13% of approvals are estimated as incorrect, primarily stemming from moderators citing the incorrect rules (84.3% of errors). This motivates us to propose RuleSharpener, a tool that uses LLMs to diagnose the root causes of moderation errors (e.g. ambiguous rules) and generate clearer, more actionable guidelines. Our evaluation shows that RuleSharpener increases the accuracy of identifying the specific rules violated by violation posts by 38.0%. Our work demonstrates how LLMs can augment human moderation, refine community policies, and reduce operational burdens, offering a better solution for platform governance on the web.
Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the lack of multilingual speech data, unified multimodal architectures, and comprehensive evaluation benchmarks. To address these challenges, we present AgriGPT-Omni, an agricultural omni-framework that integrates speech, vision, and text in a unified framework.(1) First, we construct a scalable data synthesis and collection pipeline that converts agricultural texts and images into training data, resulting in the largest agricultural speech dataset to date, including 492K synthetic and 1.4K real speech samples across six languages.(2) Second, based on this, we train the first agricultural Omni-model via a three-stage paradigm: textual knowledge injection, progressive multimodal alignment, and GRPO-based reinforcement learning, enabling unified reasoning across languages and modalities.(3) We further propose AgriBench-Omni-2K, the first tri-modal benchmark for agriculture, covering diverse speech–vision–text tasks and multilingual slices, with standardized protocols and reproducible tools. Experiments show that AgriGPT-Omni significantly outperforms general-purpose baselines on multilingual and multimodal reasoning as well as real-world speech understanding.
Cultural context profoundly shapes how people interpret online content, yet vision–language models (VLMs) remain predominantly trained through Western or English-centric lenses. This limits their fairness and cross-cultural robustness in tasks like hateful meme detection. We introduce a systematic evaluation framework designed to diagnose and quantify the cross-cultural robustness of state-of-the-art VLMs across multilingual meme datasets, analyzing three axes: (i) learning strategy (zero-shot vs. one-shot), (ii) prompting language (native vs. English), and (iii) translation effects on meaning and detection. Results show that the common ''translate-then-detect'' approach deteriorate performance, while culturally aligned interventions — native-language prompting and one-shot learning — significantly enhance detection. Our findings reveal systematic convergence toward Western safety norms and provide actionable strategies to mitigate such bias, guiding the design of globally robust multimodal moderation systems.
Popularity bias is a pervasive challenge in recommender systems, where a few popular items dominate attention while the majority of less popular items remain underexposed. This imbalance can reduce recommendation quality and lead to unfair item exposure. Although existing mitigation methods address this issue to some extent, they often lack transparency in how they operate. In this paper, we propose a post-hoc approach, PopSteer, that leverages a Sparse Autoencoder (SAE) to both interpret and mitigate popularity bias in recommendation models. The SAE is trained to replicate a trained model's behavior while enabling neuron-level interpretability. By introducing synthetic users with strong preferences for either popular or unpopular items, we identify neurons encoding popularity signals through their activation patterns. We then steer recommendations by adjusting the activations of the most biased neurons. Experiments on three public datasets with a sequential recommendation model demonstrate that PopSteer significantly enhances fairness with minimal impact on accuracy, while providing interpretable insights and fine-grained control over the fairness–accuracy trade-off.
A lesson plan (LP) is a structured guide outlining instructional objectives, methods, and assessments to ensure organized learning. However, existing LP creations are often time-consuming, inconsistent in structure, and lack pedagogical mechanisms for real-time adaptation to diverse learner needs. To address these issues, we propose a co llaborative multi-role a gent framework called COMA for automatic LP generation. COMA formulates LP generation as a collaborative workflow among multiple LLM agents with distinct pedagogical expertise: (1) the novice agent that represents a novice teacher possesses an overarching understanding of the intended lesson flow but demonstrates limited precision in implementing the specific instructional actions; (2) the veteran agent that represents an experienced teacher demonstrates deep familiarity with the curriculum, textbooks, and the knowledge components embedded in each unit; and (3) the master agent that represents a pedagogical expert exhibits a well-developed and confident grasp of lesson progression, with the ability to design, adapt, and implement specific instructional actions effectively and responsively. Through an iterative workflow, these agents collaboratively refine LP quality. Comprehensive experiments across five subjects, using expert-designed metrics, demonstrate that COMA significantly outperforms state-of-the-art methods, producing lesson plans with superior quality, coherence, and pedagogical alignment. Our framework offers a robust solution for generating deployable instructional content at scale. Data and code are available at https://github.com/ai4ed/COMA-LessonPlan.
As model parameter sizes scale into the billions and training consumes zettaFLOPs of computation, the reuse of Machine Learning (ML) assets and collaborative development have become increasingly prevalent in the ML community. These ML assets, including models, datasets, and software, may originate from various sources and be published under different licenses, which govern the use and distribution of licensed works and their derivatives. However, commonly chosen licenses, such as GPL and Apache, are software-specific and are not clearly defined or bounded in the context of model publishing. Meanwhile, the reused assets may also be under free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we address these challenges along two lines: 1) For ML workflow compliance, we propose ModelGo (MG) Analyzer, a tool that incorporates a vocabulary for ML workflow management and encoded license rules, enabling ontological reasoning to analyze rights granting and compliance issues. 2) For standardized model publishing, we introduce ModelGo Licenses, a set of modell-specific licenses that provide flexible options to meet the diverse needs of the ML community. MG Analyzer is built on Turtle language and Notation3 reasoning engine, envisioned as a first step toward Linked Open Data for ML workflow management. We have also encoded our proposed model licenses into rules and demonstrated the effects of GPL and other commonly used licenses in model publishing, along with the flexibility advantages of our licenses, through comparisons and experiments.
Large Language Models (LLMs) are now widely deployed across modern web services, but their safe and trustworthy use in real-world settings critically depends on accurate alignment with human preferences. Preference alignment is typically achieved using methods such as reinforcement learning or direct preference optimization (DPO), whose effectiveness in practice hinges on the quality of labeled preference data. However, a fundamental practical challenge remains: preference datasets inevitably contain noise. Through a systematic analysis of mainstream preference datasets, we find that roughly 25% of preference pairs show clear inconsistencies between reward-model evaluations and human annotations. Such inconsistent examples do not convey reliable preference signals; training directly on them therefore not only fails to improve alignment but can even degrade model behavior. To address this problem, we propose Noise-Aware Preference Alignment for LLMs via Confidence and Polarity Reweighting (AlignCP), a fully automated, human-free framework for noise-aware preference alignment. AlignCP derives two interpretable metrics from reward-model outputs: Confidence, which measures the reliability of each preference judgment, and Polarity, which evaluates whether the reward-model ranking agrees with the original human label. These metrics are combined to assign a training weight to each sample—amplifying high-confidence, label-consistent pairs while down-weighting or discarding low-confidence, contradictory, or otherwise noisy pairs. Unlike methods that rely on human re-inspection, repeated relabeling, or heavy computational reruns, AlignCP performs automated data-quality control with minimal overhead and no human intervention. Empirical results show that AlignCP substantially outperforms existing data-centric alignment approaches on standard preference benchmarks and remains more robust under noisy supervision.
Recent studies have discussed how users are increasingly using conversational AI systems, powered by LLMs, for information seeking, decision support, and even emotional support. However, these macro-level observations offer limited insight into how the purpose of these interactions shifts over time, how users frame their interactions with the system, and how steering dynamics unfold in these human-AI interactions. To examine these evolving dynamics, we gathered and analyzed a unique dataset InVivoGPT: consisting of 825K ChatGPT interactions, donated by 300 users through their GDPR data rights. Our analyses reveal three key findings. First, participants increasingly turn to ChatGPT for a broader range of purposes, including substantial growth in sensitive domains such as health and mental health. Second, interactions become more socially framed: the system anthropomorphizes itself at rising rates, participants more frequently treat it as a companion, and personal data disclosure becomes both more common and more diverse. Third, conversational steering becomes more prominent, especially after the release of GPT-4o, with conversations where the participants followed a model-initiated suggestion quadrupling over the period of our dataset. Overall, our results show that conversational AI systems are shifting from functional tools to social partners, raising important questions about their design and governance.
As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values has become a central objective in trustworthy AI. This challenge becomes even more pronounced when aligning multiple, potentially conflicting human values. Although recent approaches, such as reward reweighting, prompt-based supervised fine-tuning, and model merging, attempt to tackle multi-value alignment, they still face two major limitations: (1) training separate models for each value combination is prohibitively expensive; (2) value conflicts substantially degrade alignment performance. These limitations make it difficult to achieve favorable trade-offs across diverse human values. To address these challenges, we revisit multi-value alignment from the perspective of value consistency in data and propose VC-soup, a data filtering and parameter merging framework grounded in value-consistent learning. We first design a value consistency metric based on the cosine similarity between the reward-gap vector of each preference pair and an all-ones vector, which quantifies its cross-value coherence. We then filter out low-consistency preference pairs in each value dataset and train on the remaining data to obtain smooth, value-consistent policy models that better preserve linear mode connectivity. Finally, we linearly combine these policies and apply Pareto filtering across values to obtain solutions with balanced multi-value performance. Extensive experiments and theoretical analysis demonstrate that VC-soup effectively mitigates conflicts and consistently outperforms existing multi-value alignment methods.
Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from ''one-size-fits-all'' chatbot design toward condition-specific, value-sensitive LLM design.
Large Language Models (LLMs) have revolutionized Recommender Systems (RS) through advanced generative user modeling. However, LLM-based RS (LLM-RS) often inadvertently perpetuates bias present in the training data, leading to severe fairness issues. Addressing these fairness problems in LLM-RS faces two significant challenges. 1) Existing debiasing methods, designed for specific bias types, lack the generality to handle diverse or emerging biases in real-world applications. 2) Debiasing methods relying on retraining are computationally infeasible given the massive parameter scale of LLMs. To overcome these challenges, we propose FUDLR (Fast Unified Debiasing for LLM-RS). The core idea is to reformulate the debiasing problem as an efficient machine unlearning task with two stages. First, FUDLR identifies bias-inducing samples to unlearn through a novel bias-agnostic mask, optimized to balance fairness improvement with accuracy preservation. Its bias-agnostic design allows adaptability to various or co-existing biases simply by incorporating different fairness metrics. Second, FUDLR performs efficient debiasing by estimating and removing the influence of identified samples on model parameters. Extensive experiments demonstrate that FUDLR effectively and efficiently improves fairness while preserving recommendation accuracy, offering a practical path toward socially responsible LLM-RS. The code and data are available at https://github.com/JinLi-i/FUDLR.