Boundary Representation (B-rep) is the most commonly used data format in Computer-Aided Design (CAD) due to its analytical precision and direct support for parametric editing. However, its heterogeneous data structure— continuous parametric geometry with discrete topological graphs—poses fundamental challenges for deep learning models. Existing methods often directly predict the heterogeneous B-rep graph, relying on fixed-size padding or sequential tokenization to handle the varying cardinality of the geometric primitives. These approaches struggle with the combinatorial complexity of CAD models. The discrete, non-differentiable nature of the graph data structure prevents end-to-end optimization of the geometry and watertightness. In this work we introduce DualBrep, a novel continuous representation that unifies B-rep geometry and topology within a fully structured Euclidean domain. DualBrep encodes a CAD model using dual scalar fields: a Signed Distance Function (SDF) to represent the global shape geometry, and an Unsigned Distance Field (UDF) that implicitly encodes the topological structure via a Voronoi partitioning of the surface elements. Rather than processing these fields independently, we compress them into a single latent space. While the dual-field formulation alone already gives reconstruction a flexible, primitive-free segmentation signal that adapts to arbitrary face counts and surface types, the shared latent also makes generation tractable: a Flow Matching model can sample geometry and topology jointly from a single code, avoiding the error accumulation that plagues sequential or autoregressive B-rep predictors. Finally, we use a neural rebuilder to extract explicit B-rep models—comprising both prismatic and free-form primitives—directly from our continuous dual scalar fields. We demonstrate that DualBrep serves as a robust, unified backbone for CAD B-rep learning, achieving strong performance in both reverse engineering from raw point clouds and generative modeling via latent flow matching. Code is available at https://github.com/AutodeskAILab/DualBrep.
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The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language models (LLMs) offer a potential solution for translation, their deployment in data-poor contexts faces a dual challenge: the scarcity of high-quality, culturally relevant data and the prohibitive energy costs of training on massive, noisy web corpora. To resolve the tension between digital inclusion and environmental sustainability, we introduce Sustainable Agent-Guided Expert-tuning (SAGE). This framework pioneers an energy-aware paradigm that prioritizes the ''right data'' over ''big data''. Instead of carbon-intensive training on unfiltered datasets, SAGE employs a reinforcement learning (RL) agent, optimized via Group Relative Policy Optimization (GRPO), to autonomously curate a compact training set. The agent utilizes a semantic reward signal derived from a small, expert-constructed set of community dialogues to filter out noise and cultural misalignment. We then efficiently fine-tune open-source LLMs on this curated data using Low-Rank Adaptation (LoRA). We applied SAGE to translation tasks between English and seven low-resource languages (LRLs) in Southeast Asia. Our approach establishes new state-of-the-art performance on BLEU-4 and COMET-22 metrics, effectively capturing local linguistic nuances. Crucially, SAGE surpasses baselines trained on full datasets while reducing data usage by 97.1% and training energy consumption by 95.2%. By delivering high-performance models with a minimal environmental footprint, SAGE offers a scalable and responsible pathway to bridge the digital divide in the Global South.
Accurate enterprise power consumption forecasting is not only a core component of optimized green energy management but also a key support for promoting the coordinated development of a sustainable society and the digital economy. The temporal fluctuations in power consumption reflect an enterprise's production activity and operational resilience, while credit assessment combined with Web data reveals a two-way coupling relationship between it and energy use: credit changes influence financing and power consumption strategies, while energy anomalies may become early signals of credit risk. However, existing methods still have shortcomings in modeling the co-evolution of Web data and power data. Most models only focus on static or unidirectional correlations, making it difficult to capture the dynamic feedback between credit risk and power consumption; traditional multi-task learning frameworks often rely on parameter sharing or simple attention mechanisms, lacking consistency constraints across time scales and network structures. To address this, this paper proposes CPDGL, a credit-electricity co-evolution framework based on dynamic graph learning, which simultaneously performs power forecasting and credit risk assessment within a unified multi-task system. Its co-evolution path interaction module explicitly models the feedback loop between credit dynamics and power behavior, learning bidirectional causal relationships through an adaptive influence matrix; the semantic path aggregation module integrates static and dynamic features, strengthening cross-modal expression and global reasoning capabilities. Large-scale experiments conducted in a real-world enterprise environment of one of the world's largest power suppliers demonstrate that CPDGL achieves state-of-the-art performance in both power forecasting and credit assessment tasks. The results validate its broad applicability in multi-source Web data fusion scenarios, significantly improving forecasting accuracy and dispatch efficiency in clean energy management, and showcasing practical value and social impact in smart cities and sustainable development.
Online advertising platforms serve as a critical bridge between advertisers and media, requiring precise prediction of user behaviors. Systematic underestimation or overestimation in these predictions can undermine the interests of both parties. Existing calibration methods fall short in addressing two key challenges. First, significant differences in CVR distributions across various targets lead to biased calibration when using global posterior statistics, causing some sample groups to be overestimated while others are underestimated. Second, current field-aware approaches are typically limited to single-field calibration and fail to account for field sensitivity. To overcome these limitations, we propose a Pareto Frontier-based Multi-field Personalized Calibration (PF-MPC) method which formulates multi-field calibration as a multi-objective optimization problem. PF-MPC identifies the optimal Pareto-efficient weight combinations to balance the conflicting calibration errors across different fields. We evaluate PF-MPC on both public calibration benchmark and a large-scale industrial dataset. Experimental results demonstrate that our method achieves significant improvements in calibration performance compared to existing approaches.
Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recommendations. While self-supervised learning (SSL) effectively models the temporal order of events, it typically overlooks the global structure of the user-item interaction graph. To bridge this gap, we propose three model-agnostic strategies for integrating this structural information into contrastive SSL: enriching event embeddings, aligning client representations with graph embeddings, and adding a structural pretext task. Experiments on four financial and e-commerce datasets demonstrate that our approach consistently improves the accuracy (up to a 2.3% AUC) and reveals that graph density is a key factor in selecting the optimal integration strategy.
Large Language Models (LLMs) enable Web-scale multilingual content analysis but face critical challenges in scaling to long-tail languages and ensuring robustness. Current research is split between two isolated trajectories: a Macro-Paradigm (system-level engineering) and a Micro-Paradigm (internal model intervention). We argue that a true Web-scale solution requires their systematic fusion, balancing large-scale data processing with fine-grained model control. We introduce the Control-Tower Framework (CTF), a novel methodology designed to systematically enhance powerful, pre-trained base models. Inspired by control-theoretic ideas, CTF transforms a base model into a controllable analysis engine via three synergistic stages: (1) Micro-enhanced pre-training that injects linguistic priors (e.g., syntax) to build a robust semantic foundation; (2) a control-inspired fine-tuning stage where a heuristic dynamic feedback loop, driven by micro-level error signals (e.g., knowledge editing loss), actively adjusts the macro-scale learning curriculum; and (3) Macro-optimized inference using Minimum Bayes Risk (MBR) decoding to enhance robustness on noisy user-generated content (UGC). Extensive experiments show that CTF surpasses the leading open-weights model, Tower+ 9B FT, by a substantial margin of +2.18 XCOMET-XXL on low-resource languages (WMT24++). Crucially, CTF unlocks large-scale cross-lingual Web mining by converting unstructured Web text into machine-analyzable assets. We evidence this with substantial gains across both document-level (on MARC) and aspect-based (on SemEval-2016) sentiment analysis tasks. Our work offers a practical pathway toward building more reliable, scalable, and controllable global information ecosystems.
Multimodal sentiment analysis (MSA) has witnessed remarkable advancements in recent years. Existing MSA methods focus primarily on learning coarse-grained representations from different modalities to perform global cross-modal alignment or fusion. However, these approaches often neglect fine-grained valuable sentimental clues derived from local cross-modal interactions. Furthermore, the cross-modal alignment and fusion of complex global and local cross-modal information pose significant challenges in MSA tasks. To address this issue, we propose a novel MSA framework that simultaneously captures coarse-grained and fine-grained cross-modal sentiment cues through global and local cross-modal alignment and fusion. Our approach consists of three key components: i) optimal transport-based global and local cross-modal alignment, which separately aligns valuable global and local sentiment clues across modalities, ii) global and local cross-modal gated attention, which respectively fuse the aligned global and local cross-modal representations, and iii) prototype-informed information bottleneck, which utilizes learnable sentiment prototypes and contrastive prototype match to eliminate redundant cross-modal information at both global and local levels. Extensive experiments conducted on two publicly available MSA datasets demonstrate the effectiveness and superiority of our proposed model.
The strategic planning and reliability of modern web services, from cloud infrastructures to e-commerce platforms, increasingly hinge on accurate long-term forecasting of high-dimensional time series. A fundamental challenge within this task is modeling the intricate point-to-point dependencies that span across both time and variable dimensions. However, many existing methods face restricted direction modeling and computational inefficiency due to their reliance on localized paradigms and Transformer architectures. To address these, we propose replacing Self-Attention with autocorrelation, achieving two key innovations: 1) We propose calculating autocorrelation across both variable and time dimensions, which is a global paradigm, to model point-to-point dependencies. 2) Our proposed Spectral Product Mechanism (SPM) optimizes traditional autocorrelation into a data-driven form suitable for deep learning. Moreover, SPM reformulates autocorrelation as spectral product and reduces the complexity from O(N2) to O(NlogN), while its Hadamard product-based correlation score matrix further reduces core computation to O(N) compared to Self-Attention's O(N2) matrix multiplication. We further propose a Generalized Spectral Product Mechanism (GSPM), which extends traditional autocorrelation by mapping input into distinct feature representations, enabling modeling of complex dependencies through cross-feature correlations. SPM and GSPM surpass current state-of-the-art (SOTA) methods on 14 authoritative benchmarks, collectively securing the top rank on 22 out of 28 metrics, while ranking 1st in time, 2nd in memory, and 2nd in parameter overhead. Source code is available at: https://github.com/lxy-PhD2022/SPM.
Dynamic Facial Expression Recognition (DFER), as a crucial part of affective computing, has broad applications in many areas such as human-computer interaction and social media content analysis. Effectively integrating multimodal information, particularly audio-visual signals, remains the core challenge. However, existing approaches are generally constrained by two major challenges: (1) shallow and static fusion mechanisms, which fail to capture the dynamic co-evolution of audio-visual features during deep interaction; (2) implicit and coarse alignment strategies, which are insufficient to bridge the modality gap caused by heterogeneous feature distributions. To address these issues, we propose a novel framework, BHGap, which integrates deep iterative prompt generation with multi-stage feature alignment and fusion. The key idea is to reformulate audio-visual collaboration from a one-shot fusion event into a continuous, reciprocal generation process that spans every layer of frozen backbone encoders. Specifically, we design a State Space Model (SSM)-based cross-modal prompt generator that dynamically produces ''guidance prompts'' for the counterpart modality at each encoding layer, thereby enabling deep and fine-grained feature co-evolution. Beyond encoding, we further introduce a coarse-to-fine multi-stage alignment module: at the macro level, low-rank adversarial alignment is employed to establish spatio-temporal congruity between audio and video while reducing global distributional discrepancies; at the micro level, Maximum Mean Discrepancy (MMD) constraints combined with implicit differentiation optimization ensure fine-grained statistical consistency and semantic alignment. Extensive experiments on the public DFEW and MAFW datasets demonstrate that our method achieves state-of-the-art performance, offering a new paradigm of deep iterative fusion and explicit alignment for multimodal emotion recognition. Code is available at https://github.com/NDYZD666/-public-BHGap.
Multi-behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks and carts) are often noisy, weakly correlated, or semantically misaligned with the target behavior (e.g., purchase), which leads to biased preference learning and suboptimal performance. While existing methods attempt to fuse these heterogeneous signals, they inherently lack a principled mechanism to ensure robustness against such behavioral inconsistency. In this work, we propose Robust Multi-Behavior Recommendation towards Target Behaviors (RMBRec), a robust multi-behavior recommendation framework grounded in an information-theoretic robustness principle. We interpret robustness as a joint process of maximizing predictive information while minimizing its variance across heterogeneous behavioral environments. Under this perspective, the Representation Robustness Module (RRM) enhances local semantic consistency by maximizing the mutual information between users' auxiliary and target representations, whereas the Optimization Robustness Module (ORM) enforces global stability by minimizing the variance of predictive risks across behaviors, which serves as an efficient approximation to invariant risk minimization. This local–global collaboration bridges representation purification and optimization invariance in a theoretically coherent manner. Extensive experiments on three real-world datasets demonstrate that RMBRec not only outperforms state-of-the-art methods in accuracy but also maintains remarkable stability under various noise perturbations. For reproducibility, our code is available at https://github.com/miaomiao-cai2/RMBRec/.
Large language models have given rise to interactive recommendation agents (IRAs). Through proactive clarification, tool invocation, and dynamic dialogue, IRAs shift recommender systems from passive prediction to interactive, proactive intelligence. For training IRAs, agentic reinforcement learning offers a natural pathway, as it enables models to learn interactive capabilities directly from environmental feedback without requiring costly annotated data. However, this process faces three key challenges: credit assignment in multi-turn interactions, efficient exploration in large action spaces, and coordinated learning of multiple interactive skills. To tackle this challenge, we present a new preference optimization paradigm, GIMO, which treats the interaction and learning process of IRAs in sparse environments as the continuous fulfillment and stimulation of three intrinsic drives: Autonomy, Competence, and Relatedness. Instead of relying solely on external rewards, GIMO estimates the potential between adjacent states in a generative manner to construct intrinsic rewards. Such a generative motivation structure not only enables fine-grained credit assignment, but also naturally inspires a hint-guided action proposal mechanism that facilitates efficient exploration. Furthermore, during the multi-skill coordination training stage, we introduce an explicit KL regularization into policy orchestration to prevent global policy collapse. Theoretically, we prove that GIMO guarantees global policy consistency, and empirically, experiments in interactive recommendation environments built from three datasets demonstrate that GIMO consistently outperforms existing methods. Our code is anonymously available at https://github.com/XueyangFeng/GIMO.
Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process of FL incurs considerable computation and communication overhead, which is unfriendly for resource-constrained devices. One-shot FL is a promising approach to addressing communication issues inherent in conventional FL, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups.
The rise of Internet of Vehicles (IoV) technologies is transforming traffic management from isolated control to a collective, multi-vehicle process. At the heart of this shift is multi-vehicle dynamic navigation, which requires simultaneously routing large fleets under evolving traffic conditions. Existing path search algorithms and reinforcement learning methods struggle to scale to city-wide networks, often failing to capture the nonlinear, stochastic, and coupled dynamics of urban traffic. To address these challenges, we propose CityNav, a hierarchical, LLM-powered framework for large-scale multi-vehicle navigation. CityNav integrates a global traffic allocation agent, which coordinates strategic traffic flow distribution across regions, with local navigation agents that generate locally adaptive routes aligned with global directives. To enable effective cooperation, we introduce a cooperative reasoning optimization mechanism, in which agents are jointly trained with a dual-reward structure: individual rewards promote per-vehicle efficiency, while shared rewards encourage network-wide coordination and congestion reduction. Extensive experiments on four real-world road networks of varying scales (up to 1.6 million roads and 430,000 intersections) and traffic datasets demonstrate that CityNav consistently outperforms nine classical path search and RL-based baselines in city-scale travel efficiency and congestion mitigation. Our results highlight the potential of LLMs to enable scalable, adaptive, and cooperative city-wide traffic navigation, providing a foundation for intelligent, large-scale vehicle routing in complex urban environments. Our project is available at https://github.com/usail-hkust/CityNav.
Metro flow prediction is a critical application in smart city and Web of Things infrastructures, essential for optimizing urban mobility. However, building such predictive systems faces three key challenges: (1) the fragmentation of multimodal spatiotemporal data, (2) the inefficiency of existing models in capturing long-range dependencies, and (3) the data silos and privacy concerns inherent in distributed station infrastructures. To address these challenges, a multimodal federated learning framework named MF3 (Mamba-Transformer-Federated Metro Flow Prediction) is proposed. First, a multimodal alignment (MA) module is designed, where cross-modal alignment attention bridges visual and spatiotemporal features, thereby enhancing feature complementarity and alignment. Second, a dual-path Mamba-Transformer (DMT) module is designed, in which Mamba's linear long-range memory and the Transformer's global perception operate in parallel, reducing information loss. Third, a blockchain-based federated reputation (BFR) module is established to perform personalized federated learning, thereby enhancing privacy protection. Finally, extensive experiments on real metro datasets from Hangzhou and Shanghai demonstrate that MF3 achieves superior performance in terms of prediction accuracy. In summary, the proposed MF3 framework provides a new feasible paradigm for metro flow prediction, supporting urban traffic optimization, metro operation and scheduling, and the development of smart city and Web of Things infrastructures.
Adaptive traffic signal control (ATSC) is essential for mitigating urban congestion in modern smart cities, where traffic infrastructure is evolving into interconnected Web-of-Things (WoT) environments with thousands of sensing-and-control nodes. However, existing methods face a critical scalability-coordination tradeoff: centralized approaches optimize global objectives but become computationally intractable at city scale, while decentralized multi-agent methods scale efficiently yet lack network-level coherence, resulting in suboptimal performance. In this paper, we present HALO, a hierarchical reinforcement learning framework that addresses this tradeoff for large-scale ATSC. HALO decouples decision-making into two levels: a high-level global guidance policy employs Transformer-LSTM encoders to model spatio-temporal dependencies across the entire network and broadcast compact guidance signals, while low-level local intersection policies execute decentralized control conditioned on both local observations and global context. To ensure better alignment of global-local objectives, we introduce an adversarial goal-setting mechanism where the global policy proposes challenging-yet-feasible network-level targets that local policies are trained to surpass, fostering robust coordination. We evaluate HALO extensively on multiple standard benchmarks, and a newly constructed large-scale Manhattan-like network with 2,668 intersections under real-world traffic patterns, including peak transitions, adverse weather and holiday surges. Results demonstrate HALO shows competitive performance and becomes increasingly dominant as network complexity grows across small-scale benchmarks, while delivering the strongest performance in all large-scale regimes, offering up to 6.8% lower average travel time and 5.0% lower average delay than the best state-of-the-art.
Accurate multivariate time series forecasting (MTSF) is critical for intelligent web services in Web of Things. When confronted with unseen multivariate time series (MTS), the industry typically invests significant time and resources in training multiple models to identify the optimal model for deployment. This paper proposes a novel, efficient, and scalable MTSF model selection method that directly selects suitable MTSF methods based on data characteristics without extensive model training. Model selection is a core component of AutoML, which has made significant progress in recent years. However, existing methods incur high operational costs and cannot be directly applied to MTSF tasks. Moreover, there is a lack of a comprehensive and cohesive public time series library for MTSF model selection. To address these challenges, we compile the first large heterogeneous labeled MTSF model selection dataset, called the ModelPile, which covers 41 mainstream datasets across 11 domains. We then propose AutoMTSF, a large model-enabled model selection method that transforms the MTSF model selection problem into a time series classification problem and utilizes the ModelPile to unlock large-scale multi-dataset training. AutoMTSF first uses the pre-trained large model to encode raw MTS. Given the coarse-grained limitations of large model encoding, Recursive Temporal Pattern Feature (RTPF) is proposed to capture both fine-grained and global temporal feature evolution, thereby effectively mapping data characteristics to the MTSF method space. Experiments comparing AutoMTSF with 2 baselines, 17 MTSF methods, and 4 large time series models show that AutoMTSF outperforms state-of-the-art methods while maintaining comparable execution time. This work represents a critical step in validating the accuracy and efficiency of large model-enabled classification for MTSF.
Depressive disorders represent a major global public health challenge. As an increasing number of individuals share their emotional experiences and concerns on social media, researchers have shown growing interest in leveraging such data for early depression screening. However, most existing methods rely on a fixed model and a singular reasoning paradigm, which constrains their adaptability to depression detection. The limited availability of mental health-related data and variability in training data distributions across different LLMs hinder their consistent and comprehensive understanding of diverse psychological symptoms. In this paper, we propose AdaDepression, a framework that enables explainable depression screening through a two-hop retrieval algorithm to identify symptom-relevant posts and a two-stage adaptive routing mechanism for selecting appropriate reasoning strategies and LLMs. Specifically, we first collect representative posts from the training dataset to capture the real-world symptom expressions, and then utilize these posts to retrieve symptom-relevant posts from the user's posting history. Subsequently, we employ the Mixture of Routers (MoR), which integrates the Mixture of Experts (MoE) into the routing mechanism to select the optimal reasoning strategies and LLMs in a cascaded manner. Finally, we complete the standardized psychological questionnaire using the selected LLMs and reasoning strategies. Experimental results on the Reddit-based benchmarks demonstrate the effectiveness of the proposed method, outperforming existing studies on various metrics. Our code is released at https://github.com/MindIntLab-HFUT/AdaDepression.
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.
Multimodal desire understanding, a task closely related to both emotion and sentiment that aims to infer human intentions from visual and textual cues, is an emerging yet underexplored task in affective computing with applications in social media analysis. Existing methods for related tasks predominantly focus on mining verbal cues, often overlooking the effective utilization of non-verbal cues embedded in images. To bridge this gap, we propose a Symmetrical Bidirectional Multimodal Learning Framework for Desire, Emotion, and Sentiment Recognition (SyDES). The core of SyDES is to achieve bidirectional fine-grained modal alignment between text and image modalities. Specifically, we introduce a mixed-scaled image strategy that combines global context from low-resolution images with fine-grained local features via masked image modeling (MIM) on high-resolution sub-images, effectively capturing intention-related visual representations. Then, we devise symmetrical cross-modal decoders, including a text-guided image decoder and an image-guided text decoder, which enable mutual reconstruction and refinement between modalities, facilitating deep cross-modal interaction. Furthermore, a set of dedicated loss functions is designed to harmonize potential conflicts between the MIM and modal alignment objectives during optimization. Extensive evaluations on the MSED benchmark demonstrate the superiority of our approach, which establishes a new state-of-the-art performance with 1.1% F1-score improvement in desire understanding. Consistent gains in emotion and sentiment recognition further validate its generalization ability and the necessity of utilizing non-verbal cues. Our code is available at: https://github.com/especiallyW/SyDES.
Social media's openness fosters opinion exchange but complicates guiding users toward global consensus. Existing approaches are often invasive, modifying opinions or forcing cross-group interactions, undermining autonomy and triggering resistance. They also lack long-term planning, risking macro-level polarization despite local agreement. To address this, we propose H-NeiFi, a hierarchical, non-invasive opinion guidance framework. It models experts and non-experts separately and introduces a neighbor filtering mechanism that adaptively shapes communication without altering user opinions. Using multi-agent reinforcement learning with a long-term reward, H-NeiFi optimizes information flow while preserving interaction autonomy. Experiments show that it accelerates consensus by 22.0% to 30.7% and achieves global convergence even without experts, providing a natural and efficient paradigm for social network governance. Our code is available at: https://github.com/shijunguo44/H-NeiFi.