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Ru Li

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10 papers

1 author row

EAAI Journal 2026 Journal Article

Blockchain storage optimization mechanism using hyper-heuristic algorithm based on reinforcement learning in the Internet of Vehicles

Xiaodong Zhang
Ru Li
Leixiao Li
Gang Wang
Jianxiong Wan
Pengfei Yue

Blockchain technology in the Internet of Vehicles (IoV) can effectively address security challenges. However, due to the high dynamics and resource constraints of the IoV, large-scale deployment of blockchain applications remains challenging. Although many methods for storage optimization have been proposed, none have comprehensively considered the high dynamic nature of the IoV and the impact of transaction pruning on the system. Therefore, this paper innovatively introduces a Directed Acyclic Graph (DAG) lattice structure and proposes an effective blockchain storage optimization mechanism. This paper presents a pruning utility evaluation model that takes three factors into account, namely the number of transaction copies, transaction generation time, and transaction size. Given the highly dynamic nature of the IoV, this paper innovatively employs a hyper-heuristic algorithm with reinforcement learning (HHRL) to optimize the system’s comprehensive utility, solving the problem of not being able to customize optimization algorithms to determine the optimal pruning transaction strategy in dynamic IoV environments. Experimental results demonstrate that the proposed blockchain storage optimization mechanism based on HHRL outperforms other optimization methods in terms of the average target deviation, average execution time, target variance, and success rate. The experiments show that the proposed mechanism can effectively reduce the impact of pruning transactions on blockchain compared to other approaches and better support the implementation of blockchain applications in the IoV and real-world asset tokenization scenarios.

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AAAI Conference 2026 Conference Paper

Learning to Generate and Extract: A Multi-Agent Collaboration Framework for Zero-Shot Document-Level Event Arguments Extraction

Guangjun Zhang
Hu Zhang
Yazhou Han
Yue Fan
Yuhang Shao
Hongye Tan
Ru Li

Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents. In the zero-shot setting, existing methods employ LLMs to generate synthetic data to address the challenge posed by the scarcity of annotated data. However, relying solely on Event-type-only prompts makes it difficult for the generated content to accurately capture the contextual and structural relationships of unseen events. Moreover, ensuring the reliability and usability of synthetic data remains a significant challenge due to the absence of quality evaluation mechanisms. To this end, we introduce a multi-agent collaboration framework for zero-shot document-level event argument extraction (ZS-DEAE), which simulates the human collaborative cognitive process of “Propose–Evaluate–Revise.” Specifically, the framework comprises a generation agent and an evaluation agent. The generation agent synthesizes data for unseen events by leveraging knowledge from seen events, while the evaluation agent extracts arguments from the synthetic data and assesses their semantic consistency with the context. The evaluation results are subsequently converted into reward signals, with event structure constraints incorporated into the reward design to enable iterative optimization of both agents via reinforcement learning. In three zero-shot scenarios constructed from the RAMS and WikiEvents datasets, our method achieves improvements both in data generation quality and argument extraction performance, while the generated data also effectively enhances the zero-shot performance of other DEAE models.

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AAAI Conference 2026 Conference Paper

Uncovering and Mitigating Transient Blindness in Multimodal Model Editing

XiaoQi Han
Ru Li
Ran Yi
Hongye Tan
Zhuomin Liang
Victor Gutierrez Basulto
Jeff Z. Pan

Multimodal Model Editing (MMED) aims to correct erroneous knowledge in multimodal models. Existing evaluation methods, adapted from textual model editing, overstate success by relying on low-similarity or random inputs, obscure overfitting. We propose a comprehensive locality evaluation framework, covering three key dimensions: random-image locality, no-image locality, and consistent-image locality, operationalized through seven distinct data types, enabling a detailed and structured analysis of multimodal edits. We introduce De-VQA, a dynamic evaluation for visual question answering, uncovering a phenomenon we term transient blindness, overfitting to edit-similar text while ignoring visuals. Token analysis shows edits disproportionately affect textual tokens. We propose locality-aware adversarial losses to balance cross-modal representations. Empirical results demonstrate that our approach consistently outperforms existing baselines, reducing transient blindness and improving locality by 17% on average.

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AAAI Conference 2025 Conference Paper

Diff-Shadow: Global-guided Diffusion Model for Shadow Removal

Jinting Luo
Ru Li
Chengzhi Jiang
Xiaoming Zhang
Mingyan Han
Ting Jiang
Haoqiang Fan
Shuaicheng Liu

We propose Diff-Shadow, a global-guided diffusion model for high-quality shadow removal. Previous transformer-based approaches can utilize global information to relate shadow and non-shadow regions but are limited in their synthesis ability and recover images with obvious boundaries. In contrast, diffusion-based methods can generate better content but they are not exempt from issues related to inconsistent illumination. In this work, we combine the advantages of diffusion models and global guidance to realize shadow-free restoration. Specifically, we propose a parallel UNets architecture: 1) the local branch performs the patch-based noise estimation in the diffusion process, and 2) the global branch recovers the low-resolution shadow-free images. A Reweight Cross Attention (RCA) module is designed to integrate global contextual information of non-shadow regions into the local branch. We further design a Global-guided Sampling Strategy (GSS) that mitigates patch boundary issues and ensures consistent illumination across shaded and unshaded regions in the recovered image. Comprehensive experiments on three publicly standard datasets ISTD, ISTD+, and SRD have demonstrated the effectiveness of Diff-Shadow. Compared to state-of-the-art methods, our method achieves a significant improvement in terms of PSNR, increasing from 32.33dB to 33.69dB on the ISTD dataset.

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AAAI Conference 2025 Conference Paper

Multi-view Consistent 3D Panoptic Scene Understanding

Xianzhu Liu
Xin Sun
Haozhe Xie
Zonglin Li
Ru Li
Shengping Zhang

3D panoptic scene understanding seeks to create novel view images with 3D-consistent panoptic segmentation, which is crucial for many vision and robotics applications. Mainstream methods (e.g., Panoptic Lifting) directly use machine-generated 2D panoptic segmentation masks as training labels. However, these generated masks often exhibit multi-view inconsistencies, leading to ambiguities during the optimization process. To address this, we present Multi-view Consistent 3D Panoptic Scene Understanding (MVC-PSU), featuring two key components: 1) Probabilistic Semantic Aligner, which associates semantic information of corresponding pixels across multiple views by probabilistic alignment to ensure that predicted panoptic segmentation masks are consistent across different views. 2) Geometric Consistency Enforcer, which uses multi-view projection and monocular depth consistency to ensure that the geometry of the reconstructed scene is accurate and consistent across different views. Experimental results demonstrate that the proposed MVC-PSU surpasses state-of-the-art methods on the ScanNet, Replica, and HyperSim datasets.

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AAAI Conference 2025 Conference Paper

Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints

Qinglin Liu
Zonglin Li
Xiaoqian Lv
Xin Sun
Ru Li
Shengping Zhang

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitations, we introduce Path-Adaptive Matting (PAM), a framework that dynamically adjusts network paths based on image contexts and computational cost constraints. We formulate the training of the computational cost-constrained matting network as a bilevel optimization problem, jointly optimizing the matting network and the path estimator. Building on this formalization, we design a path-adaptive matting architecture by incorporating path selection layers and learnable connect layers to estimate optimal paths and perform efficient inference within a unified network. Furthermore, we propose a performance-aware path-learning strategy to generate path labels online by evaluating a few paths sampled from the prior distribution of optimal paths and network estimations, enabling robust and efficient online path learning. Experiments on five image matting datasets demonstrate that the proposed PAM framework achieves competitive performance across a range of computational cost constraints.

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YNIMG Journal 2025 Journal Article

The sensory-motor overlap in motor imitation: evidence from motor imagery

Xianyang Wang
Hongliang Lu
Lingwei Zeng
Kewei Sun
Shuyi Liang
Ru Li
Congchong Li
Chaoxian Wang

Motor imitation is crucial for acquiring motor skills and social cognition, yet the theoretical understanding of its underlying mechanism remains partial. The direct matching hypothesis suggests that the overlap between observed and executed motions is crucial for effective motor imitation. This study aimed to investigate the behavioral benefits of motor imitation, and to validate its theoretical basis by examining its brain activation pattern during motor imagery. We recruited 56 college students and compared their motor proficiency and imagery quality after different learning strategies. Participants were randomly assigned to observation, observation with disturbance, imitation, or control groups. The observation with disturbance group was designed as a sensory-motor conflict condition to contrast with imitation where the sensory and motor representations aligned. The results showed that both motor imitation and observation enhanced motor proficiency with only imitation outperforming control group, and only imitation improved imagery quality. The functional near-infrared spectroscopy (fNIRS) analysis revealed a significant increase in the primary somatosensory cortex activation following imitation, and a contrasting change pattern following observation with disturbance. The fNIRS results highlighted the necessity of sensory-motor overlap for effective motor imitation. These findings confirmed that motor imitation was an optimal strategy for motor learning, with the primary somatosensory cortex as the key neural substrate, and validated the direct matching hypothesis.

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AAAI Conference 2024 Conference Paper

Knowledge-Aware Neuron Interpretation for Scene Classification

Yong Guan
Freddy Lécué
Jiaoyan Chen
Ru Li
Jeff Z. Pan

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and more attentions. However, current methods rarely incorporate external knowledge and still suffer from three limitations: (1) Neglecting concept completeness. Merely selecting concepts may not sufficient for prediction. (2) Lacking concept fusion. Failure to merge semantically-equivalent concepts. (3) Difficult in manipulating model behavior. Lack of verification for explanation on original model. To address these issues, we propose a novel knowledge-aware neuron interpretation framework to explain model predictions for image scene classification. Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts. Our method, incorporating complete concepts, effectively provides better prediction explanations compared to baselines. Furthermore, for concept fusion, we introduce a knowledge graph-based method known as Concept Filtering, which produces over 23% point gain on neuron behaviors for neuron interpretation. At last, we propose Model Manipulation, which aims to study whether the core concepts based on ConceptNet could be employed to manipulate model behavior. The results show that core concepts can effectively improve the performance of original model by over 26%.

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AAAI Conference 2024 Conference Paper

SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field

Ru Li
Jia Liu
Guanghui Liu
Shengping Zhang
Bing Zeng
Shuaicheng Liu

In this paper, we propose SpectralNeRF, an end-to-end Neural Radiance Field (NeRF)-based architecture for high-quality physically based rendering from a novel spectral perspective. We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output. Our SpectralNeRF follows these two steps through the proposed multi-layer perceptron (MLP)-based architecture (SpectralMLP) and Spectrum Attention UNet (SAUNet). Given the ray origin and the ray direction, the SpectralMLP constructs the spectral radiance field to obtain spectrum maps of novel views, which are then sent to the SAUNet to produce RGB images of white-light illumination. Applying NeRF to build up the spectral rendering is a more physically-based way from the perspective of ray-tracing. Further, the spectral radiance fields decompose difficult scenes and improve the performance of NeRF-based methods. Comprehensive experimental results demonstrate the proposed SpectralNeRF is superior to recent NeRF-based methods when synthesizing new views on synthetic and real datasets. The codes and datasets are available at https://github.com/liru0126/SpectralNeRF.

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IJCAI Conference 2022 Conference Paper

Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Zhiwei Hu
Victor Gutierrez Basulto
Zhiliang Xiang
Xiaoli Li
Ru Li
Jeff Z. Pan

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel type-aware model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP’s effectiveness.

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