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Kaiwen Wei

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains

Kaiwen Wei
Rui Shan
Dongsheng Zou
Jianzhong Yang
Bi Zhao
Junnan Zhu
Jiang Zhong

Large reasoning models (LRMs) have shown significant progress in test-time scaling through chain-of-thought prompting. Current approaches like search-o1 integrate retrieval augmented generation (RAG) into multi-step reasoning processes but rely on a single, linear reasoning path while incorporating unstructured textual information in a flat, context-agnostic manner. As a result, these approaches can lead to error accumulation throughout the reasoning chain, which significantly limits its effectiveness in medical question-answering (QA) tasks where both accuracy and traceability are critical requirements. To address these challenges, we propose MIRAGE (Multi-path Inference with Retrieval-Augmented Graph Exploration), a novel test-time scalable reasoning framework that performs dynamic multi-path inference over structured medical knowledge graphs. Specifically, MIRAGE 1) decomposes complex queries into entity-grounded sub-questions, 2) executes parallel inference paths, 3) retrieves evidence adaptively via neighbor expansion and multi-hop traversal, and 4) integrates answers using cross-path verification to resolve contradictions. Experiments on three medical QA benchmarks (GenMedGPT-5k, CMCQA, and ExplainCPE) show that MIRAGE consistently outperforms GPT-4o, Tree-of-Thought variants, and other retrieval-augmented baselines in both automatic and human evaluations. Additionally, MIRAGE improves interpretability by generating explicit reasoning chains that trace each factual claim to concrete paths within the knowledge graph, making it especially suitable for complex medical reasoning scenarios.

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AAAI Conference 2024 Conference Paper

CAMEL: Capturing Metaphorical Alignment with Context Disentangling for Multimodal Emotion Recognition

Linhao Zhang
Li Jin
Guangluan Xu
Xiaoyu Li
Cai Xu
Kaiwen Wei
Nayu Liu
Haonan Liu

Understanding the emotional polarity of multimodal content with metaphorical characteristics, such as memes, poses a significant challenge in Multimodal Emotion Recognition (MER). Previous MER researches have overlooked the phenomenon of metaphorical alignment in multimedia content, which involves non-literal associations between concepts to convey implicit emotional tones. Metaphor-agnostic MER methods may be misinformed by the isolated unimodal emotions, which are distinct from the real emotions blended in multimodal metaphors. Moreover, contextual semantics can further affect the emotions associated with similar metaphors, leading to the challenge of maintaining contextual compatibility. To address the issue of metaphorical alignment in MER, we propose to leverage a conditional generative approach for capturing metaphorical analogies. Our approach formulates schematic prompts and corresponding references based on theoretical foundations, which allows the model to better grasp metaphorical nuances. In order to maintain contextual sensitivity, we incorporate a disentangled contrastive matching mechanism, which undergoes curricular adjustment to regulate its intensity during the learning process. The automatic and human evaluation experiments on two benchmarks prove that, our model provides considerable and stable improvements in recognizing multimodal emotion with metaphor attributes.

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ECAI Conference 2024 Conference Paper

Multi-View Prompt for Fine-Grained Multimodal Named Entity Recognition and Grounding

Jintao Liu
Chenglong Liu
Kaiwen Wei

Fine-Grained Multimodal Named Entity Recognition and Grounding (FMNERG) aims to extract entity name, fine-grained entity type, and its corresponding object from paired text and image. This task demands fundamental reasoning capability for complex language and multimodal comprehension. Despite encouraging results, existing methods face two critical issues: (1) Insufficient knowledge of the entity poses challenges to fine-grained entity recognition; (2) Limited correlations between entities and objects hinder the visual grounding of entities. To tackle these issues, we propose a Multi-View Prompt (MVP) method for the FMNERG task in this paper, which collaborates with Large Language Models (LLMs) and Visual Grounding Models (VGMs) for reasoning. Concretely, MVP constructs a knowledgeable prompt in a chain-of-thought format, progressively refining possible entity types from coarse-grained to fine-grained levels. It leverages a heuristic method to select demonstration examples, which could provide guiding knowledge about entities from LLMs. To establish correlations between entities and potential objects, MVP introduces a grounded prompt that exploits information from guiding knowledge and image caption, enabling VGMs to detect related objects. Experimental results indicate that MVP achieves state-of-the-art performance on the Twitter dataset.

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AAAI Conference 2024 Conference Paper

Multimodal Event Causality Reasoning with Scene Graph Enhanced Interaction Network

Jintao Liu
Kaiwen Wei
Chenglong Liu

Multimodal event causality reasoning aims to recognize the causal relations based on the given events and accompanying image pairs, requiring the model to have a comprehensive grasp of visual and textual information. However, existing studies fail to effectively model the relations of the objects within the image and capture the object interactions across the image pair, resulting in an insufficient understanding of visual information by the model. To address these issues, we propose a Scene Graph Enhanced Interaction Network (SEIN) in this paper, which can leverage the interactions of the generated scene graph for multimodal event causality reasoning. Specifically, the proposed method adopts a graph convolutional network to model the objects and their relations derived from the scene graph structure, empowering the model to exploit the rich structural and semantic information in the image adequately. To capture the object interactions between the two images, we design an optimal transport-based alignment strategy to match the objects across the images, which could help the model recognize changes in visual information and facilitate causality reasoning. In addition, we introduce a cross-modal fusion module to combine textual and visual features for causality prediction. Experimental results indicate that the proposed SEIN outperforms state-of-the-art methods on the Vis-Causal dataset.

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AAAI Conference 2024 Conference Paper

Video Event Extraction with Multi-View Interaction Knowledge Distillation

Kaiwen Wei
Runyan Du
Li Jin
Jian Liu
Jianhua Yin
Linhao Zhang
Jintao Liu
Nayu Liu

Video event extraction (VEE) aims to extract key events and generate the event arguments for their semantic roles from the video. Despite promising results have been achieved by existing methods, they still lack an elaborate learning strategy to adequately consider: (1) inter-object interaction, which reflects the relation between objects; (2) inter-modality interaction, which aligns the features from text and video modality. In this paper, we propose a Multi-view Interaction with knowledge Distillation (MID) framework to solve the above problems with the Knowledge Distillation (KD) mechanism. Specifically, we propose the self-Relational KD (self-RKD) to enhance the inter-object interaction, where the relation between objects is measured by distance metric, and the high-level relational knowledge from the deeper layer is taken as the guidance for boosting the shallow layer in the video encoder. Meanwhile, to improve the inter-modality interaction, the Layer-to-layer KD (LKD) is proposed, which integrates additional cross-modal supervisions (i.e., the results of cross-attention) with the textual supervising signal for training each transformer decoder layer. Extensive experiments show that without any additional parameters, MID achieves the state-of-the-art performance compared to other strong methods in VEE.

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