Author name cluster

Minghao Hu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

13 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeepWriter: A Multi-Agent Collaboration Framework for Information-rich Ultra-long Book Writing

Ming Wang
Minghao Hu
Xiuli Kang
Li He
Yu Tian
Chunming Liu
Han Shi
Zhunchen Luo

Long-form books are among the most information-rich and structurally complex forms of written content, often exceeding 100,000 words. While recent methods have enabled basic long-text generation, they remain limited in two key aspects: the inability to generate ultra-long content at book scale, and the lack of mechanisms for integrating rich factual information. To address these limitations, we propose DeepWriter, a multi-agent collaborative framework that follows a structured planning-then-generation paradigm. It first constructs a detailed book outline with narrative arcs and chapter semantics, then incrementally generates content conditioned on retrieved knowledge and contextual signals. DeepWriter supports controllable generation of full-length books exceeding 100,000 words, enriched with citations, trivia and images. To support evaluation beyond surface-level fluency, we introduce DeepWriter-Bench, a bilingual benchmark of 18 annotated books designed to assess book-scale coherence, richness, and factual grounding. Additionally, we propose BookScore, a unified 100-point metric for quantifying book maturity. Experimental results show that DeepWriter achieves a state-of-the-art BookScore of 80.92, consistently outperforming strong baselines.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Global-Local Confidence Fusion for Hallucination Detection in Mathematical Reasoning Task

Bo Zhang
Cong Gao
Linkang Yang
Bingxu Han
Minghao Hu
Zhunchen Luo
Guotong Geng
Xiaoying Bai

Large Reasoning Models (LRMs) achieve promising results on complex reasoning tasks but remain susceptible to hallucinations. Existing hallucination detection methods based on Large Language Models (LLMs) often focus solely on final answers, overlooking inconsistencies between the answer and reasoning process. This limitation reduces their ability to detect hallucinations during inference. Moreover, training-free approaches lack mechanisms for confidence estimation, resulting in an unquantified detection output. In contrast, training-based methods can provide fine-grained assessments but often neglect the self-correction capability of LRMs, where earlier errors may be corrected in subsequent steps, leading to inaccurate hallucination detection. To address these challenges, we propose ConfFuse, a unified framework that fuses global and local confidence scores for hallucination detection. A Global Hallucination Detection Model (GHDM) is trained using Direct Preference Optimization (DPO) to assess hallucinations at the level of entire reasoning chains, yielding global confidence estimates. Simultaneously, a Process Reward Model (PRM) estimates step-wise confidence scores to capture local logical flaws. A weighted fusion strategy combines the global confidence score with the minimum local score to jointly reflect overall reasoning consistency and local soundness. Experimental evaluations demonstrate that ConfFuse surpasses Qwen3-1.7B and Qwen3-8B by up to 11.86% and 5.46% in F1 score on in-distribution datasets, and achieves average improvements of 4.65% and 2.80% on out-of-distribution datasets. These results verify the effectiveness and generalizability of the proposed framework.

PDF Details DOI

AAAI Conference 2026 Conference Paper

WikiMAG: A Multi-Agent Guided Framework for Generating Structured Wikipedia-like Articles

Xiuli Kang
Yinlong Xiao
Minghao Hu
Yuan Huang
Bin Mao
Ming Wang
Fang Wang
Zhunchen Luo

Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table. To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions. Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Hippocampal-like Sequential Editing for Continual Knowledge Updates in Large Language Models

Quntian Fang
Zhen Huang
Zhiliang Tian
Minghao Hu
Dongsheng Li
Yiping Yao
Xinyue Fang
Menglong Lu

Large language models (LLMs) are now pivotal in real-world applications. Model editing has emerged as a promising paradigm for efficiently modifying LLMs without full retraining. However, current editing approaches face significant limitations due to parameter drift, which stems from inconsistencies between newly edited knowledge and the model's existing knowledge. In sequential editing scenarios, cumulative drifts progressively lead to model collapse characterized by general capability degradation and balance between acquiring new knowledge and catastrophic forgetting of existing knowledge. Drawing inspiration from the hippocampal trisynaptic circuit for continual memorizing and forgetting, we propose a Hippocampal-like Sequential Editing (HSE) framework that designs the unlearning of obsolete knowledge, domain-specific knowledge update separation and replay for edited knowledge. Specifically, the HSE framework designs three core mechanisms: (1) Machine unlearning selectively erases outdated knowledge to facilitate integration of new information, (2) Fisher Information Matrix-guided parameter updates prevents cross-domain knowledge interference, and (3) Parameter replay consolidates long-term editing memory through lightweight and global replay of editing data in a parametric form. Theoretical analysis demonstrates that HSE achieves smaller generalization error bounds, more stable convergence and higher computational efficiency. Experimental results validate its effective balance between acquiring new knowledge and mitigating catastrophic forgetting, maintaining or even slightly enhancing general capabilities. In practical applications, experiments confirm its effectiveness in multi-domain hallucination mitigation, healthcare knowledge injecting, and societal bias reduction.

PDF Details

AAAI Conference 2025 Conference Paper

Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction

Peixin Huang
Xiang Zhao
Minghao Hu
Zhen Tan
Weidong Xiao

To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods for such problems have commonly built document-level event graphs for global reasoning across sentences. However, the edges between events are usually derived from external tools heuristically, which are not always reliable and may introduce noise. Moreover, they are not capable of preserving logical constraints among event relations, e.g., coreference constraint, symmetry constraint and conjunction constraint. These constraints guarantee coherence between different relation types, enabling the generation of a unified event evolution graph. In this work, we propose a novel method named LogicERE, which performs high-order event relation reasoning through modeling logic constraints. Specifically, different from conventional event graphs, we design a logic constraint induced graph (LCG) without any external tools. LCG involves event nodes where the interactions among them can model the coreference constraint, and event pairs nodes where the interactions among them can retain the symmetry constraint and conjunction constraint. Then we perform high-order reasoning on LCG with relational graph transformer to obtain enhanced event and event pair embeddings. Finally, we further incorporate logic constraint information via a joint logic learning module. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets.

PDF Details DOI

AAAI Conference 2025 Conference Paper

M^3EL: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking

Fang Wang
Shenglin Yin
Xiaoying Bai
Minghao Hu
Tianwei Yan
Yi Liang

Multi-modal Entity Linking (MEL) is a fundamental component for various downstream tasks. However, existing MEL datasets suffer from small scale, scarcity of topic types and limited coverage of tasks, making them incapable of effectively enhancing the entity linking capabilities of multi-modal models. To address these obstacles, we propose a dataset construction pipeline and publish M^3EL, a large-scale dataset for MEL. M^3EL includes 79,625 instances, covering 9 diverse multi-modal tasks, and 5 different topics. In addition, to further improve the model's adaptability to multi-modal tasks, We propose a modality-augmented training strategy. Utilizing M^3EL as a corpus, train the CLIP_ND model based on CLIP (ViT-B-32), and conduct a comparative analysis with an existing multi-modal baselines. Experimental results show that the existing models perform far below expectations (ACC of 49.4%-75.8%), After analysis, it was obtained that small dataset sizes, insufficient modality task coverage, and limited topic diversity resulted in poor generalization of multi-modal models. Our dataset effectively addresses these issues, and the CLIP_ND model fine-tuned with M^3EL shows a significant improvement in accuracy, with an average improvement of 9.3% to 25% across various tasks. Our dataset publicly available to facilitate future research.

PDF Details DOI

IJCAI Conference 2023 Conference Paper

A Canonicalization-Enhanced Known Fact-Aware Framework For Open Knowledge Graph Link Prediction

Yilin Wang
Minghao Hu
Zhen Huang
Dongsheng Li
Wei Luo
Dong Yang
Xicheng Lu

Open knowledge graph (OpenKG) link prediction aims to predict missing factual triples in the form of (head noun phrase, relation phrase, tail noun phrase). Since triples are not canonicalized, previous methods either focus on canonicalizing noun phrases (NPs) to reduce graph sparsity, or utilize textual forms to improve type compatibility. However, they neglect to canonicalize relation phrases (RPs) and triples, making OpenKG maintain high sparsity and impeding the performance. To address the above issues, we propose a Canonicalization-Enhanced Known Fact-Aware (CEKFA) framework that boosts link prediction performance through sparsity reduction of RPs and triples. First, we propose a similarity-driven RP canonicalization method to reduce RPs' sparsity by sharing knowledge of semantically similar ones. Second, to reduce the sparsity of triples, a known fact-aware triple canonicalization method is designed to retrieve relevant known facts from training data. Finally, these two types of canonical information are integrated into a general two-stage re-ranking framework that can be applied to most existing knowledge graph embedding methods. Experiment results on two OpenKG datasets, ReVerb20K and ReVerb45K, show that our approach achieves state-of-the-art results. Extensive experimental analyses illustrate the effectiveness and generalization ability of the proposed framework.

PDF Details DOI

AAAI Conference 2023 Conference Paper

MCL: Multi-Granularity Contrastive Learning Framework for Chinese NER

Shan Zhao
Chengyu Wang
Minghao Hu
Tianwei Yan
Meng Wang

Recently, researchers have applied the word-character lattice framework to integrated word information, which has become very popular for Chinese named entity recognition (NER). However, prior approaches fuse word information by different variants of encoders such as Lattice LSTM or Flat-Lattice Transformer, but are still not data-efficient indeed to fully grasp the depth interaction of cross-granularity and important word information from the lexicon. In this paper, we go beyond the typical lattice structure and propose a novel Multi-Granularity Contrastive Learning framework (MCL), that aims to optimize the inter-granularity distribution distance and emphasize the critical matched words in the lexicon. By carefully combining cross-granularity contrastive learning and bi-granularity contrastive learning, the network can explicitly leverage lexicon information on the initial lattice structure, and further provide more dense interactions of across-granularity, thus significantly improving model performance. Experiments on four Chinese NER datasets show that MCL obtains state-of-the-art results while considering model efficiency. The source code of the proposed method is publicly available at https://github.com/zs50910/MCL

PDF Details DOI

IROS Conference 2022 Conference Paper

Development of a 6 DOF Soft Robotic Manipulator with Integrated Sensing Skin

Shen Treratanakulchai
Enrico Franco
Arnau Garriga-Casanovas
Minghao Hu
Panagiotis Kassanos
Ferdinando Rodriguez y Baena

This paper presents a new 6 DOF soft robotic manipulator intended for colorectal surgery. The manipulator, based on a novel design that employs an inextensible tube to limit axial extension, is shown to maximize the force exerted at its tip and the bending angle, the latter being measured with a soft sensing skin. Manufacturing of the prototype is achieved with a lost-wax silicone-casting technique. The kinematic model of the manipulator, its workspace, and its manipulability are discussed. The prototype is evaluated with extensive experiments, including pressure-deflection measurement with and without tip load, and lateral force measurements with and without the soft sensing skin to assess hysteresis. The experimental results indicate that the prototype fulfils the key design requirements for colorectal surgery: (i) it can generate sufficient force to perform a range of laparoscopic tasks; (ii) the workspace is commensurate with the dimensions of the large intestine; (iii) the soft sensing skin only results in a marginal reduction of the maximum tip rotation within the range of pressures and external loads relevant for the chosen application.

Details

AAAI Conference 2021 Conference Paper

Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER

Shan Zhao
Minghao Hu
Zhiping Cai
Haiwen Chen
Fang Liu

Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encoding to integrate word-character lattice information, but fail to capture fine-grained correlations in word-character spaces. In this paper, we propose DC- SAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. By carefully combining cross-lattice and self-lattice attention modules with gated word-character semantic fusion unit, the network can explicitly capture fine-grained correlations across different spaces (e. g. , word-to-character and character-to-character), thus significantly improving model performance. Experiments on four Chinese NER datasets show that DCSAN obtains stateof-the-art results as well as efficiency compared to several competitive approaches.

PDF Details

IJCAI Conference 2020 Conference Paper

Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction

Shan Zhao
Minghao Hu
Zhiping Cai
Fang Liu

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g. , token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90. 62% F1 on entity recognition and 72. 97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by more than 1. 9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

PDF Details DOI

AAAI Conference 2019 Conference Paper

Read + Verify: Machine Reading Comprehension with Unanswerable Questions

Minghao Hu
Furu Wei
Yuxing Peng
Zhen Huang
Nan Yang
Dongsheng Li

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional “no-answer” probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce noanswer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as noanswer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2. 0 dataset show that our system obtains a score of 74. 2 F1 on test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).

PDF Details

IJCAI Conference 2018 Conference Paper

Reinforced Mnemonic Reader for Machine Reading Comprehension

Minghao Hu
Yuxing Peng
Zhen Huang
Xipeng Qiu
Furu Wei
Ming Zhou

In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects. First, a reattention mechanism is proposed to refine current attentions by directly accessing to past attentions that are temporally memorized in a multi-round alignment architecture, so as to avoid the problems of attention redundancy and attention deficiency. Second, a new optimization approach, called dynamic-critical reinforcement learning, is introduced to extend the standard supervised method. It always encourages to predict a more acceptable answer so as to address the convergence suppression problem occurred in traditional reinforcement learning algorithms. Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets.

PDF Details