Author name cluster

Yuyu Luo

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.

10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Explainable Oracle Bone Script Recognition via Multimodal Pictographic Reasoning

Yin Wu
Zhengxuan Zhang
Jiayu Chen
Chang Xu
Yuyu Luo
Nan Tang
Hui Xiong

Oracle Bone Script, East Asia's earliest mature writing system from over 3,500 years ago, encodes ancient cognition through visual metaphors, yet remains largely undeciphered and inaccessible, severing modern society from its cultural roots. Traditional AI methods, while accurate in classification, treat glyphs as opaque data, neglecting their pictographic essence and failing to foster public understanding—exacerbating a heritage crisis amid linguistic evolution. We pioneer a paradigm shift toward AI-driven cultural democratization, introducing OracleVis, the first human-validated multimodal dataset of glyph-image-explanation triplets, curated through expert collaborations to overcome data scarcity, bias, and incompleteness in archaeological sources. Building on this, OBS-VM, an explainability-centric multimodal large language model fine-tuned on Qwen2-VL-7B, models pictographic reasoning by balancing semantic fidelity with interpretive transparency, transforming black-box predictions into cognition-aligned narratives. Rigorous evaluations, including benchmarks and a user study with 24 non-experts, reveal our system's superiority: it outperforms GPT-4o in pictographic rationality (3.79 vs. 3.58 in human evaluation) and achieves a 35.3% relative improvement in recognition accuracy, while interactive learning boosts knowledge gains (+5.5 vs. +1.7), interest (+1.9 vs. +0.4), and confidence (+2.0 vs. +0.3) over static methods. This work illuminates AI's potential to bridge ancient wisdom and contemporary audiences, redefining heritage preservation as an inclusive, socially impactful endeavor that turns cultural alienation into enlightened engagement.

PDF Details DOI

ICLR Conference 2025 Conference Paper

AFlow: Automating Agentic Workflow Generation

Jiayi Zhang 0017
Jinyu Xiang
Zhaoyang Yu 0004
Fengwei Teng
Xionghui Chen
Jiaqi Chen
Mingchen Zhuge
Xin Cheng

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. We introduce AFLOW, an automated framework that efficiently explores this space using Monte Carlo Tree Search, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. Empirical evaluations across six benchmark datasets demonstrate AFLOW's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines. Furthermore, AFLOW enables smaller models to outperform GPT-4o on specific tasks at 4.55% of its inference cost in dollars. The code is available at https://github.com/FoundationAgents/AFlow.

Details

ICML Conference 2025 Conference Paper

Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search

Boyan Li 0001
Jiayi Zhang 0017
Ju Fan
Yanwei Xu 0004
Chong Chen 0001
Nan Tang 0001
Yuyu Luo

Text-to-SQL, which enables natural language interaction with databases, serves as a pivotal method across diverse industries. With new, more powerful large language models (LLMs) emerging every few months, fine-tuning has become incredibly costly, labor-intensive, and error-prone. As an alternative, zero-shot Text-to-SQL, which leverages the growing knowledge and reasoning capabilities encoded in LLMs without task-specific fine-tuning, presents a promising and more challenging direction. To address this challenge, we propose Alpha-SQL, a novel approach that leverages a Monte Carlo Tree Search (MCTS) framework to iteratively infer SQL construction actions based on partial reasoning states. To enhance the framework’s reasoning capabilities, we introduce LLM-as-Action-Model to dynamically generate SQL construction actions during the MCTS process, steering the search toward more promising SQL queries. Moreover, Alpha-SQL employs a self-supervised reward function to evaluate the quality of candidate SQL queries, ensuring more accurate and efficient query generation. Experimental results show that Alpha-SQL achieves 69. 7% execution accuracy on the BIRD development set, using a 32B open-source LLM without fine-tuning. Alpha-SQL outperforms the best previous zero-shot approach based on GPT-4o by 2. 5% on the BIRD development set.

Details

NeurIPS Conference 2025 Conference Paper

Atom of Thoughts for Markov LLM Test-Time Scaling

Fengwei Teng
Quan Shi
Zhaoyang Yu
Jiayi Zhang
Yuyu Luo
Chenglin Wu
Zhijiang Guo

Large Language Models (LLMs) achieve superior performance through training-time scaling, and test-time scaling further enhances their capabilities by conducting effective reasoning during inference. However, as the scale of reasoning increases, existing test-time scaling methods suffer from accumulated historical information, which not only wastes computational resources but also interferes with effective reasoning. To address this issue, we observe that complex reasoning can be achieved by solving a series of independent and self-contained subquestions. These subquestions are essentially \textit{atomic questions}, exhibiting the memoryless property similar to Markov processes. Based on this observation, we propose Atom of Thoughts (\our), where each state transition consists of decomposing the current question into a dependency-based directed acyclic graph and contracting its subquestions, forming a simplified question that maintains answer equivalence with the original problem. This answer preservation enables the iterative \textit{decomposition-contraction} process to naturally form a meaningful Markov reasoning process. Furthermore, these atomic states can be seamlessly integrated into existing test-time scaling methods, enabling \our to serve as a plug-in enhancement for improving reasoning capabilities.

PDF Details

IJCAI Conference 2025 Conference Paper

Graph Neural Networks for Databases: A Survey

Ziming Li
Youhuan Li
Yuyu Luo
Guoliang Li
Chuxu Zhang

Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) community has increasingly recognized the potentiality of GNNs, prompting a surge of researches focusing on improving database systems through GNN-based approaches. However, despite notable advances, There is a lack of a comprehensive review and understanding of how GNNs could improve DB systems. Therefore, this survey aims to bridge this gap by providing a structured and in-depth overview of GNNs for DB systems. Specifically, we propose a new taxonomy that classifies existing methods into two key categories: (1) Relational Databases, which includes tasks like performance prediction, query optimization, and Text-to-SQL, and (2) Graph Databases, addressing challenges like efficient graph query processing and graph similarity computation. We systematically review key methods in each category, highlighting their contributions and practical implications. Finally, we suggest promising avenues for integrating GNNs into Database systems.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning

Tianqi Luo
Chuhan Huang
Leixian Shen
Boyan Li
Shuyu Shen
Wei Zeng
Nan Tang
Yuyu Luo

Text-to-Visualization (Text2VIS) enables users to create visualizations from natural language queries, making data insights more accessible. However, Text2VIS faces challenges in interpreting ambiguous queries, as users often express their visualization needs in imprecise language. To address this challenge, we introduce nBench 2. 0, a new benchmark designed to evaluate Text2VIS systems in scenarios involving ambiguous queries. nvBench 2. 0 includes 7, 878 natural language queries and 24, 076 corresponding visualizations, derived from 780 tables across 153 domains. It is built using a controlled ambiguity-injection pipeline that generates ambiguous queries through a reverse-generation workflow. By starting with unambiguous seed visualizations and selectively injecting ambiguities, the pipeline yields multiple valid interpretations for each query, with each ambiguous query traceable to its corresponding visualization through step-wise reasoning paths. We evaluate various Large Language Models (LLMs) on their ability to perform ambiguous Text2VIS tasks using nBench 2. 0. We also propose Step-Text2Vis, an LLM-based model trained on nvBench 2. 0, which enhances performance in ambiguous scenarios through step-wise preference optimization. Our results show that Step-Text2Vis outperforms all baselines, setting a new state-of-the-art for ambiguous Text2VIS tasks. Our source code and data are available at https: //nvbench2. github. io/

PDF Details

IJCAI Conference 2025 Conference Paper

RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition

Xudong Yang
Yizhang Zhu
Hanfeng Liu
Zeyi Wen
Nan Tang
Yuyu Luo

Conventional Multi-modal multi-label emotion recognition (MMER) assumes complete access to visual, textual, and acoustic modalities. However, real-world multi-party settings often violate this assumption, as non-speakers frequently lack acoustic and textual inputs, leading to a significant degradation in model performance. Existing approaches also tend to unify heterogeneous modalities into a single representation, overlooking each modality’s unique characteristics. To address these challenges, we propose RAMer (Reconstruction-based Adversarial Model for Emotion Recognition), which refines multi-modal representations by not only exploring modality commonality and specificity but crucially by leveraging reconstructed features, enhanced by contrastive learning, to overcome data incompleteness and enrich feature quality. RAMer also introduces a personality auxiliary task to complement missing modalities using modality-level attention, improving emotion reasoning. To further strengthen the model's ability to capture label and modality interdependency, we propose a stack shuffle strategy to enrich correlations between labels and modality-specific features. Experiments on three benchmarks, i. e. , MEmoR, CMU-MOSEI, and M³ED, demonstrate that RAMer achieves state-of-the-art performance in dyadic and multi-party MMER scenarios.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

Yanchen Zhu
Honghui Zou
Chufan Liu
Yuyu Luo
Yuankai Wu
Yuxuan Liang

efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability—achieving up to 244. 4% increase in coverage—and reduces user inconvenience with up to 79. 8% shorter waiting times, compared to existing solutions.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking

Changlun Li
Yao SHI
Chen Wang
Qiqi Duan
Runke RUAN
Weijie Huang
Haonan Long
Lijun Huang

Large Language Models (LLMs) have demonstrated notable capabilities across financial tasks, including financial report summarization, earnings call transcript analysis, and asset classification. However, their real-world effectiveness in managing complex fund investment remains inadequately assessed. A fundamental limitation of existing benchmarks for evaluating LLM-driven trading strategies is their reliance on historical back-testing, inadvertently enabling LLMs to "time travel"—leveraging future information embedded in their training corpora, thus resulting in possible information leakage and overly optimistic performance estimates. To address this issue, we introduce DeepFund, a live fund benchmark tool designed to rigorously evaluate LLM in real-time market conditions. Utilizing a multi-agent architecture, DeepFund connects directly with real-time stock market data—specifically data published after each model’s pretraining cutoff—to ensure fair and leakage-free evaluations. Empirical tests on nine flagship LLMs from leading global institutions across multiple investment dimensions—including ticker-level analysis, investment decision-making, portfolio management, and risk control—reveal significant practical challenges. Notably, even cutting-edge models such as DeepSeek-V3 and Claude-3. 7-Sonnet incur net trading losses within DeepFund real-time evaluation environment, underscoring the present limitations of LLMs for active fund management. Our code is available at https: //github. com/HKUSTDial/DeepFund.

PDF Details

NeurIPS Conference 2024 Conference Paper

Are Large Language Models Good Statisticians?

Yizhang Zhu
Shiyin Du
Boyan Li
Yuyu Luo
Nan Tang

Large Language Models (LLMs) have demonstrated impressive capabilities across a range of scientific tasks including mathematics, physics, and chemistry. Despite their successes, the effectiveness of LLMs in handling complex statistical tasks remains systematically under-explored. To bridge this gap, we introduce StatQA, a new benchmark designed for statistical analysis tasks. StatQA comprises 11, 623 examples tailored to evaluate LLMs' proficiency in specialized statistical tasks and their applicability assessment capabilities, particularly for hypothesis testing methods. We systematically experiment with representative LLMs using various prompting strategies and show that even state-of-the-art models such as GPT-4o achieve a best performance of only 64. 83%, indicating significant room for improvement. Notably, while open-source LLMs (e. g. LLaMA-3) show limited capability, those fine-tuned ones exhibit marked improvements, outperforming all in-context learning-based methods (e. g. GPT-4o). Moreover, our comparative human experiments highlight a striking contrast in error types between LLMs and humans: LLMs primarily make applicability errors, whereas humans mostly make statistical task confusion errors. This divergence highlights distinct areas of proficiency and deficiency, suggesting that combining LLM and human expertise could lead to complementary strengths, inviting further investigation into their collaborative potential. Our source code and data are available at https: //statqa. github. io/.

PDF Details DOI