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Xiangyang Li

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11 papers

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AAAI Conference 2026 Conference Paper

MCPTox: A Benchmark for Tool Poisoning on Real-World MCP Servers

Zhiqiang Wang
Yichao Gao
Yanting Wang
Suyuan Liu
Haifeng Sun
Haoran Cheng
Guanquan Shi
Haohua Du

By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a cornerstone of the modern autonomous agent ecosystem. However, it creates novel attack surfaces due to untrusted external tools. While prior work has focused on attacks injected through external tool outputs, we investigate a more fundamental vulnerability: Tool Poisoning, where malicious instructions are embedded within a tool's metadata at the registration stage. To date, this threat has been primarily demonstrated through isolated cases, lacking a systematic, large-scale evaluation. We introduce MCPTox, the first benchmark to systematically evaluate agent robustness against Tool Poisoning in realistic MCP settings. MCPTox is constructed upon 45 live, real-world MCP servers and 353 authentic tools. To achieve this, we design three distinct attack templates to generate a comprehensive suite of 1348 malicious test cases by few-shot learning, covering 10 categories of potential risks. Our evaluation on 20 prominent LLM agents setting reveals a widespread vulnerability to Tool Poisoning, with GPT-o1-mini, achieving an attack success rate of 72.8%. We find that more capable models are often more susceptible, as the attack exploits their superior instruction-following abilities. Finally, the failure case analysis reveals that agents rarely refuse these attacks, with the highest refused rate (Claude-3.7-Sonnet) less than 3%, demonstrating that existing safety alignment is ineffective against malicious actions that use legitimate tools for unauthorized operation. Our findings create a crucial empirical baseline for understanding and mitigating this widespread threat, and we release MCPTox for the development of verifiably safer AI agents.

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AAAI Conference 2026 Conference Paper

Scalable Mixed-Integer Optimization with Neural Constraints via Dual Decomposition

Shuli Zeng
Sijia Zhang
Feng Wu
Shaojie Tang
Xiangyang Li

Embedding deep neural networks (NNs) into mixed-integer programs (MIPs) is attractive for decision making with learned constraints, yet state-of-the-art monolithic linearisations blow up in size and quickly become intractable. In this paper, we introduce a novel dual-decomposition framework that relaxes the single coupling equality u=x with an augmented Lagrange multiplier and splits the problem into a vanilla MIP and a constrained NN block. Each part is tackled by the solver that suits it best-branch and cut for the MIP subproblem, first-order optimisation for the NN subproblem, so the model remains modular, the number of integer variables never grows with network depth, and the per-iteration cost scales only linearly with the NN size. On the public SurrogateLIB benchmark, our method proves scalable, modular, and adaptable: it runs 120x faster than an exact Big-M formulation on the largest test case; the NN sub-solver can be swapped from a log-barrier interior step to a projected-gradient routine with no code changes; and swapping the MLP for an LSTM backbone still completes the full optimisation in 47s without any bespoke adaptation.

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EAAI Journal 2025 Journal Article

Enhanced understanding of bond strength of fiber-reinforcement-polymer bars reinforced concrete subjected to elevated temperature for interfacial failure prevention: Explainable artificial intelligence method

Jianwen Hao
Shichao Zhu
Jianliang Zhang
Jiancheng Yao
Xiaoning Zhang
Xiangyang Li

Fiber-reinforcement-polymer (FRP) bars, as alternatives to traditional steel bars, effectively enhance the durability of concrete structures in extreme environments. However, the interfacial mechanical performance of FRP bars reinforced concrete degrades under elevated temperatures or fire, leading to bonded interface failure. The bond strength between FRP bars and concrete is critical to structural performance, but existing studies lack accurate prediction of its evolution under multi-factor coupling and clear mechanistic explanations. In order to accurately characterize the interfacial bond performance of FRP-reinforced concrete under elevated temperatures, three machine learning models, namely Support Vector Machine (SVM), Random Forest (RF) and eXterme Gradient Boosting (XGBoost), are established to predict bond strength using a database of 289 experimental results. These models are selected for their fitting capabilities matching the data's complexity (nonlinear coupling of multiple features): SVM handles moderate nonlinearity, RF adapts to mid-to-high complexity with anti-overfitting, and XGBoost captures intricate interactions for high complexity. Comparing them identifies the best-fitted model for bond strength prediction. In the established models based on the machine learning algorithms, the significant features are selected such as experimental temperature (T), diameter of FRP bar (d), anchorage length (l a ), concrete compressive strength (f c ), and concrete cover thickness to diameter of FRP bar ratio (c/d) considering the credibility of black-box models and the rationality of input features. In addition, the SHapley Additive exPlanations (SHAP) method is employed to enhance interpretability and overcome the “black box” problem of the machine learning (ML) based prediction models. Beyond explaining model behavior, its application here provides actionable insights for fire-resistant design: it quantifies feature contributions and interactive effects, addressing the lack of mechanistic guidance in previous artificial intelligence (AI) based studies. This paper provides meaningful guidelines for the interfacial failure prevention and safe service of FRP bars reinforced concrete structures in the elevated temperature environment.

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NeurIPS Conference 2025 Conference Paper

MoonCast: High-Quality Zero-Shot Podcast Generation

Zeqian Ju
Dongchao Yang
Shen Kai
Yichong Leng
Zhengtao Wang
Songxiang Liu
Xinyu Zhou
Tao Qin

Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face challenges when extending their capabilities to long, multi-speaker, and spontaneous dialogues, typical of real-world scenarios such as podcasts. These limitations arise from two primary challenges: 1) long speech: podcasts typically span several minutes, exceeding the upper limit of most existing work; 2) spontaneity: podcasts are marked by their spontaneous, oral nature, which sharply contrasts with formal, written contexts; existing works often fall short in capturing this spontaneity. In this paper, we propose MoonCast, a solution for high-quality zero-shot podcast generation, aiming to synthesize spontaneous podcast-style speech from text-only sources (e. g. , stories, technical reports, news in TXT, PDF, or Web URL formats) using the voices of unseen speakers. To enable long audio generation, we employ a language model with parameter, data, and context scaling to process sequences in an innovative format designed for modeling entire multi-speaker, multi-turn speech interactions. To enhance spontaneity, we observe that ASR transcripts capture spontaneous speech details (e. g. , filler words indicating hesitations, and specific punctuation and spaces reflecting breathing pauses), suggesting that these transcripts can serve as a partial indicator of speech spontaneity. Building upon this assumption, we utilize a script generation module to generate scripts incorporating these spontaneous elements. Experiments show MoonCast outperforms baselines, with notable improvements in contextual coherence and spontaneity.

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NeurIPS Conference 2025 Conference Paper

MTRec: Learning to Align with User Preferences via Mental Reward Models

Mengchen Zhao
Yifan Gao
Yaqing Hou
Xiangyang Li
Pengjie Gu
Zhenhua Dong
Ruiming Tang
Yi Cai

Recommendation models are predominantly trained using implicit user feedback, since explicit feedback is often costly to obtain. However, implicit feedback, such as clicks, does not always reflect users' real preferences. For example, a user might click on a news article because of its attractive headline, but end up feeling uncomfortable after reading the content. In the absence of explicit feedback, such erroneous implicit signals may severely mislead recommender systems. In this paper, we propose MTRec, a novel sequential recommendation framework designed to align with real user preferences by uncovering their internal satisfaction on recommended items. Specifically, we introduce a mental reward model to quantify user satisfaction and propose a distributional inverse reinforcement learning approach to learn it. The learned mental reward model is then used to guide recommendation models to better align with users’ real preferences. Our experiments show that MTRec brings significant improvements to a variety of recommendation models. We also deploy MTRec on an industrial short video platform and observe a 7\% increase in average user viewing time.

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NeurIPS Conference 2025 Conference Paper

MURKA: Multi-Reward Reinforcement Learning with Knowledge Alignment for Optimization Tasks

WANTONG XIE
Yi-Xiang Hu
Jieyang Xu
Feng Wu
Xiangyang Li

Optimization plays a central role in Operations Research (OR) and numerous industrial applications, yet automating the end-to-end process of translating natural language descriptions into executable optimization programs remains a formidable challenge. While recent efforts have applied Large Language Models (LLMs) to this task, existing approaches are hindered by high inference costs, limited robustness across domains, and weak verification mechanisms. In this work, we propose MURKA, a reinforcement learning and knowledge distillation-based framework that enhances LLM-driven optimization modeling via collaborative agent alignment. MURKA orchestrates three specialized agents---Extractor, Solver, and Checker---to achieve accurate problem understanding, robust formulation, and verifiable execution. The Extractor is trained using group relative policy optimization with a composite reward function that incorporates semantic correctness and execution fidelity. The Solver benefits from knowledge distillation from a powerful teacher model, yielding structurally valid and executable formulations in AMPL. The Checker iteratively verifies solution correctness via solver feedback. We validate MURKA's generalizability through extensive experiments across diverse OR benchmarks, demonstrating its robustness and scalability. Experimental results on eight diverse OR benchmarks, including NLP4LP, ComplexOR, and NL4Opt, demonstrate that MURKA, built on the LLaMa3-8B backbone, achieves a 5. 9\% absolute improvement in solution accuracy and a 5. 1\% increase in execution success rate compared to leading baselines. These results establish MURKA as an effective and scalable paradigm for LLM-driven optimization, with strong potential for deployment in real-world OR applications.

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NeurIPS Conference 2025 Conference Paper

Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning

Wenlin Zhang
Xiangyang Li
Kuicai Dong
Yichao Wang
Pengyue Jia
Xiaopeng Li
Yingyi Zhang
Derong Xu

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge, yet traditional RAG systems struggle with static workflows and limited adaptability for complex, multistep reasoning tasks. Agentic RAG systems, such as DeepResearch, address these issues through dynamic retrieval, iterative context refinement, and adaptive workflows. However, recent methods like Search-R1, which rely on outcome-based reinforcement learning, face challenges such as low exploration efficiency, gradient conflict, and sparse reward signals. To tackle these limitations, we introduce ReasonRAG, a novel method that leverages RAG-ProGUIDE—a high-quality dataset providing fine-grained, process-level rewards for query generation, evidence extraction, and answer generation. By employing process-supervised reinforcement learning, ReasonRAG enhances LLMs’ autonomous capabilities in search, query generation, evidence extraction, and answer synthesis. Experimental results show that ReasonRAG, utilizing RAG-ProGUIDE, outperforms existing approaches like Search-R1 and traditional RAG systems, achieving superior performance on five benchmark datasets with only 5k training instances—significantly fewer than the 90k required by Search-R1. Our code is available at https: //github. com/Applied-Machine-Learning-Lab/ReasonRAG.

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NeurIPS Conference 2025 Conference Paper

Purest Quantum State Identification

Yingqi Yu
Honglin Chen
Jun Wu
Wei Xie
Xiangyang Li

Quantum noise constitutes a fundamental obstacle to realizing practical quantum technologies. To address the pivotal challenge of identifying quantum systems least affected by noise, we introduce the purest quantum state identification, which can be used to improve the accuracy of quantum computation and communication. We formulate a rigorous paradigm for identifying the purest quantum state among $K$ unknown $n$-qubit quantum states using total $N$ quantum state copies. For incoherent strategies, we derive the first adaptive algorithm achieving error probability $\exp\left(- \Omega\left(\frac{N H_1}{\log(K) 2^n }\right) \right)$, fundamentally improving quantum property learning through measurement optimization. By developing a coherent measurement protocol with error bound $\exp\left(- \Omega\left(\frac{N H_2}{\log(K) }\right) \right)$, we demonstrate a significant separation from incoherent strategies, formally quantifying the power of quantum memory and coherent measurement. Furthermore, we establish a lower bound by demonstrating that all strategies with fixed two-outcome incoherent POVM must suffer error probability exceeding $ \exp\left( - O\left(\frac{NH_1}{2^n}\right)\right)$. This research advances the characterization of quantum noise through efficient learning frameworks. Our results establish theoretical foundations for noise-adaptive quantum property learning while delivering practical protocols for enhancing the reliability of quantum hardware.

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NeurIPS Conference 2022 Conference Paper

BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis

Yichong Leng
Zehua Chen
Junliang Guo
Haohe Liu
Jiawei Chen
Xu Tan
Danilo Mandic
Lei He

Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/ear related filtration, which, however, are difficult to accurately simulate in traditional digital signal processing. In this paper, we formulate the synthesis process from a different perspective by decomposing the binaural audio into a common part that shared by the left and right channels as well as a specific part that differs in each channel. Accordingly, we propose BinauralGrad, a novel two-stage framework equipped with diffusion models to synthesize them respectively. Specifically, in the first stage, the common information of the binaural audio is generated with a single-channel diffusion model conditioned on the mono audio, based on which the binaural audio is generated by a two-channel diffusion model in the second stage. Combining this novel perspective of two-stage synthesis with advanced generative models (i. e. , the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples. Experiment results show that on a benchmark dataset, BinauralGrad outperforms the existing baselines by a large margin in terms of both object and subject evaluation metrics (Wave L2: $0. 128$ vs. $0. 157$, MOS: $3. 80$ vs. $3. 61$). The generated audio samples\footnote{\url{https: //speechresearch. github. io/binauralgrad}} and code\footnote{\url{https: //github. com/microsoft/NeuralSpeech/tree/master/BinauralGrad}} are available online.

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NeurIPS Conference 2021 Conference Paper

FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition

Yichong Leng
Xu Tan
Linchen Zhu
Jin Xu
Renqian Luo
Linquan Liu
Tao Qin
Xiangyang Li

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER) than original ASR outputs. Previous works usually use a sequence-to-sequence model to correct an ASR output sentence autoregressively, which causes large latency and cannot be deployed in online ASR services. A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate. In this paper, observing distinctive error patterns and correction operations (i. e. , insertion, deletion, and substitution) in ASR, we propose FastCorrect, a novel NAR error correction model based on edit alignment. In training, FastCorrect aligns each source token from an ASR output sentence to the target tokens from the corresponding ground-truth sentence based on the edit distance between the source and target sentences, and extracts the number of target tokens corresponding to each source token during edition/correction, which is then used to train a length predictor and to adjust the source tokens to match the length of the target sentence for parallel generation. In inference, the token number predicted by the length predictor is used to adjust the source tokens for target sequence generation. Experiments on the public AISHELL-1 dataset and an internal industrial-scale ASR dataset show the effectiveness of FastCorrect for ASR error correction: 1) it speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER reduction) compared with the autoregressive correction model; and 2) it outperforms the popular NAR models adopted in neural machine translation and text edition by a large margin.

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AAAI Conference 2019 Conference Paper

Learning Object Context for Dense Captioning

Xiangyang Li
Shuqiang Jiang
Jungong Han

Dense captioning is a challenging task which not only detects visual elements in images but also generates natural language sentences to describe them. Previous approaches do not leverage object information in images for this task. However, objects provide valuable cues to help predict the locations of caption regions as caption regions often highly overlap with objects (i. e. caption regions are usually parts of objects or combinations of them). Meanwhile, objects also provide important information for describing a target caption region as the corresponding description not only depicts its properties, but also involves its interactions with objects in the image. In this work, we propose a novel scheme with an object context encoding Long Short-Term Memory (LSTM) network to automatically learn complementary object context for each caption region, transferring knowledge from objects to caption regions. All contextual objects are arranged as a sequence and progressively fed into the context encoding module to obtain context features. Then both the learned object context features and region features are used to predict the bounding box offsets and generate the descriptions. The context learning procedure is in conjunction with the optimization of both location prediction and caption generation, thus enabling the object context encoding LSTM to capture and aggregate useful object context. Experiments on benchmark datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods.

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