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Li He

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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.

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IROS Conference 2025 Conference Paper

Robust Reinforcement Learning based on Momentum Adversarial Training

Li He
Hanchen Liu
Junru Sheng
Lihua Zhang
Zhiyan Dong

Reinforcement learning (RL) is a fundamental and pivotal algorithm in the advancement of autonomous intelligence, including Embodied Intelligence and Physical Intelligence. The performance of RL directly influences the quality and efficiency of a robot’s decision-making and execution during interactions with its environment. Moreover, the robustness of RL remains a critical challenge that needs to be addressed. A promising approach to enhancing robustness is adversarial reinforcement learning. However, the existing methods primarily focus on perturbations in the state space, while perturbations in the action space have been relatively underexplored. The action space in RL is as crucial as the state space in autonomous intelligence. Furthermore, action-space perturbations provide a more comprehensive evaluation of RL robustness. Therefore, it is necessary and valuable to investigate RL robustness under action-space perturbations for the development of autonomous intelligence. To this end, we propose an adversarial learning framework that employs momentum-based gradient descent to model perturbations in the action space, such as actuator disturbances. Furthermore, we introduce an improved optimization method that integrates historical gradient information into conventional Stochastic Gradient Descent (SGD). This approach enhances training stability and improves perturbation efficiency. The proposed method is evaluated through simulations in the MuJoCo environment and UAV control experiments in GymFC, demonstrating significant improvements in robustness and adaptability under action-space perturbations. Additionally, real-world UAV flight tests are conducted to further validate the effectiveness of the proposed framework. The results confirm that the Sim-to-Real transfer is successful, providing empirical evidence for the applicability of our method in real-world scenarios. This study shows that enhancing RL robustness through action-space perturbations is feasible and effective. More importantly, our findings contribute to the future development of autonomous intelligence, particularly in improving its resilience to uncertainties and dynamic environments.

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NeurIPS Conference 2024 Conference Paper

Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals

Hui Zheng
Hai-Teng Wang
Wei-Bang Jiang
Zhong-Tao Chen
Li He
Pei-Yang Lin
Peng-Hu Wei
Guo-Guang Zhao

Invasive brain-computer interfaces with Electrocorticography (ECoG) have shown promise for high-performance speech decoding in medical applications, but less damaging methods like intracranial stereo-electroencephalography (sEEG) remain underexplored. With rapid advances in representation learning, leveraging abundant recordings to enhance speech decoding is increasingly attractive. However, popular methods often pre-train temporal models based on brain-level tokens, overlooking that brain activities in different regions are highly desynchronized during tasks. Alternatively, they pre-train spatial-temporal models based on channel-level tokens but fail to evaluate them on challenging tasks like speech decoding, which requires intricate processing in specific language-related areas. To address this issue, we collected a well-annotated Chinese word-reading sEEG dataset targeting language-related brain networks from 12 subjects. Using this benchmark, we developed the Du-IN model, which extracts contextual embeddings based on region-level tokens through discrete codex-guided mask modeling. Our model achieves state-of-the-art performance on the 61-word classification task, surpassing all baselines. Model comparisons and ablation studies reveal that our design choices, including (\romannumeral1) temporal modeling based on region-level tokens by utilizing 1D depthwise convolution to fuse channels in the ventral sensorimotor cortex (vSMC) and superior temporal gyrus (STG) and (\romannumeral2) self-supervision through discrete codex-guided mask modeling, significantly contribute to this performance. Overall, our approach -- inspired by neuroscience findings and capitalizing on region-level representations from specific brain regions -- is suitable for invasive brain modeling and represents a promising neuro-inspired AI approach in brain-computer interfaces. Code and dataset are available at https: //github. com/liulab-repository/Du-IN.

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ICRA Conference 2024 Conference Paper

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

Qingmao Wei
Bi Zeng
Jianqi Liu
Li He
Guotian Zeng

The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, running latency correspondingly increases, presenting a challenge for real-time robotics applications, especially on edge devices with computational constraints. In response to this, we introduce LiteTrack, an efficient transformer-based tracking model optimized for high-speed operations across various devices. It achieves a more favorable trade-off between accuracy and efficiency than the other lightweight trackers. The main innovations of LiteTrack encompass: 1) asynchronous feature extraction and interaction between the template and search region for better feature fushion and cutting redundant computation, and 2) pruning encoder layers from a heavy tracker to refine the balnace between performance and speed. As an example, our fastest variant, LiteTrack-B4, achieves 65. 2% AO on the GOT-10k benchmark, surpassing all preceding efficient trackers, while running over 100 fps with ONNX on the Jetson Orin NX edge device. Moreover, our LiteTrack-B9 reaches competitive 72. 2% AO on GOT-10k and 82. 4% AUC on TrackingNet, and operates at 171 fps on an NVIDIA 2080Ti GPU. The code and demo materials will be available at https://github.com/TsingWei/LiteTrack.

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ICML Conference 2023 Conference Paper

Beyond Reward: Offline Preference-guided Policy Optimization

Yachen Kang
Diyuan Shi
Jinxin Liu
Li He
Donglin Wang

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https: //sites. google. com/view/oppo-icml-2023.

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NeurIPS Conference 2023 Conference Paper

CEIL: Generalized Contextual Imitation Learning

Jinxin Liu
Li He
Yachen Kang
Zifeng Zhuang
Donglin Wang
Huazhe Xu

In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1) learning from observations (LfO), 2) offline IL, 3) cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.

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NeurIPS Conference 2023 Conference Paper

Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. To close this gap, we first conduct an empirical study to disclose that the edge noise bilaterally perturbs both input topology and target label, yielding severe performance degradation and representation collapse. To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse. Different from the basic information bottleneck, RGIB further decouples and balances the mutual dependence among graph topology, target labels, and representation, building new learning objectives for robust representation against the bilateral noise. Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies, i. e. , self-supervised learning and data reparameterization, for implicit and explicit data denoising, respectively. Extensive experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations. The code is publicly available at: https: //github. com/tmlr-group/RGIB.

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ICML Conference 2023 Conference Paper

Exploring Model Dynamics for Accumulative Poisoning Discovery

Jianing Zhu
Xiawei Guo
Jiangchao Yao
Chao Du
Li He
Shuo Yuan
Tongliang Liu
Liang Wang 0001

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples based on their distinct dynamics from the clean samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks. Extensive experiments comprehensively characterized Memorization Discrepancy and verified its effectiveness. The code is publicly available at: https: //github. com/tmlr-group/Memorization-Discrepancy.

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ECAI Conference 2023 Conference Paper

OER: Offline Experience Replay for Continual Offline Reinforcement Learning

Sibo Gai
Donglin Wang
Li He

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks. For consistently learning on all sequential tasks, an agent requires acquiring new knowledge and meanwhile preserving old knowledge in an offline manner. To this end, we introduced continual learning algorithms and experimentally found experience replay (ER) to be the most suitable algorithm for the CORL problem. However, we observe that introducing ER into CORL encounters a new distribution shift problem: the mismatch between the experiences in the replay buffer and trajectories from the learned policy. To address such an issue, we propose a new model-based experience selection (MBES) scheme to build the replay buffer, where a transition model is learned to approximate the state distribution. This model is used to bridge the distribution bias between the replay buffer and the learned model by filtering the data from offline data that most closely resembles the learned model for storage. Moreover, in order to enhance the ability on learning new tasks, we retrofit the experience replay method with a new dual behavior cloning (DBC) architecture to avoid the disturbance of behavior-cloning loss on the Q-learning process. In general, we call our algorithm offline experience replay (OER). Extensive experiments demonstrate that our OER method outperforms SOTA baselines in widely-used Mujoco environments.

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YNIMG Journal 2021 Journal Article

Connectome-based evidence for creative thinking as an emergent property of ordinary cognitive operations

Kaixiang Zhuang
Wenjing Yang
Yu Li
Jie Zhang
Qunlin Chen
Jie Meng
Dongtao Wei
Jiangzhou Sun

Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel "travelling pattern prediction analysis", here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.

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IJCAI Conference 2018 Conference Paper

Differential Equations for Modeling Asynchronous Algorithms

Li He
Qi Meng
Wei Chen
Zhi-Ming Ma
Tie-Yan Liu

Asynchronous stochastic gradient descent (ASGD) is a popular parallel optimization algorithm in machine learning. Most theoretical analysis on ASGD take a discrete view and prove upper bounds for their convergence rates. However, the discrete view has its intrinsic limitations: there is no characterizationof the optimization path and the proof techniques are induction-based and thus usually complicated. Inspired by the recent successful adoptions of stochastic differential equations (SDE) to the theoretical analysis of SGD, in this paper, we study the continuous approximation of ASGD by using stochastic differential delay equations (SDDE). We introduce the approximation method and study the approximation error. Then we conduct theoretical analysis on the convergence rate of ASGD algorithm based on the continuous approximation. There are two methods: moment estimation and energy function minimization can be used to analyzethe convergence rates. Moment estimation depends on the specific form of the loss function, while energy function minimization only leverages the convex property of the loss function, and does not depend on its specific form. In addition to the convergence analysis, the continuous view also helps us derive better convergence rates. All of this clearly shows the advantage of taking the continuous view in gradient descent algorithms.

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TCS Journal 2014 Journal Article

Conditional diagnosability of optical multi-mesh hypercube networks under the comparison diagnosis model

XianYong Li
Xiaofan Yang
Li He
Jing Zhang
Cui Yu

Due to integrated positive features of both hypercubes and tori, optical multi-mesh hypercube (OMMH) networks are regarded as a class of promising optical interconnection topologies. The notion of conditional diagnosability helps enhance the self-diagnosing capability of multicomputers. This paper determines the conditional diagnosabilities of OMMH networks under the Maeng–Malek comparison model.

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TCS Journal 2014 Journal Article

Optimal wavelength assignment in the implementation of parallel algorithms with ternary n -cube communication pattern on mesh optical network

Cui Yu
Xiaofan Yang
Li He
Jing Zhang

Multi-ary n-cubes are a class of communication patterns that are employed by a number of typical parallel algorithms. This paper addresses the implementation of parallel algorithms with bidirectional and unidirectional ternary n-cube communication patterns on a mesh WDM optical network. For each of these two communication patterns, a routing and wavelength assignment scheme is described, and the number of wavelengths required is shown to attain the minimum, which guarantees the optimality of the proposed scheme.

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