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Kang Xu

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning

Jianbo Yu
Yixuan Li
Hai Xu
Kang Xu
Junjielong Xu
Zhijing Li
Pinjia He
Wanyuan Wang

Log parsing converts semi-structured logs into structured templates, forming a critical foundation for downstream analysis. Traditional syntax and semantic-based parsers often struggle with semantic variations in evolving logs and data scarcity stemming from their limited domain coverage. Recent large language model (LLM)-based parsers leverage in-context learning (ICL) to extract semantics from examples, demonstrating superior accuracy. However, LLM-based parsers face two main challenges: 1) underutilization of ICL capabilities, particularly in dynamic example selection and cross-domain generalization, leading to inconsistent performance; 2) time-consuming and costly LLM querying. To address these challenges, we present MicLog, the first progressive meta in-context learning (ProgMeta-ICL) log parsing framework that combines meta-learning with ICL on small open-source LLMs (i.e., Qwen-2.5-3B). Specifically, MicLog: i) enhances LLMs' ICL capability through a zero-shot to k-shot ProgMeta-ICL paradigm, employing weighted DBSCAN candidate sampling and enhanced BM25 demonstration selection; ii) accelerates parsing via a multi-level pre-query cache that dynamically matches and refines recently parsed templates. Evaluated on Loghub-2.0, MicLog achieves 10.3% higher parsing accuracy than the state-of-the-art parser while reducing parsing time by 42.4%.

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ICRA Conference 2025 Conference Paper

M3DSS: A Multi-Platform, Multi-Sensor, and Multi-Scenario Dataset for SLAM System

Shulei Huang
Haotian Zhang
Kang Xu
Xianwei Lv
Xiaoguang Ma

This paper proposed M3DSS, a multi-platform, multi-sensor, and multi-scenario dataset for Simultaneous Localization and Mapping (SLAM) systems. Fifty-five sequences were collected from multiple platforms, including a handheld equipment, an unmanned ground vehicle, a quadruped robot, a car, and an unmanned aerial vehicle. Sensors used in M3DSS included two pairs of stereo event cameras with resolutions of $640\times 480$ and $346\times 260$, one infrared camera, four RGB cameras, two visual-inertial sensors, four mechanical and one solid-state LiDARs, three inertial measurement units, two global navigation satellite and inertial navigation systems with real-time kinematic signals. 21 various sensors were used on 5 different platforms under various challenging scenarios, including extreme illumination, aggressive motion, low-texture, high-speed driving scenarios, etc. To the best of our knowledge, M3DSS offered the richest event-based sensory information for SLAM up to date. We comprehensively evaluated state-of-the-art SLAM approaches and identified their limitations on M3DSS. Details could be found at https://neufs-ma.github.io/M3DSS.

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ICLR Conference 2025 Conference Paper

Online Preference Alignment for Language Models via Count-based Exploration

Chenjia Bai
Yang Zhang
Shuang Qiu
Qiaosheng Zhang 0002
Kang Xu
Xuelong Li 0001

Reinforcement Learning from Human Feedback (RLHF) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences. Existing methods perform preference alignment from a fixed dataset, which can be limited in data coverage and the resulting reward model is hard to generalize in out-of-distribution responses. Thus, online RLHF is more desirable to empower the LLM to explore outside the support of the initial dataset by iteratively collecting the prompt-response pairs. In this paper, we study the fundamental problem in online RLHF, i.e., how to explore for LLM. We give a theoretical motivation in linear reward assumption to show that an optimistic reward with an upper confidence bound (UCB) term leads to a provably efficient RLHF policy. Then, we reformulate our objective to direct preference optimization with an exploration term, where the UCB-term can be converted to a count-based exploration bonus. We further propose a practical algorithm, named Count-based Online Preference Optimization (COPO), which leverages a simple coin-flip counting module to estimate the pseudo-count of a prompt-response pair in previously collected data. COPO encourages LLMs to balance exploration and preference optimization in an iterative manner, which enlarges the exploration space and the entire data coverage of iterative LLM policies. We conduct online RLHF experiments on Zephyr and Llama-3 models. The results on instruction-following and standard academic benchmarks show that COPO significantly increases performance.

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ICML Conference 2024 Conference Paper

Constrained Ensemble Exploration for Unsupervised Skill Discovery

Chenjia Bai
Rushuai Yang
Qiaosheng Zhang 0002
Kang Xu
Yi Chen
Ting Xiao 0002
Xuelong Li 0001

Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly conduct empowerment-driven skill discovery or entropy-based exploration. However, empowerment often leads to static skills, and pure exploration only maximizes the state coverage rather than learning useful behaviors. In this paper, we propose a novel unsupervised RL framework via an ensemble of skills, where each skill performs partition exploration based on the state prototypes. Thus, each skill can explore the clustered area locally, and the ensemble skills maximize the overall state coverage. We adopt state-distribution constraints for the skill occupancy and the desired cluster for learning distinguishable skills. Theoretical analysis is provided for the state entropy and the resulting skill distributions. Based on extensive experiments on several challenging tasks, we find our method learns well-explored ensemble skills and achieves superior performance in various downstream tasks compared to previous methods.

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ICML Conference 2024 Conference Paper

Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning

Xiaoyu Wen
Chenjia Bai
Kang Xu
Xudong Yu
Yang Zhang
Xuelong Li 0001
Zhen Wang 0004

Cross-domain offline reinforcement learning leverages source domain data with diverse transition dynamics to alleviate the data requirement for the target domain. However, simply merging the data of two domains leads to performance degradation due to the dynamics mismatch. Existing methods address this problem by measuring the dynamics gap via domain classifiers while relying on the assumptions of the transferability of paired domains. In this paper, we propose a novel representation-based approach to measure the domain gap, where the representation is learned through a contrastive objective by sampling transitions from different domains. We show that such an objective recovers the mutual-information gap of transition functions in two domains without suffering from the unbounded issue of the dynamics gap in handling significantly different domains. Based on the representations, we introduce a data filtering algorithm that selectively shares transitions from the source domain according to the contrastive score functions. Empirical results on various tasks demonstrate that our method achieves superior performance, using only 10% of the target data to achieve 89. 2% of the performance on 100% target dataset with state-of-the-art methods.

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YNICL Journal 2024 Journal Article

Functional network reorganization after endovascular thrombectomy in patients with anterior circulation stroke

Tongyue Li
Jiaona Xu
Luoyu Wang
Kang Xu
Weiwei Chen
Liqing Zhang
Guozhong Niu
Yu Zhang

BACKGROUND: Endovascular thrombectomy has been confirmed to be an effective therapy for acute ischemic stroke (AIS). However, how functional brain networks reorganize after restoration of blood supply in AIS patients, and whether the degree of reperfusion associates with functional network changes remains unclear. METHODS: Resting-state fMRI data were collected from 43 AIS patients with anterior circulation occlusion after thrombectomy and 37 healthy controls (HCs). Both static and dynamic functional connectivity (FC) within four advanced functional networks including dorsal attention network (DAN), ventral attention network (VAN), executive control network (ECN) and default mode network (DMN), were calculated and compared between post-thrombectomy patients and HCs, and between two subgroups of post-thrombectomy patients with different reperfusion conditions. RESULTS: As compared to HCs, patients showed significant differences in static FC of four functional networks, and in dynamic FC of DAN, ECN and DMN. Furthermore, patients with better reperfusion conditions exhibited increased static FC with precuneus, and altered dynamic FC within precuneus. Moreover, these alterations were associated with clinical assessments of stroke severity and functional recovery in post-thrombectomy patients. CONCLUSIONS: Collectively, these findings may provide the potential imaging markers for assessment of thrombectomy efficacy and help establish the specific rehabilitation treatments for post-thrombectomy patients.

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NeurIPS Conference 2024 Conference Paper

ODRL: A Benchmark for Off-Dynamics Reinforcement Learning

Jiafei Lyu
Kang Xu
Jiacheng Xu
Mengbei Yan
Jingwen Yang
Zongzhang Zhang
Chenjia Bai
Zongqing Lu

We consider off-dynamics reinforcement learning (RL) where one needs to transfer policies across different domains with dynamics mismatch. Despite the focus on developing dynamics-aware algorithms, this field is hindered due to the lack of a standard benchmark. To bridge this gap, we introduce ODRL, the first benchmark tailored for evaluating off-dynamics RL methods. ODRL contains four experimental settings where the source and target domains can be either online or offline, and provides diverse tasks and a broad spectrum of dynamics shifts, making it a reliable platform to comprehensively evaluate the agent's adaptation ability to the target domain. Furthermore, ODRL includes recent off-dynamics RL algorithms in a unified framework and introduces some extra baselines for different settings, all implemented in a single-file manner. To unpack the true adaptation capability of existing methods, we conduct extensive benchmarking experiments, which show that no method has universal advantages across varied dynamics shifts. We hope this benchmark can serve as a cornerstone for future research endeavors. Our code is publicly available at https: //github. com/OffDynamicsRL/off-dynamics-rl.

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NeurIPS Conference 2023 Conference Paper

Cross-Domain Policy Adaptation via Value-Guided Data Filtering

Kang Xu
Chenjia Bai
Xiaoteng Ma
Dong Wang
Bin Zhao
Zhen Wang
Xuelong Li
Wei Li

Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator, but when it is deployed in the real world, the dynamics of the environment may be different. Given the source and target domain with dynamics mismatch, we consider the online dynamics adaptation problem, in which case the agent can access sufficient source domain data while online interactions with the target domain are limited. Existing research has attempted to solve the problem from the dynamics discrepancy perspective. In this work, we reveal the limitations of these methods and explore the problem from the value difference perspective via a novel insight on the value consistency across domains. Specifically, we present the Value-Guided Data Filtering (VGDF) algorithm, which selectively shares transitions from the source domain based on the proximity of paired value targets across the two domains. Empirical results on various environments with kinematic and morphology shifts demonstrate that our method achieves superior performance compared to prior approaches.

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NeurIPS Conference 2023 Conference Paper

Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Haoran He
Chenjia Bai
Kang Xu
Zhuoran Yang
Weinan Zhang
Dong Wang
Bin Zhao
Xuelong Li

Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of addressing multi-task predicaments is absent. In this paper, we aim to investigate the effectiveness of a single diffusion model in modeling large-scale multi-task offline data, which can be challenging due to diverse and multimodal data distribution. Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings. \textsc{MTDiff} leverages vast amounts of knowledge available in multi-task data and performs implicit knowledge sharing among tasks. For generative planning, we find \textsc{MTDiff} outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D. For data synthesis, \textsc{MTDiff} generates high-quality data for testing tasks given a single demonstration as a prompt, which enhances the low-quality datasets for even unseen tasks.

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AAAI Conference 2023 Conference Paper

Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation

Kang Xu
Yan Ma
Bingsheng Wei
Wei Li

While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch or unexpected perturbations. Recent works have demonstrated that a policy population with diverse behavior characteristics can generalize to downstream environments with various discrepancies. However, such policies might result in catastrophic damage during the deployment in practical scenarios like real-world systems due to the unrestricted behaviors of trained policies. Furthermore, training diverse policies without regulation of the behavior can result in inadequate feasible policies for extrapolating to a wide range of test conditions with dynamics shifts. In this work, we aim to train diverse policies under the regularization of the behavior patterns. We motivate our paradigm by observing the inverse dynamics in the environment with partial state information and propose Diversity in Regulation (DiR) training diverse policies with regulated behaviors to discover desired patterns that benefit the generalization. Considerable empirical results on various variations of different environments indicate that our method attains improvements over other diversity-driven counterparts.

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IJCAI Conference 2018 Conference Paper

Fine-grained Image Classification by Visual-Semantic Embedding

Huapeng Xu
Guilin Qi
Jingjing Li
Meng Wang
Kang Xu
Huan Gao

This paper investigates a challenging problem, which is known as fine-grained image classification(FGIC). Different from conventional computer visionproblems, FGIC suffers from the large intraclassdiversities and subtle inter-class differences. Existing FGIC approaches are limited to exploreonly the visual information embedded in the images. In this paper, we present a novel approachwhich can use handy prior knowledge from eitherstructured knowledge bases or unstructured text tofacilitate FGIC. Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space. Experimental results on a challenginglarge-scale UCSD Bird-200-2011 datasetverify that our approach outperforms several state-of-the-art methods with significant advances.

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AAAI Conference 2016 Conference Paper

Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model

Tianxing Wu
Guilin Qi
Haofen Wang
Kang Xu
Xuan Cui

As more and more multilingual knowledge becomes available on the Web, knowledge sharing across languages has become an important task to beneﬁt many applications. One of the most crucial kinds of knowledge on the Web is taxonomy, which is used to organize and classify the Web data. To facilitate knowledge sharing across languages, we need to deal with the problem of cross-lingual taxonomy alignment, which discovers the most relevant category in the target taxonomy of one language for each category in the source taxonomy of another language. Current approaches for aligning crosslingual taxonomies strongly rely on domain-speciﬁc information and the features based on string similarities. In this paper, we present a new approach to deal with the problem of cross-lingual taxonomy alignment without using any domain-speciﬁc information. We ﬁrst identify the candidate matched categories in the target taxonomy for each category in the source taxonomy using the crosslingual string similarity. We then propose a novel bilingual topic model, called Bilingual Biterm Topic Model (BiBTM), to perform exact matching. BiBTM is trained by the textual contexts extracted from the Web. We conduct experiments on two kinds of real world datasets. The experimental results show that our approach signiﬁcantly outperforms the designed state-of-the-art comparison methods.

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