Changjian Liu 刘昌健

Changjian Liu

Hi! I am Changjian Liu (刘昌健), a master’s student in Spatio-Temporal Big Data at Peking University, where I study at the Institute of Remote Sensing and Geographic Information Systems, under the supervision of Prof. Yong Gao. I am currently a Research Intern at Galbot, working on agentic systems for embodied brains and long-horizon tasks.

Previously, I was an Intern at Alibaba · Taobao & Tmall Group · Alimama, where I worked on agentic reinforcement learning, causal decision systems, and large-scale decision optimization, and an Intern at DiDi, focusing on advertising algorithms and decision making. I received my B.Eng. in Computer Science and Technology from China University of Geosciences, Beijing.

My research interests lie broadly in agentic reinforcement learning, LLM post-training, and sequential decision making. I am particularly interested in how intelligent agents can learn from interaction, assign credit over long horizons, and reliably translate reasoning into actions in both digital and embodied environments.

EmailGitHubGoogle Scholar

Research interests

From learning signals to real-world decisions.

Learning from interaction

How can an agent learn which decisions mattered over a long task? I study credit assignment, agentic reinforcement learning, and learning from experimental feedback.

  • Agentic RL
  • LLM post-training
  • Credit assignment

Making better decisions

How can learned policies stay useful under constraints and distribution shift? My work connects causal learning with decision objectives, from resource allocation to LLM agents.

  • LLM decision making
  • Causal policy learning

Bringing reasoning into the world

How can robots reason about a task and carry it out? At Galbot, I am exploring agentic approaches to embodied reasoning and task execution.

  • Embodied agents
  • Reasoning & execution
Observe, reason, and act A conceptual agent loop: observe the context, reason about a next step, then act in the world. Feedback from action informs the next observation and decision. Observe Reason Act Read the context Plan a next step Interact with the world Feedback informs the next observation and decision
  1. ObserveRead the context
  2. ReasonPlan a next step
  3. ActInteract with the world

Feedback informs the next observation and decision

My research connects learning from feedback with decisions that lead to action.

Selected research

Current projects, submissions, and published work.

Google Scholar

LLMs & agentic learning

EXACT: EXecution-Aware Credit Assignment with Target Conservation

Ongoing researchFirst author

Execution-aware credit assignment for long-horizon agents. The project uses local verification signals to connect feedback to relevant decisions while preserving the original reward objective, with a training pipeline built on verl.

AutoAttribRec: Attribution-Guided Self-Evolving Research for Recommender Systems

Submitted to AAAI 2027Fourth author

Helping research agents learn from experiments through attribution checks and hierarchical memory, so that verified insights can inform later tasks.

Causal decision making

Conceptual illustration of ReAlloc, contrasting local supported reallocation with unsafe global predict-then-optimize extrapolation.

Multi-channel Uplift Policy Learning

Changjian Liu, Tianyu Wang, Xiaoxuan Deng, Wentao Zhu, Yuwei Xu, Junqi Jin, Yong Gao, Chuan Yu, Jian Xu, Bo Zheng

Submitted to KDD 2027 Applied Data Science (ADS) TrackFirst author

ReAlloc formulates fixed-budget multi-channel marketing as simplex-constrained uplift policy learning, combining an orthogonal teacher, explanation-guided student, and support-aware local reallocation for stable production decisions.

PDFarXiv
Figure 1 from the paper accepted at NeurIPS 2026, illustrating target mismatch under temporal drift and pooled action-response learning.

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

NeurIPS 2026 · AcceptedFirst author

A causal response learning framework for continuous decisions under temporal drift, using anchored link-scale contrasts, orthogonal pilots, and profiled morphology selection to learn deployable action geometry.

Interactive demo

Anchor-Regularized Advertising ROI-Uplift Frontier Learning

Submitted to KDD 2027First author

Research on anchor-regularized ROI–uplift frontier learning for advertising decisions.

Graph learning & GeoAI6 works

My earlier work on structured environments, mobility, and generalization.

First-page preview of the OpFlow arXiv preprint, showing the paper title, author list, and abstract.

OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction

Changjian Liu, Yong Gao, Yuqing Wang, Leyi Su, Honglei Guo, Zhiyang Wang, Xiaoyu Wang, Fan Zhang

arXiv preprint; submitted to AAAI 2027First author

A mechanism-constrained framework for robust origin-destination flow prediction that learns row-centered choice potentials and separates transferable allocation laws from origin demand scale.

arXivPDF
Figure from the trajectory semantic model paper, showing spatial dependence modelling and temporal dependence modelling.

Integrating trajectory data and demographic characteristics: a trajectory semantic model for predicting travel flow and conducting interaction analysis

Changjian Liu, Shuhui Gong, Hui Su, Jianwei Chen, Honglei Guo, Jifeng He, Changfeng Jing, Yu Liu

International Journal of Digital Earth 17(1), 2392842 (2024)First author

A trajectory semantic modeling framework that combines movement traces with demographic structure for travel-flow prediction and interaction analysis.

PaperPDFScholar
Figure 1 from the photovoltaic power forecasting paper, showing the workflow and schematic diagram of the proposed model.

High precision prediction of time-varying photovoltaic power based on dynamic adjacency matrix and temporal spectral graph convolution network

Honglei Guo, Zhenchan Su, Zhiwei Wang, Guiren Zhan, Changjian Liu, Ling Bu

Energy Conversion and Management: X 30, 101676 (2026)Fifth author

A dynamic graph forecasting model for time-varying photovoltaic power prediction under volatile renewable-energy generation patterns.

PaperScholar
Figure from the VisitFrequency-Diffusion paper, showing diffusion-based trajectory generation and trajectory refinement.

VisitFrequency-Diffusion: Leveraging Recurrent Visits for Long-Term Individual Trajectory Forecasting

Ziyan Yang, Shuhui Gong, Xinqi Liu, Jiahao Lv, Changjian Liu, Jilin Hu, Hongbin Pei

Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, 1190-1193 (2025)Fifth author

A generative trajectory forecasting study that uses recurrent visit patterns to improve long-horizon individual mobility prediction.

PaperPDFScholar

SH2EM: A Meta-Learning Framework for Explainable Spatial Regression via Global Homogeneity and Local Heterogeneity Modeling

International Journal of Geographical Information Science · Minor revisionFirst author

Explainable spatial regression combining global homogeneity and local heterogeneity through meta-learning.

VEGAR propagation architecture combining signed attraction and user subgraphs through five propagation procedures and spatial-sentiment attention.

VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph

Renjun Cao, Yong Gao, Yi Zhang, Changjian Liu, Zhiyang Wang

Journal of King Saud University Computer and Information Sciences (2026)Fourth author

Tourist attraction recommendation with signed feedback and a spatial-sentiment knowledge graph.

PaperFigure 4CC BY 4.0

Experience

Learning and decision systems in practice.

Sep 2026 – Present

Galbot

Research internship · Embodied intelligence brain

Working on agentic approaches to robot reasoning and task execution, exploring how robots can think through tasks and translate decisions into actions.

Nov 2025 – Jun 2026

Alibaba · Taotian Group · Alimama

LLM Algorithm Intern

Worked on agentic reinforcement learning, self-evolving research agents, and causal decision systems. Projects covered execution-aware credit assignment, experimental attribution, pricing, and constrained multi-channel allocation.

Jun 2025 – Oct 2025

Didi · International Business

Advertising Algorithm Intern

Developed models for personalized coupon allocation, combining purchase, redemption, and short-term value prediction with uncertainty-aware treatment modeling and budget-constrained optimization.

Education

Selected honors
  • Academic Scholarship, Peking University
  • National Scholarship
  • Xiaomi Scholarship
  • National Second Prize, China Undergraduate Mathematical Contest in Modeling
  • National First Prize, China Computer Design Competition
  • National Innovation Project Leader
  • Outstanding Student / Outstanding Party Member / Outstanding Class Cadre

Research notes

All notes

Engineering Notes

From OD Flows to Decision Systems

A short technical outline for connecting spatial flow modeling with downstream allocation, pricing, and intervention decisions.

Updates

All news

Paper accepted at NeurIPS 2026

Our work on deployable causal action geometry under temporal non-stationarity has been accepted at NeurIPS 2026.

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