Portrait of Haochen Shi

Haochen Shi

PhD - Université de Montréal
Supervisor
Bang Liu
Research Topics
Deep Learning
Dynamical Systems
Multimodal Learning
Natural Language Processing
Reasoning

Publications

TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
Qianggang Ding
Haochen Shi
Luis Castejón Lozano
Miguel Conner
Juan Abia
Luis Gallego-Ledesma
Joshua Fellowes
Gerard Conangla Planes
Adam Elwood
Bang Liu
We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies s… (see more)ymbolic relational priors, dynamic graph exploration, and LLM-guided decision making in a single end-to-end pipeline. The approach performs rule-guided multi-hop exploration restricted to admissible relation sequences, grounds candidate reasoning chains in contemporaneous news, and aggregates fully grounded evidence into auditable \texttt{UP}/\texttt{DOWN} verdicts with human-readable paths connecting text and structure. On an S\&P~500 benchmark, the method achieves 55.1\% accuracy, 55.7\% precision, 71.5\% recall, and 60.8\% F1, surpassing strong baselines and improving recall and F1 over the best graph baseline under identical evaluation. The gains stem from (i) rule-guided exploration that focuses search on economically meaningful motifs rather than arbitrary walks, and (ii) text-grounded consolidation that selectively aggregates high-confidence, fully grounded hypotheses instead of uniformly pooling weak signals. Together, these choices yield higher sensitivity without sacrificing selectivity, delivering predictive lift with faithful, auditably interpretable explanations.
2026-03-11
arXiv (preprint)
doi.org
arxiv.org
Evolving Programmatic Skill Networks
Haochen Shi
Xingdi Yuan
Bang Liu
We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding libra… (see more)ry of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\footnote{We plan to open-source the code.
2026-01-06
arXiv (preprint)
doi.org
arxiv.org
Enhancing Agent Learning through World Dynamics Modeling
Zhiyuan Sun
Haochen Shi
Marc-Alexandre Côté
Glen Berseth
Xingdi Yuan
Bang Liu
Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their i… (see more)mpressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.
2024-07-24
arXiv (Cornell University) (preprint)
doi.org
arxiv.org