Bang Liu

qianggang.ding@mila.quebec

Biography

Bang Liu is an assistant professor in the Department of Computer Science and Operations Research (DIRO), and a core member of the Applied Research in Computational Linguistics Lab (RALI) at Université de Montréal. He is also an associate academic member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair.

Liu received his BEng from the University of Science and Technology of China in 2013, and his MSc and PhD degrees from the University of Alberta in 2015 and 2020, respectively. His research interests lie primarily in the areas of natural language processing, multimodal and embodied learning, theory and techniques for AGI (e.g., understanding and improving large language models), and AI for science (e.g., health, material science, XR).

Current Students

Qianggang Ding

PhD - Université de Montréal

Farshid Effaty

Postdoctorate - Université de Montréal

f.effaty@gmail.com

Wenhao Huang

PhD - Université de Montréal

huang.wenhao@mila.quebec

Yizhan Li

PhD - Université de Montréal

yizhan.li@mila.quebec

Kyle Roth

PhD - Université de Montréal

kyle.roth@mila.quebec

Haochen Shi

PhD - Université de Montréal

haochen.shi@mila.quebec

Xiran Song

PhD - Université de Montréal

xiran.song@mila.quebec

Github

suyuchen.wang@mila.quebec

Jia'ao Sun

PhD - Université de Montréal

sunjiaao@mila.quebec

Suyuchen Wang

PhD - Université de Montréal

PhD - Université de Montréal

xiaoqiang.wang@mila.quebec

Dekun Wu

PhD - Université de Montréal

dekun.wu@mila.quebec

Sifan Wu

PhD - Université de Montréal

sifan.wu@mila.quebec

Mengyang Xiong

Research Intern - McGill University

mengyang.xiong@mila.quebec

Yan Zhang

PhD - Université de Montréal

yan.zhang2@mila.quebec

Huan Zhang

Master's Research - Université de Montréal

huan.zhang@mila.quebec

armin.zolfagharidariani@mila.quebec

Armin Zolfagharidariani

Master's Research - Université de Montréal

Github

Blog Posts

MatSci-Instruct and HoneyBee training workflow.

October 21, 2024

Revolutionizing Materials Science with NLP: Introducing MatSci-NLP and HoneyBee

Bang Liu

Read the article

Publications

Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning

Jia Ao Sun

Yu Hao

Fengran Mo

Zhan Su

Yuchen Hui

Jian-Yun Nie

Knowledge graph question answering (KGQA) requires navigating from topic entities to an answer several relations away. Recent methods prompt… (see more) a frontier LLM to explore the graph through a retrieval tool, but their reliance on frontier-scale inference makes them costly to deploy. We present Search-on-Graph-R1 (\sogrone{}), which internalizes this navigation into a compact 8B model through supervised fine-tuning (SFT) followed by reinforcement learning (RL). Our central idea is to scaffold a frontier teacher with each question's gold SPARQL query, so the teacher traverses a known answer-bearing path with a live \texttt{Search} tool rather than having to discover the path itself. Since every call executes against a live Freebase server, the resulting trajectories are grounded in the knowledge graph by construction. On WebQSP, CWQ, and GrailQA, \sogrone{} at 8B surpasses every frozen frontier-LLM system in our comparison and posts the strongest results on CWQ of any system we compare against. It does so using no auxiliary module at inference and no LLM judge during training. Isolating each training stage shows that SFT and RL contribute complementary gains, our approach transfers across model families, and RL learns to reach answers in fewer \texttt{Search} calls than its SFT initialization.

2026-07-19

arXiv (preprint)

MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation

Junlin He

Yihong Tang

Tong Nie

Ao Qu

Yuebing Liang

Hamzeh Alizadeh

Wei Ma

Lijun Sun

Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, … (see more)including deep generative models, LLM-based methods, and traditional heuristics, struggle to satisfy the complex demands of this task while simultaneously maintaining interpretability, behavioral plausibility, population-level distributional alignment, and inference efficiency. To bridge this gap, we introduce MobEvolve, the first agentic self-evolving heuristic framework for human mobility generation. MobEvolve initializes a behavior-inspired heuristic system and employs an LLM agent to iteratively evolve its internal logic. By diagnosing empirical misalignments and failure cases on a validation set, the agent proposes targeted updates and accumulates evolution memory for cumulative self-improvement. Extensive evaluations on the Singapore and Montreal benchmarks demonstrate that MobEvolve significantly outperforms state-of-the-art deep generative and LLM-based methods in individual trajectory fidelity, population-level distribution alignment, and behavioral plausibility, while preserving interpretability and high inference efficiency.

2026-05-31

arXiv (preprint)

A survey of agentic materials science and engineering: where are we and where are we going?

Jiayi Zhu

Longhan Zhang

Yizhang Zhu

Xiaotian Lin

Yifan Wu

Shimin Di

Yuyu Luo

Tongyi Zhang

Agents, primarily built upon large language models (LLMs) and equipped with planning, tool use, memory, and self-reﬂection capabilities, a… (see more)re revolutionizing all aspects of materials science and engineering (MSE), from materials design and experimental execution to industrial manufacturing and deployment, thereby opening the age of agentic MSE. Rather than functioning as isolated artificial intelligence (AI) predictive models, these agents coordinate multi-step scientiﬁc workﬂows by retrieving and structuring knowledge, proposing and reﬁning hypotheses, planning experiments, combining multimodal simulations and characterizations, and, when integrated with AI materials laboratories, closing the loop toward autonomous materials discovery. However, agentic systems exhibit varying degrees of autonomy, and their roles in materials research and development diﬀer accordingly. To systematically examine the landscape of agentic MSE, this survey proposes a six-level autonomy framework (Levels 0-5) that characterizes the progression from human-only workﬂows to fully autonomous scientiﬁc agents. The framework aligns with key task families across the entire MSE pipeline, including information retrieval, property prediction, simulation, synthesis, and characterization. By reviewing recent advances in agentic MSE, we reveal uneven progress across these domains. Knowledge-centric capabilities often remain at an early stage, while experimental orchestration and characterization are starting to explore higher-level agent behaviors. Importantly, achieving mature autonomy requires coordinating multiple tasks rather than optimizing any single task in isolation. Collectively, these insights provide a structured roadmap for advancing agentic MSE toward higher levels of autonomy.

2026-05-25

Journal of Materials Informatics (published)

ReCode: Unify Plan and Action for Universal Granularity Control

Zhaoyang Yu

Jiayi Zhang

Huixue Su

Yufan Zhao

Yifan Wu

Mingyi Deng

Jinyu Xiang

Yizhang Lin

Fanqi Kong

Lingxiao Tang

Yuyu Luo

Chenglin Wu

Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where… (see more) planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose **ReCode** (**Re**cursive **Code** Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control.

2026-05-22

AIWILD @ International Conference on Machine Learning (published)

openreview.net

Mem-$π$: Adaptive Memory through Learning When and What to Generate

Xiaoqiang Wang

Chao Wang

Hadi Nekoei

Christopher Pal

Alexandre Lacoste

Spandana Gella

Perouz Taslakian

We present Mem-…

2026-05-19

arXiv (preprint)

Scalable Environments Drive Generalizable Agents

Jiayi Zhang

Fanqi Kong

Guibin Zhang

Maojia Song

Zhaoyang Yu

Jianhao Ruan

Jinyu Xiang

Chenglin Wu

Yuyu Luo

Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues th… (see more)at such generalization requires environment scaling: expanding the distribution of executable rule-sets that agents interact with, rather than only increasing trajectories or tasks within fixed benchmarks. Current scaling practices largely focus on collecting more experience or broader task sets under fixed interaction rules, leaving agents brittle when underlying interfaces, dynamics, observations, or feedback signals change. The core challenge is therefore a world-level distribution shift: agents need systematic exposure to environments with meaningfully different executable rule-sets. To clarify this challenge, we propose a unified taxonomy that separates trajectory scaling, task scaling, and environment scaling by their primary deliverables and by what changes in the executable rule-set. Building on this taxonomy, we synthesize construction paradigms for scalable environments, contrasting programmatic generators that prioritize controllability and verifiability with generative world models that offer broader coverage and open-endedness. We further outline how environment scaling can be coupled with stateful learning mechanisms, emphasizing learned update rules for cross-environment adaptation. We conclude by discussing alternative perspectives and argue that scalable environments provide the essential substrate for measurable and controllable progress toward robust general agents.

2026-05-17

arXiv (preprint)

EIAN: Explicit Interaction-aware Attention Network for Interpretable Event Modeling

Jiping Zhang

Hua Zhu

Hong Huang

Yi Zhou

Kehan Yin

Event sequences are integral to domains such as e-commerce, social networks, and healthcare. Traditional point process models, like Poisson … (see more)and Hawkes processes, are foundational but limited by rigid parametric assumptions, constraining their flexibility in complex real-world scenarios. Neural point processes offer a more adaptable alternative, but typically perform implicit sequence modeling, which does not fully exploit critical event interaction patterns and limits transparency. To address these challenges, we introduce the Explicit Interaction-aware Attention Network (EIAN), a novel model that enhances event modeling by explicitly capturing both intra-type and cross-type event interactions. Specifically, EIAN employs two key components: an intra-type temporal encoder that preserves the unique temporal dynamics within each event type, and a cross-type interaction decoder that highlights interactions across event types. Furthermore, two temporal encoding mechanisms are integrated into the interaction decoder to handle irregular inter-event intervals in diverse temporal scenarios. Extensive experiments show that EIAN consistently outperforms existing models in predictive performance and provides deeper insights into event interaction patterns, advancing both flexibility and interpretability. Our code is available at https://github.com/CGCL-codes/EIAN.git.

2026-04-11

ACM Web Conference (published)

Co-Evolution of Policy and Internal Reward for Language Agents

Xinyu Wang

Hanwei Wu

Jingwei Song

Shuyuan Zhang

Jiayi Zhang

Fanqi Kong

Tung Sum Thomas Kwok

Xiao-Wen Chang

Yuyu Luo

Chenglin Wu

Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sp… (see more)arse and delayed rewards. Existing methods typically address this challenge through post-hoc credit assignment or external reward models, which provide limited guidance at inference time and often separate reward improvement from policy improvement. We propose Self-Guide, a self-generated internal reward for language agents that supports both inference-time guidance and training-time supervision. Specifically, the agent uses Self-Guide as a short self-guidance signal to steer the next action during inference, and converts the same signal into step-level internal reward for denser policy optimization during training. This creates a co-evolving loop: better policy produces better guidance, and better guidance further improves policy as internal reward. Across three agent benchmarks, inference-time self-guidance already yields clear gains, while jointly evolving policy and internal reward with GRPO brings further improvements (8\%) over baselines trained solely with environment reward. Overall, our results suggest that language agents can improve not only by collecting more experience, but also by learning to generate and refine their own internal reward during acting and learning.

2026-04-02

arXiv (preprint)

Unsteady flow behavior and fluid-induced noise characteristics of T-junction pipe with a closed main branch

Jianchao Yu

Yinqi Wu

Xiating Jiang

Hui Huang

YuZheng Li

Fuqi Li

2026-03-31

Ocean Engineering (published)

What to Ask Next? Probing the Imaginative Reasoning of LLMs with TurtleSoup Puzzles

Mengtao Zhou

Sifan Wu

Huan Zhang

Qi Sima

We investigate the capacity of Large Language Models (LLMs) for imaginative reasoning—the proactive construction, testing, and revision of… (see more) hypotheses in information-sparse environments. Existing benchmarks, often static or focused on social deduction, fail to capture the dynamic, exploratory nature of this reasoning process. To address this gap, we introduce a comprehensive research framework based on the classic "Turtle Soup" game, integrating a benchmark, an agent, and an evaluation protocol. We present TurtleSoup-Bench, the first large-scale, bilingual, interactive benchmark for imaginative reasoning, comprising 800 turtle soup stories sourced from both the Internet and expert authors. We also propose Mosaic-Agent, a novel agent designed to assess LLMs' performance in this setting. To evaluate reasoning quality, we develop a multi-dimensional protocol measuring logical consistency, detail completion, and conclusion alignment. Experiments with leading LLMs reveal clear capability limits, common failure patterns, and a significant performance gap compared to humans. Our work offers new insights into LLMs' imaginative reasoning and establishes a foundation for future research on exploratory agent behavior.

2026-03-13

AAAI Conference on Artificial Intelligence (published)

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

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)

Latent Action Reparameterization for Efficient Agent Inference

Qingwen Zeng

Wenhao Huang

Zerui Xu

Zijie Guo

Yu Sun

Cheng Yang

Siru Ouyang

Jiri Gesi

Fang Wu

Jiayi Zhang