Bang Liu

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

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

Yinhan Lu

Master's Research - Université de Montréal

Co-supervisor :

David Ifeoluwa Adelani

yinhan.lu@mila.quebec

Github

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

yihong.tang@mail.mcgill.ca

Jia'ao Sun

PhD - Université de Montréal

sunjiaao@mila.quebec

Winston Tang

PhD - McGill University

suyuchen.wang@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

Mengyang Xiong

Research Intern - McGill University

mengyang.xiong@mila.quebec

Yan Zhang

PhD - Université de Montréal

yan.zhang2@mila.quebec

Armin Zolfagharidariani

Master's Research - Université de Montréal

armin.dariani@mila.quebec

Github

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

Blog Posts

MatSci-Instruct and HoneyBee training workflow.

October 21, 2024

Bang Liu

Read the article

Publications

SMC1A is required for fate determinations of human spermatogonial stem cells and male fertility by interacting with YBX1 and stabilizing HMGA2 mRNA via an m5C modification

Wei Liu

Li Du

Yanna Zhu

Yuxuan Xu

Dai Zhou

Jun Gao

Yinghong Cui

Wei Chen

Chunyun Li

Yongzhe Chen

Fen Jiang

Zuping He

Spermatogonial stem cells (SSCs) are required for initiating and maintaining normal spermatogenesis, and notably, they have significant appl… (see more)ications in both reproductive and regenerative medicine owing to their great plasticity with de-differentiation and trans-differentiation potentials. Nevertheless, molecular mechanisms regulating human SSC fate determinations and male fertility remain elusive. Here we report for the first time that structural maintenance of chromosomes 1A (SMC1A) interacts with RNA-binding protein YBX1 to control fate decisions of human SSCs and an association exists between SMC1A dysfunction with male infertility. SMC1A/YBX1 complex stabilizes high mobility group AT-hook 2 (HMGA2) mRNA in an m5C modification-dependent manner to activate the G1/S phase transition, which ultimately mediates human SSC self-renewal and apoptosis. Transplantation of SSCs into the seminiferous tubules of recipient mice without male germ cells reveals that Smc1a silencing impairs SSC colonization and causes differentiation defect. Notably, Smc1a-conditional knockout (Smc1a-cKO) mice assume spermatogenesis disorder and obviously compromise male fertility. Single-cell transcriptomics demonstrates that Smc1a-cKO mice exhibit the downregulation of genes associated with mitosis, meiosis, and m5C regulation. Significantly, whole-exome sequencing (WES) of 2028 non-obstructive azoospermia (NOA) patients identify two deleterious SMC1A variants and one deleterious HMGA2 variant. SMC1A and HMGA2 expression levels were remarkably decreased in testicular tissues from NOA patients with spermatogenic failure compared to obstructive azoospermia (OA) patients with normal spermatogenesis. Collectively, our findings highlight the critical roles and a novel mechanism by the SMC1A/YBX1/HMGA2 axis in regulating human SSC fate decisions and male fertility via an m5C modification. This study is thus of the utmost significance because it would provide novel molecular mechanisms underlying fate determinations of adult stem cells and human spermatogenesis and it could offer network targets for gene targeting male infertility and other diseases.

2026-09-14

Cell Death and Differentiation (published)

ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs

Yizhan Li

Jianxin You

Mengyang Xiong

Yinhuan Chen

Zicheng Zhao

Dekun Wu

Dongqing Zhang

Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence … (see more)and a hard requirement for deploying multimodal large language models (MLLMs) as the decision coreof household robots. Existing evaluations, however, probe intuitive physics passively through question answering over videos, or target deliberate, long-horizon tasks such as navigation and rearrangement; none measure whether a model can turn physical understanding into immediate, safety-critical action. We introduce ReactHuman, the first physics-grounded benchmark for human-like reactive decision-making, in which the evaluated MLLM acts as the brain of a simulated humanoid facing sudden household hazards; it spans 17 event families and over 1,000 bit-for-bit reproducible scenes with exact, annotation-free ground truth derived from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics (a foam anvil, a steel apple). We further design a five-metric suite that scores each reaction along three axes: reasonable, safe, and physically grounded. We physically execute every committed plan so that decisions have observable consequences. With this harness we evaluate seven representative MLLMs. Results show that reactive safety is far from solved: models mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale even when the chosen action is correct; none of these failures shrink with model scale. ReactHuman thus offers both a fine-grained diagnosis and a scalable training signal toward physically grounded, safety-aware embodied agents. The benchmark can be found here: https://huggingface.co/datasets/Alan123/reacthuman-benchmark-scaled

2026-09-08

arXiv (preprint)

One Policy Is Enough: Single-Agent Reinforcement Learning Outperforms Tree Search for Chemistry Tool Learning

Armin Dariani

Sifan Wu

Entao Yang

Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must … (see more)reach for external tools. Tool use here is a three-part problem: select the right tool from a large pool, fill it with correctly typed arguments, and chain calls so that each consumes the outputs of the last. CheMatAgent, a previously published system, addresses this with hierarchical evolutionary MCTS: separate policy and execution models searching tool-call trees under two learned critics, one regressed partly onto GPT-assigned scores. We show that a single policy suffices. Our model interleaves reasoning, tool calls, and returns in one left-to-right generation, trained by a supervised warm-up and then outcome-level reinforcement learning against a programmatic reward read directly off the gold call chain, which leaves no learned critic and no judge in the training loop. On ChemToolBench multiple-tool comprehensive chemistry, on both backbones CheMatAgent use, we improve Tool F1 by 5.5% and Return F1 by 9.6% on Qwen-2.5-7B, and by 3.7% and 3.9% on Llama-3.1-8B, compared with their strongest search configuration, at one model invocation per question, against a search whose cost grows with the tree; we also lead answer Pass Rate on Qwen-2.5-7B.

2026-08-30

arXiv (preprint)

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)