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

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

System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts

Xiaoqiang Wang

Yun Zhu

Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move beyond fast System-1 responses and engage in deliberative Syst… (see more)em-2 reasoning. However, this comes at the cost of significant inefficiency due to verbose intermediate output. Recent latent-space reasoning methods improve efficiency by operating on hidden states without decoding into language, yet they treat all steps uniformly, failing to distinguish critical deductions from auxiliary steps and resulting in suboptimal use of computational resources. In this paper, we propose System-1.5 Reasoning, an adaptive reasoning framework that dynamically allocates computation across reasoning steps through shortcut paths in latent space. Specifically, System-1.5 Reasoning introduces two types of dynamic shortcuts. The model depth shortcut (DS) adaptively reasons along the vertical depth by early exiting non-critical tokens through lightweight adapter branches, while allowing critical tokens to continue through deeper Transformer layers. The step shortcut (SS) reuses hidden states across the decoding steps to skip trivial steps and reason horizontally in latent space. Training System-1.5 Reasoning involves a two-stage self-distillation process: first distilling natural language CoT into latent-space continuous thought, and then distilling full-path System-2 latent reasoning into adaptive shortcut paths (System-1.5 Reasoning). Experiments on reasoning tasks demonstrate the superior performance of our method. For example, on GSM8K, System-1.5 Reasoning achieves reasoning performance comparable to traditional CoT fine-tuning methods while accelerating inference by over 20x and reducing token generation by 92.31% on average.

2025-12-02

Conference on Neural Information Processing Systems (Accept (poster))

Accelerated Inorganic Materials Design with Generative Al Agents

Izumi Takahara

Teruyasu Mizoguchi

Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computati… (see more)onal methods often struggle to efficiently explore desired targets with sufficient interpretability. Here, we present MatAgent, a generative approach for inorganic materials discovery that harnesses the powerful reasoning capabilities of large language models (LLMs). By combining a diffusion-based generative model for crystal structure estimation with a predictive model for property evaluation, MatAgent uses iterative, feedback-driven guidance to steer material exploration precisely toward user-defined targets. Integrated with external cognitive tools-including short-term memory, long-term memory, the periodic table, and a comprehensive materials knowledge base-MatAgent emulates human expert reasoning to vastly expand the accessible compositional space. Our results demonstrate that MatAgent robustly directs exploration toward desired properties while consistently achieving high compositional validity, uniqueness, and material novelty. This framework thus provides a highly interpretable, practical, and versatile AI-driven solution to accelerate the discovery and design of next-generation inorganic materials.

2025-11-30

Cell Reports Physical Science (published)

arxiv.org

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping

Christopher Pal

2025-10-02

ArXiv (preprint)

arxiv.org

Accelerated Inorganic Materials Design with Generative AI Agents

Izumi Takahara

Teruyasu Mizoguchi

2025-09-19

NeurIPS.cc/2025/Workshop/AI4Mat (poster)

Concept-based Steering of Large Language Models for Conditional Molecular Generation

Yang Zhang

Modern LLMs, with their internet-scale pretraining and advanced human-level capabilities across specialized tasks, have demonstrated promisi… (see more)ng performance in molecular discovery using existing text-based molecular representations, such as SMILES and SELFIES. However, generating valid, unique, and high-fidelity molecules while precisely controlling for multiple properties simultaneously remains challenging. While prior works demonstrated success by fine-tuning language models on a novel corpus of molecules with property-conditioned tags, real-world applications require generating molecules from diverse property distributions, previously unseen in the training data. To this end, we present Concept-based Activation STeering (CAST), the first approach to apply activation steering to directly edit a model's internal representation for conditional molecular generation. CAST offers a lightweight, flexible alternative to fine-tuning by computing property-conditioned steering vectors via a concept network that does not require retraining the LLM. Through extensive experiments on datasets such as Therapeutics Data Commons, we show that CAST consistently outperforms existing methods on both in-distribution and out-of-distribution conditional generation tasks. We also conduct comprehensive ablation studies to highlight the extent of control our concept-guided steering provides on the molecules generated by the LLM.

2025-09-19

NeurIPS.cc/2025/Workshop/AI4Mat (poster)

R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression.

Xiaoqiang Wang 0007

Yun Zhu

2025-08-26

DBLP.org (unknown)

LLMs for Experiment Design in Scientific Domains: Are We There Yet?

Rushil Gupta

Jason Hartford

2025-06-10

ICML.cc/2025/Workshop/GenBio (poster)

Improving Context Fidelity via Native Retrieval-Augmented Reasoning

Jinlin Wang

Xinyu Wang

Shiqi Li

Xiangru Tang

Sirui Hong

Xiao-Wen Chang

Chenglin Wu

Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on prov… (see more)ided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires minimal labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.

2025-06-08

ICML.cc/2025/Workshop/LCFM (accepted)

MatExpert: Decomposing Materials Discovery by Mimicking Human Experts

Qianggang Ding

Santiago Miret

Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a no… (see more)vel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text

Ge Zhang

Sai Rajeswar

We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (see more)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection

Yun Zhu

Jia-Chen Gu

Caitlin Sikora

Ho Ko

Yinxiao Liu

Chu-Cheng Lin

Lei Shu

Liangchen Luo

Lei Meng

Jindong Chen

Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external context… (see more)s. However, the input length grows linearly in the number of retrieved documents, causing a dramatic increase in latency. In this paper, we propose a novel paradigm named Sparse RAG, which seeks to cut computation costs through sparsity. Speciﬁcally, Sparse RAG encodes retrieved documents in parallel, which eliminates latency introduced by long-range attention of retrieved documents. Then, LLMs selectively decode the output by only attending to highly relevant caches auto-regressively, which are chosen via prompting LLMs with special control tokens. It is notable that Sparse RAG combines the assessment of each individual document and the generation of the response into a single process. The designed sparse mechanism in a RAG system can facilitate the reduction of the number of documents loaded during decoding for accelerating the inference of the RAG system. Additionally, ﬁltering out undesirable contexts enhances the model’s focus on relevant context, inherently improving its generation quality. Evaluation results on four datasets show that Sparse RAG can be used to strike an optimal balance between generation quality and computational efﬁciency, demonstrating its generalizability across tasks.

2025-01-21

ICLR.cc/2025/Conference (poster)

AFlow: Automating Agentic Workflow Generation

Jiayi Zhang

Jinyu Xiang

Zhaoyang Yu

Fengwei Teng

Xiong-Hui Chen

Jiaqi Chen

Mingchen Zhuge

Xin Cheng

Sirui Hong

Jinlin Wang

Bingnan Zheng

Yuyu Luo

Chenglin Wu

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing … (see more)agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. We introduce AFLOW, an automated framework that efficiently explores this space using Monte Carlo Tree Search, iteratively refining workflows through code modification, tree-structured experience, and execution feedback. Empirical evaluations across six benchmark datasets demonstrate AFLOW's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines. Furthermore, AFLOW enables smaller models to outperform GPT-4o on specific tasks at 4.55% of its inference cost in dollars. The code is available at https://github.com/geekan/MetaGPT.

2025-01-21

ICLR.cc/2025/Conference (oral)