CPIL / MBZUAI / McGill University / Mila
Advancing trustworthy, efficient, and agentic AI for the cyber-physical world.
CPIL is an academic research lab studying intelligent systems for the cyber-physical world. Led by Prof. Xue (Steve) Liu, the lab brings together researchers across MBZUAI, McGill University, and Mila to advance AI, learning, reasoning, optimization, and intelligent infrastructure.
Overview
Cyber-Physical Intelligence Lab is led by Prof. Xue (Steve) Liu. The lab develops methods and systems for trustworthy, efficient, and agentic intelligence in environments where computation, data, infrastructure, and physical processes interact.
Research spans large models, efficient inference, retrieval-augmented reasoning, representation learning, reinforcement learning, federated learning, optimization, trustworthy evaluation, and AI for infrastructure.
CPIL connects foundational AI research with applications in cyber-physical systems, digital twins, wireless networks, data centers, energy systems, and autonomous control.
Research Areas
Theme 01
Methods and systems for making large language models more efficient, scalable, and deployable, including quantization, pruning recovery, early-exit inference, heterogeneous inference, and KV cache compression.
Theme 02
Benchmarks, training methods, and runtime frameworks for AI agents that retrieve evidence, use tools, receive feedback, and operate across long-horizon workflows.
Theme 03
Robust and transferable representations, stable model alignment, reinforcement learning from human feedback, and data synthesis for knowledge distillation.
Theme 04
Diffusion language models and conditional generative modeling for offline black-box optimization, design problems, and settings where labels or experiments are expensive.
Theme 05
Efficient and trustworthy learning methods for distributed, resource-constrained, and heterogeneous environments, including edge devices and non-IID settings.
Theme 06
AI for intelligent infrastructure, data centers, wireless networks, digital twins, energy systems, autonomous control, and real-world optimization constraints.
Projects
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Members: CPIL
Publications
CPIL
Venue to be updated, 2026
A sample CPIL publication entry showing the fields maintainers and contributors should fill in when adding a real publication.
News
CPIL discusses OpenClaw as a window into the future of agentic AI, highlighting runtime systems, gateways, memory, skills, safety, and task trajectories as core research challenges.
CPIL has 6 papers accepted to ICML 2026, including 1 Spotlight paper. The works cover diffusion models, black-box optimization, retrieval-augmented reasoning, efficient inference, robust representation learning, and federated learning.
CPIL has 9 papers accepted to ICLR 2026, covering large model optimization, efficient inference, knowledge distillation, trustworthy evaluation, reinforcement learning, representation learning, and alignment.
Join CPIL
CPIL welcomes PhD Students, Postdoctoral Researchers, Visiting Scholars, Research Interns, Undergraduate and Master’s Research Students, Academic Collaborators, Industry Collaborators interested in trustworthy, efficient, and agentic AI systems for the cyber-physical world.