projects

L3 Lab research areas and projects. Click a project for related publications.

AI and Formal Methods

We develop AI systems that use formal methods to advance mathematics and software, along with tools that make machine-checked reasoning broadly useful.

AI for Formal Mathematics

AI for Formal Mathematics

We aim to build AI systems that can understand, create, and verify mathematics in collaboration with people and proof assistants.

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AI for Verified Software

AI for Verified Software

We aim to build AI systems that produce software with machine-checkable guarantees and make formal verification a routine part of software development.

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AI Tools for Formal Verification

AI Tools for Formal Verification

We build AI-powered tools that make formal verification more accessible, efficient, and useful to mathematicians and programmers.

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Reasoning and Agents

We build models and agents that reason, act through computers, and improve through interaction and feedback.

Computer-Use Agents

Computer-Use Agents

We aim to build general-purpose agents that can reliably carry out any task achievable through a computer interface.

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Reasoning Models

Reasoning Models

We aim to make reasoning models more capable, controllable, and efficient, including through inference-time scaling and efficient inference.

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Continually Improving AI

Continually Improving AI

We aim to build AI systems that learn from feedback, experience, and their own failures to become more capable over time.

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Foundations of Language Models

We study the principles and algorithms underlying how language models learn, generate, and generalize.

Learning and Optimization

Learning and Optimization

We study how objectives, data, and optimization shape what language models learn and how effectively they generalize.

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Inference and Generation

Inference and Generation

We develop algorithms that make language-model inference and generation more capable, efficient, and reliable.

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Evaluation and Generalization

Evaluation and Generalization

We develop rigorous ways to evaluate language models and understand when, how, and why they generalize.

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