科學課程的AI代理

AI Agents for Science Curriculum

The following curriculum outlines topics to be covered and readings, and provides the slides presented in class (minus purely administrative material).

Several guest lectures are not included.

Lecture 1: What is an agent?

Introduces AI agents and and the sense-plan-act-learn loop. Motivates scientific Discovery Platforms (SDPs): AI-native systems that connect reasoning models with scientific resources.

Slides: Lecture 1 slides.

Readings:

Lecture 2: Frontiers of Language Models

Surveys frontier reasoning models: general-purpose LLMs (GPT, Claude), domain-specific foundation models (materials, bio, weather), and hybrids. Covers techniques for eliciting better reasoning: prompting, chain-of-thought, retrieval-augmented generation (RAG), fine-tuning, and tool-augmented reasoning.

Slides: Lecture 2 slides.

Readings:

Lecture 3: Systems for Agents

Discusses architectures and frameworks for building multi-agent systems, with emphasis on inter-agent communication, orchestration, and lifecycle management.

Slides: Lecture 3 slides.

Readings:

Lecture 4: Retrieval Augmented Generation (RAG) and Vector Databases

Covers how to augment reasoning models with external knowledge bases, vector search, and hybrid retrieval methods.

Slides: Lecture 4 slides.

Readings:

Lecture 5: Tool Calling

Introduces methods for invoking external tools from reasoning models. Focus on model context protocol (MCP), schema design, and execution management.

Slides: Lecture 5 slides.

Readings:

Lecture 6: HPC Systems and Self Driving Labs

How SDPs connect to HPC workflows and experimental labs. Covers distributed coordination, robotics, and federated agents.

Slides: Lecture 6 slides.

Readings:

Lecture 7: Human–AI Workflows

Explores how scientists and agents collaborate: trust boundaries, interaction design, and debugging.

Slides: Lecture 7 slides.

Readings:

Lecture 8: AI co-scientists for accelerating scientific discovery

Guest lecture by Dr. Arvind Ramanathan.

Slides: Lecture 8 slides.

Readings:

Lecture 9: Human–AI Workflows, continued

Further discussion of how scientists and agents collaborate

Slides: Lecture 9 slides.

Lecture 10: Benchmarking and Evaluation

Frameworks for assessing agents and SDPs: robustness, validity, and relevance.

Slides: Lecture 10 slides.

Readings:

Lecture 11: Failures and Safety

Examines why multi-agent systems fail and methods for safety and guardrails.

Slides: Lecture 11 slides.

Readings:

Lecture 12: Novelty and Plagiarism

Explores originality, credit, and the risks of plagiarism in AI-generated science.

Slides: Lecture 12 slides.

Readings:

Assignment A5: Capstone project planning (novel contributions).

Lecture 13: Building Agents and Workflows

Pipelines, workflow composition, and self-improving systems.

Slides: Lecture 13 slides.

Readings:

Lecture 14: Finetuning

Covers approaches to adapt agents with reinforcement learning and real-world training.

Slides: Lecture 14 slides.

Readings: