Syllabus & policies

Course information

Assessment

Attendance and participation

This is a discussion-driven course; attendance and active participation are graded. Up to two unexcused absences are allowed. Additional absences require an approved reason — illness, family emergency, conference presentation — with advance notice when the conflict is foreseeable.

Participation means engaging with the reading in class: asking questions, taking a position on the paper’s claims, and responding to your classmates. Quantity of speech is not the metric; substance is.

Paper summaries

There are at most two readings per week. For each assigned reading you submit a short summary:

• Length: fewer than 500 words.
• Due: 12:30PM Saudi time, the day of class.
• Late: accepted until class starts at −50%. Not accepted once class has begun, except with KAUST-approved accommodations or an excused absence.

A good summary states what the paper claims, what evidence it offers, and where you think it is weak. It is not an abstract-rewrite.

Summaries must be written individually and without generative AI. Grammar and spell-checking tools are fine.

Paper presentations

Each student presents in one or two classes.

• Slides due: 24 hours before the class you are presenting.
• Late slides: shared less than 24 hours before class, −25%; not shared before class starts, −50%.
• Swaps: arrange a swap or notify the instructor at least 72 hours in advance. Documented emergencies will be accommodated.
• You may use publicly available material — including the authors’ own slides — with proper attribution.

A presentation must situate the paper in its literature, explain the method precisely enough that the room could critique it, and end with discussion questions rather than a summary slide.

Assignments

There are three short implementation assignments. Late work incurs a 10% penalty per 24 hours (or fraction thereof) up to 72 hours, after which a zero is recorded without an approved extension.

Research project

The project is 50% of the grade and runs the whole term. Teams of up to three. It is graded in three milestones: proposal 10%, midterm report and presentation 15% (report 10, presentation 5), and final report and presentation 25% (report 15, presentation 10). Details and deliverables are on the Assignments & project page.

Academic integrity, collaboration and AI use

• Paper summaries: individual work, no generative AI (spell-check is fine).

• Presentations: public materials permitted with proper attribution.

• Project: teams of up to three. Conceptual discussion across teams is encouraged; code sharing across teams is not permitted. External libraries and datasets are allowed where license-compatible.

• AI coding assistants are allowed on the project, and using them well is a real skill. What is required is disclosure: name the models and tools you used, include representative prompts, and say where in the work they were applied. You remain responsible for the correctness, security and originality of everything you submit — a tool’s mistake becomes your mistake the moment you hand it in.

  This is asked of you because disclosure is becoming the standard expectation in computing research, not a rule peculiar to this course. ACM’s Policy on Authorship already holds that generative AI tools may not be listed as authors of a published work, and that their use to create content “is permitted but must be fully disclosed in the Work.” Building the habit now is practice for submitting your work anywhere.

• Fabricated results are a violation, full stop.

• All work must comply with KAUST research ethics and data-governance policy — including de-identification and IRB approval where required. Violations are handled per KAUST policy.

Learning outcomes

By the end of the course, successful students will be able to:

1. Conduct independent research in LLM systems, applications or safety by formulating novel hypotheses, designing rigorous experiments, and presenting findings through technical reports and peer-review-quality presentations.
2. Critically analyze and synthesize state-of-the-art LLM research by evaluating methodologies, identifying limitations, and proposing meaningful extensions.
3. Design and implement scalable multi-agent LLM systems that coordinate specialized agents to solve complex, multi-step problems with dynamic task allocation and external tool integration.
4. Evaluate and mitigate safety risks in LLM applications by detecting hallucinations, bias and adversarial behavior, and implementing response mechanisms suitable for deployment in sensitive domains.
5. Design and optimize LLM inference systems using serving architecture design, caching strategies and distributed computing to meet real-world performance demands.
6. Build and evaluate RAG systems integrating vector databases, embedding models and structured knowledge sources for factual consistency and source attribution.
7. Adapt LLMs to domain-specific applications in healthcare, education or scientific research, addressing ethical concerns, regulatory standards and domain-expertise integration.

Changes

This page is the syllabus of record and is kept current through the term. The instructor reserves the right to make changes as necessary; material changes will be announced in class and reflected here.