2026 focus: Human-LLM Collaboration for Scientific Discovery
Instructor: Edward Y. Chang
Lectures: Mondays & Wednesdays, 4:30 PM – 5:50 PM
Operational format: Monday seminar; Wednesday research clinic, office hours, and team check-ins
Dates: March 30, 2026 – June 3, 2026
This seminar studies how scientists and mathematicians can conduct exploratory research through disciplined human-LLM collaboration, and how to build a platform that makes such collaboration trustworthy, productive, and publishable.
The purpose of this course is to mentor students to build a platform for scientific discovery in human-LLM collaboration settings and to write a paper for submission to NeurIPS. The tentative submission deadline is May 11, 2026, and teams should plan for the paper to be ready and submitted by that date. The course builds directly on ideas covered in CS372, but shifts from conceptual foundations to implementation, integration, evaluation, and research writing.
The main theme is AGI-oriented scientific discovery. We will design and implement a platform that helps scientists and mathematicians search literature, organize memory, generate hypotheses, run controlled exploration, track provenance, validate intermediate results, and produce research artifacts under human supervision. The platform itself is a research contribution. The use of the platform in mathematical or scientific case studies may also become research papers.
The course is motivated in part by recent remarks from Terence Tao, who has argued that AI is becoming a practical research assistant in mathematics and theoretical physics, especially for literature search, coding, calculation, and rapid exploration of candidate ideas, while the human remains responsible for selecting problems, designing workflows, and verifying correctness.
Prerequisite: CS372 is strongly recommended. Students who have not taken CS372 may be admitted by exception with instructor approval, typically only if they are exceptionally strong coders and have substantial OpenClaw experience.
Multi-LLM Collaborative Intelligence (MACI), The Path to AGI Volume 1, ACM Books, Edward Y. Chang.
System-2 Reasoning: From Semantic Anchoring to Causal Intelligence, The Path to AGI Volume 2, Socrasynth, Edward Y. Chang.
The seminar is centered on building a platform for exploratory research in human-LLM collaboration settings, motivated by recent work on mathematical exploration and broader questions about AI-assisted scientific discovery.
Monday: main seminar meeting, lecture, design review, milestone planning, and paper discussion.
Wednesday: research clinic, implementation support, office hours, debugging, and team check-ins.
The schedule below is a draft and will be refined.
| # | Date | Topic | Focus | Milestone |
|---|---|---|---|---|
| 1 | 3/30/2026 | Kickoff Course aims, platform vision, and team formation | Scientific discovery as human-LLM collaboration; course roadmap; paper targets | Team formation begins |
| 2 | 4/6 | Systems #1 Requirements for a scientific discovery platform | User stories, provenance, memory, validation, rollback, trust | 2-page design brief |
| 3 | 4/13 | Systems #2 Search, synthesis, and research memory | Literature workflows, persistent state, versioning, state management | Module ownership and interface spec |
| 4 | 4/20 | Systems #3 Conjecture generation, branching exploration, and verification | Multi-path reasoning, validator roles, computational checks, audit trails | Prototype checkpoint #1 |
| 5 | 4/27 | Paper #1 Human-in-the-loop research workflows | Moderator roles, refusal, escalation, significance judgments, writing plan | Evaluation plan and paper outline |
| 6 | 5/4 | Build #1 Integrated prototype and internal review | End-to-end prototype, first case studies, debugging, interface refinement | Integrated prototype v1 |
| 7 | 5/11 | Paper #2 Writing sprint and launch week | Experiments, figures, system diagrams, submission packaging | Paper ready and submitted by tentative 5/11 deadline |
| 8 | 5/18 | Build #2 Post-submission refinement and usage studies | Additional experiments, failure analysis, case-study extension | Usage-paper or extension draft |
| 9 | 5/25 | Generalization Platform portability across domains | Mathematics, science, causal discovery, long-term roadmap | Final demo preparation |
| 10 | 6/1 | Finale Final presentations, demos, and next steps | Lessons learned, summer continuation plans, release discussion | Final presentation and report |