An exam tests reasoning under a fixed question. A paper report tests your judgment of someone else’s evidence. A project tests your ability to produce reproducible evidence. These are distinct learning tasks, now taught separately while this URL remains an assessment map.
The last CS224R lecture has three parts. It first folds the whole quarter into one toolbox. It then lists seven unsolved problems: domains without verifiable rewards, using prior data, world models, scaling, safety, hallucination and calibration, and evaluating generalist systems. Nearly half the deck is about how to do research: you need both an important problem and a workable plan, you front-load the risk, you consider pivoting early, and research only counts once you share it. Reading it alongside the 244 public 2026 final project reports shows what those principles look like in practice.
The last two lectures of CS231N Spring 2026 have no public slides. The schedule lists L17 only as "World Modeling" with guest lecturer Gordon Wetzstein, and L18 only as "Human-Centered AI." Outside readers get 2025 substitutes: that year's L17 was a different topic, Robot Learning (Yunzhu Li, slides and video), and L18 is a Fei-Fei Li recording with no slides. This post labels each year separately and never presents 2025 content as 2026. The second half covers the final project: 35% of the grade, two tracks (Applications and Models), pixels required, and deliverables of a one-paragraph proposal, three milestone check-ins, a 6–8 page report and a poster.
The Fall 2025 final project in Harvard CS 2881R came in two flavors: extend an existing paper, or start longer-term research with a theory of change. Teams submitted a 5–10 page NeurIPS-style paper plus a poster, graded 65 for the writeup, 15 for code, and 20 for the poster. The projects page lists 19 papers, and about half cluster around persona vectors and chain-of-thought monitoring. The head TA and the Harvard Q-report point to the same problem: the final project started too late, the rubrics came out too late, and feedback was the lowest-rated item in the course.
Fall 2025 was graded 70% assignments + 30% term project. Projects were done in groups of 3–4 and split into Proposal 6%, Progress 6%, Poster 6% and Report 12%, with no GPUs provided. The repo has no project spec, only the syllabus structure, an end-of-term reminder and the W15–W16 recordings. Fall 2026 switches to 75% assignments (4 of them) + a 25% in-person midterm in W14. The schedule drops the presentation weeks, adds a Reasoning/Agent unit, and brings in an AI-TA for grading support and TAICA compute credits. The official materials don't say why.
The first two deliverables of the CS224U final project are a literature review and an experiment protocol. The lit review covers 5, 7, or 9 papers depending on team size, under five suggested sections. The protocol has seven required sections, and its core is a hypothesis you can state. The course supplies a six-step paper-search loop, a rule that AI-assistant output must be quoted, and a worked example: a student's final project that became a Findings of EMNLP paper. The Gradescope format and rubric slides, and past exemplary papers, are behind a login.
CS224U's 'Presenting your research' lecture has four parts: the course-specific rules for the final paper, how to write an NLP paper, how conference submission works, and how to give a talk. Three things matter most. The final paper must include Known project limitations and an Authorship statement. Write as a Shieber-style 'rational reconstruction,' not a chronological tour of your dead ends. At submission, your title largely decides reviewer bidding. The slides, four videos, and projects.md are public; past example papers need a Stanford login.
Lecture 6 completes the Transformer picture with encoders, decoders, and cross-attention, then breaks the final project into formats, assessment, research topics, and data. A viable topic needs one explicit baseline and metric.