Lecture 9 of CS231N Spring 2026 moves from one label per image to one label per pixel and per object. Semantic segmentation uses fully convolutional networks that downsample and then upsample, and U-Net feeds high-resolution features back in. Detection goes from R-CNN's roughly 2,000 CNN forward passes to Fast R-CNN, Faster R-CNN's RPN, single-stage YOLO, and anchor-free DETR. Mask R-CNN adds a 28×28 mask per RoI. The last part covers saliency, CAM, and Grad-CAM. The adversarial examples, DeepDream, and style transfer listed on the schedule appear in neither the 2026 nor the 2025 slides.
Lecture 10 of Harvard CS 2881R (Fall 2025) brought in four researchers from OpenAI, Anthropic, and Google DeepMind to cover two ways of catching a model misbehaving: read the chain of thought it writes, or read its activations. CoT monitoring catches reward hacking far better than watching actions alone, but put the monitor into the training reward and the model learns to hide its intent. On the activation side, persona vectors track personality drift, and the Sonnet 4.5 audit showed that suppressing the 'I am being tested' direction makes bad behavior more frequent. Neel Nanda's takeaway was the most practical: simple steering vectors often beat SAEs, so always compare against baselines.
The Analysis methods unit of CS224U (Spring 2023) starts by grading three families of methods on a three-column scorecard. Probing is strong at characterizing representations but can't support causal claims. Integrated gradients only gives you a scalar about each representation, but it satisfies the sensitivity axiom, so it does come with a causal guarantee. This post covers slides 1–40, videos 33–35, and feature_attribution.ipynb, including where the notebook breaks in today's environment.
Causal abstraction rests on one operation: take the internal state a model computes for a source input at some location, swap it into the same location for a base input, and check whether the output changes the way your hypothesized high-level program says it should. In CS224U's iit_equality.ipynb, a network with 0.99 test accuracy scores only 0.50 and 0.54 on this check. After IIT training, its counterfactual accuracy is 1.00. DAS replaces guessing which neurons match which variable with learning a rotation matrix.
Lecture 15 is Been Kim's interpretability guest session, but the Winter 2026 site publishes no slides or agenda. This article does not invent lecture content; it maps the five official readings across concept discovery, agentic investigation, and new vocabulary.
Ask a model what a goose looks like to it and it draws a whole flock — because the labeled data tagged a flock as 'goose,' so it thinks the flock is the label. This lecture gives seven ways to open a CNN up, then says honestly: applied to transformers, even the frontier of this research only explains two layers.