A two-hour lecture is about 720,000 tokens, which Gemini's API accepts and Claude's and ChatGPT's API docs don't list video input for. On code, Claude and GPT are 0.8 points apart on the Vals board, so compare time and cost instead. Grok for X sentiment and auto-fill checkout, ChatGPT for ready-made apps and voice.
Nemotron covers text reasoning, multimodal understanding, and safety classification. Ultra, Super, Lightning, Nano Omni, and Content Safety differ in architecture, context, and licensing; model specifications and API limits must be checked separately.
Audio representation, temporal relationships in video, and action-conditioned world transitions are different learning tasks. The earlier combined guide checked only audio at the end. This URL now serves as a reading map to one full guide each for Lectures 24–26; the Lecture 26 guide also covers the separate alignment deck.
Cross-attention reads another representation, DiT is a diffusion-backbone choice, and Q-Former uses learned queries to extract or bridge information. They can coexist rather than being three competing products. The Lecture 14 lesson handles Prompt-to-Prompt and classifier-free guidance in detail.
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
“Text plus images” does not specify the task. Text-to-image generation produces an image conditioned on text; a VLM may consume an image and produce an answer. Direction, output objective, and evaluation differ even when both use Transformers.
CS231N Lecture 10 treats video as 2D plus time, a T×3×H×W tensor, and follows one thread: efficiency. Train on short clips and average several clips at test time. Architectures run from per-frame 2D CNNs and late fusion to 3D CNNs, then two-stream networks that isolate motion with optical flow, and I3D, which inflates 2D weights into 3D. After 2021 the field moved to Transformers, where token counts explode, which led to divided space-time attention, Video Swin, MViT, and tubelets. The last part covers temporal localization, audio-visual models, VideoLLMs, and long-form video, where HourVideo shows how far the field still has to go.
The CS231N Spring 2026 vision-and-language lecture replaces the "one model per task" approach of the first half of the course with foundation models: pre-train one model on a large, diverse dataset, then adapt it to many tasks through fine-tuning, zero-shot, or few-shot use. Three threads carry the lecture. First, CLIP: contrastive learning in both directions over 400 million image-text pairs scraped from the web, then writing class names as sentences to classify without any fine-tuning; it also has weak spots, such as failing to tell "a mug in some grass" from "some grass in a mug". Second, vision-language models from LLaVA and Flamingo to Qwen3-VL and Molmo, which feed image features into an LLM so it can look at an image and output text. Third, chaining: letting an LLM write descriptions or programs that string existing vision models together.
Lecture 14 has three parts. Fine-tuning: SFT runs next-token prediction on desired answers, RLHF trains a reward model and then fine-tunes with KL-penalized RL, and DPO collapses both stages into one supervised step. Then comes a chain of PEFT methods: BitFit tunes only biases, Adapters add small layers but slow inference, Prompt/Prefix-Tuning eat input length, LoRA fixes latency with a low-rank branch you can merge back, QLoRA stores the backbone in NF4, and BitDelta compresses the fine-tune delta to 1 bit. Multimodal LLMs: Flamingo uses cross-attention, PaLM-E and VILA feed images in as tokens, and VILA-U can also output images. Prompt engineering: zero/few-shot, CoT, and RAG.
A lot of data does not mean a lot of annotation. This lecture asks: with no annotations, how do you learn useful representations from data? The shared idea is to find the latent factors behind the observations. An auto-encoder compresses the input into a short code and reconstructs it. A VAE additionally requires the code to follow a Gaussian distribution, so it can generate by sampling from the distribution. Dual learning lets two tasks that run in opposite directions serve as each other's feedback. Self-supervised learning divides into self-prediction (hide a part and guess it back) and contrastive learning (pull similar ones together, push dissimilar ones apart). CLIP does contrastive learning on image-text pairs, so image classification needs no separate training; DALL·E 2 then uses its representations to generate images. The lecture ends with multimodal models: projecting images into the language model's space, and streaming interaction that listens while deciding when to speak.
HW10 is 12 multiple-choice questions answered only on NTU COOL. Section 1 compares three spoken language model architectures: Cascade (ASR → LLM → TTS, with text in the middle), End-to-End (a language model over discrete speech tokens), and Thinker-Talker (an LLM thinks, a separate decoder speaks). In the Colab, two models listen to three clips and guess the speaker's gender, and you work out which one is the cascade. Section 2 takes Mimi apart: tokenize an emotion corpus into 32 RVQ layers, plot UMAP for layers 0, 6, 16, and 31, then encode and decode speech, laughter, and music to hear what breaks. The rest are paper questions on TWIST, AudioLM, LLaMA-Omni 2, Moshi, and GLM-4-Voice, covering initialization, pretraining, interleaving, and realtime/full-duplex behavior. The Colab needs Llama-3.2-3B-Instruct access and an HF token. Questions and Colab are public; outside readers miss only the COOL grading and answers.
All deep research agents are 'text-first'—but the real world isn't just text. WebWatcher (NeurIPS 2025) is the first system to integrate visual reasoning into deep research, using OCR, image search, code execution, and other tools to handle charts, screenshots, videos, and other diverse information.
OpenELM uses layer-wise scaling to shift parameters toward layers near the output; with 1.08B parameters and 1.5T tokens it beats OLMo 1.2B (+2.36% on the LLM360 average) despite OLMo training on 3T tokens. MiniCPM trains multimodal small models from scratch with a three-stage unfreezing recipe (Resampler first, vision encoder next, everything unfrozen last); MiniCPM-V 4.5 reaches sub-30B SOTA on VideoMME with only 8B parameters, and 4-bit quantization squeezes fp16's 16–17GB memory footprint down to about 5GB for phones.
Thinking Machines Lab (founded 2025 by Mira Murati, $2B seed at a $12B valuation) released Inkling in July 2026 under Apache 2.0 (975B total / 41B active params, 1M context, native multimodality, controllable thinking effort) plus a smaller Inkling-Small (276B / 12B), paired with the Tinker fine-tuning platform—turning customizability itself into the product.
The defining conference keywords of 2024 were agents, alignment, multimodal LLMs, and inference-time compute. The LLM share at five major conferences doubled again after its sharp 2023 rise; agent-related terms grew 4.3 times; and diffusion models graduated from an emerging topic to a second generative-AI pillar alongside LLMs. Traditional task-oriented NLP continued to contract, while GANs almost disappeared from top venues.
Gemini is Google DeepMind's native multimodal LLM family, famed for a 1M-token context window and native video/speech input plus scientific reasoning. 3.1 Pro tops GPQA Diamond 94.1% and ARC-AGI-2 77.1% to claim science-reasoning dual crowns, at $2/$12—1/6 of Claude. 3.8 Flash delivers agentic coding results close to frontier models at $0.75/$3.75 (Terminal-Bench 2.1 89.4%; DeepSWE v1.1 73.7%), with the same introductory pricing across 3.6, 3.7, and 3.8.
Llama is Meta's open-source LLM family, with the largest enterprise deployment footprint and the most mature ecosystem. Llama 4 Scout (10M context) and Maverick (17B active / 400B total MoE) are the current open multimodal benchmarks, but Meta pivoted to closed-source Muse Spark in April 2026—Llama 4 is likely the last major open Llama, and its license is not truly open (Llama 4 Community License, separate license required above 700M MAU).
Mistral is Europe's most successful AI startup, cutting through the market with a 'smaller, faster, cheaper' strategy and European data-sovereignty positioning. Mistral Large 3 is Europe's strongest commercial LLM, Small 4 is the 24B efficiency king, and Medium 3.5 is the open Modified-MIT model optimized for agentic coding. Its moat is not technical scale but the 'European compliance' card.
In 2026, AI models span seven major categories and more than 20 subcategories. This introduction to the AI Model Families series maps use cases to models and models to families, with current rankings and selection advice for each use case.
ReactGenie annotates React components to expose data, actions, and views, parses composite voice commands into a DSL, and renders native graphical output against shared UI context.
Lecture 17 organizes CLIP/SigLIP, LLaVA, Qwen-VL, and Chameleon into three paths: contrastive encoders learn semantics, vision-encoder/projector/LM stacks provide understanding, and discrete image tokens enable generation. Resolution, token budgets, and modality balance constrain them all.
CS25 is Stanford's 1-unit seminar where attendance is the only homework and anyone can audit. Of the nine talks in the Spring 2026 season, the three worth your time are Albert Gu on the inductive biases of SSMs vs Transformers, Charles Frye on serving inference across thousands of GPUs, and Victoria Lin on what native multimodality still hasn't solved.
Pure image understanding has flattened out — four frontier models all clear 80% on MMMU-Pro within 3 points of each other. The real differentiation is video, long-document OCR, and realtime speech, each with a different leader. But the most useful lesson from assembling these rankings is that two credible sources named different Video-MME leaders more than 10 points apart — and that July and August each turned the field over again.
NCA-GENM matches NCA-GENL on price, length, and level but not on emphasis: Experimentation rises to 25% (the heaviest), Core ML drops from 30% to 20%, and two new areas appear — Multimodal Data 15% and Performance Optimization 10%. The content covers U-Net, CLIP, diffusion models, multimodal loss functions, attention maps, and NVIDIA's Riva / NeMo / Triton / ACE SDKs. The official recommended training was replaced in autumn 2026: it is now one free open-source set of deep learning materials on GitHub plus one multimodal agent course, assigned differently per area, with the heaviest area, Experimentation, getting only the free materials. Official specs: $125, 1 hour, 50–60 items, two-year validity, English only.
Climbing routes carry a ton of visual information (topos, wall photos) that text-only RAG misses entirely. Multimodal RAG makes images searchable and understandable.