Reading Notes

Notes and summaries from reading ML/AI papers (and some blog posts). All credit to the content in the papers and blog posts goes to the original authors.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

ConceptAttention

3 min read · February 10, 2026

2026 · distillation
Recursive Language Models

RLMs

4 min read · February 09, 2026

2026 · distillation
FLUX.2: Analyzing and Enhancing the Latent Space of FLUX – Representation Comparison

Flux 2 technical reports

3 min read · February 09, 2026

2026 · distillation
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

MM-DiTs

8 min read · February 08, 2026

2026 · distillation
Scalable Diffusion Models with Transformers

Diffusion Transformers

2 min read · February 08, 2026

2026 · distillation