---
title: Purpose
tags: [meta]
status: active
confidence: high
created: 2026-05-09
sources: []
orphan: false
aliases: []
---

# Purpose

This wiki tracks the state of AI/ML research — foundational architectures, training methods,
benchmark results, and the researchers driving the field.

## In scope

- Neural network architectures: transformers, attention mechanisms, MoE, SSMs
- Training techniques: pre-training, instruction tuning, RLHF, DPO, synthetic data
- Benchmark results and evaluation methodology
- Key researchers and their contributions
- Landmark papers and their core claims
- Inference optimization: quantization, speculative decoding, KV cache
- Educational lectures, tutorials, and talks on in-scope topics — including YouTube
  videos and conference presentations from established AI/ML researchers

## Out of scope

- Business news, funding rounds, and corporate strategy
- General software engineering not specific to AI/ML
- Hardware specifications unless directly relevant to model training or inference
- Non-peer-reviewed opinion pieces without original research claims
