From 9e6f87641baca0dc7eb6ea762a0fd8cd52163922 Mon Sep 17 00:00:00 2001
From: rUv <ruv@ruv.net>
Date: Tue, 2 Dec 2025 17:43:33 +0000
Subject: [PATCH] docs: Add brief introductions to attention mechanism sections
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Added one-line descriptions before each table:
- Core: Standard attention for sequence modeling
- Graph: Attention for graph-structured data and GNNs
- Specialized: Task-specific variants for efficiency
- Hyperbolic: Curved space for hierarchies
- Async: High-throughput inference utilities

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
---
 README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c21bb938..fd9db2ce 100644
--- a/README.md
+++ b/README.md
@@ -91,6 +91,8 @@ High-performance attention mechanisms for transformers, graph neural networks, a
 
 #### Core Attention Mechanisms
 
+Standard attention layers for sequence modeling and transformers.
+
 | Mechanism | Complexity | Memory | Best For |
 |-----------|------------|--------|----------|
 | **DotProductAttention** | O(n²) | O(n²) | Basic attention for small-medium sequences |
@@ -102,6 +104,8 @@ High-performance attention mechanisms for transformers, graph neural networks, a
 
 #### Graph Attention Mechanisms
 
+Attention layers designed for graph-structured data and GNNs.
+
 | Mechanism | Complexity | Best For |
 |-----------|------------|----------|
 | **GraphRoPeAttention** | O(n²) | Position-aware graph transformers |
@@ -111,6 +115,8 @@ High-performance attention mechanisms for transformers, graph neural networks, a
 
 #### Specialized Mechanisms
 
+Task-specific attention variants for efficiency and multi-modal learning.
+
 | Mechanism | Type | Best For |
 |-----------|------|----------|
 | **SparseAttention** | Efficiency | Long docs, low-memory inference |
@@ -120,7 +126,7 @@ High-performance attention mechanisms for transformers, graph neural networks, a
 
 #### Hyperbolic Math Functions
 
-Operations for Poincaré ball embeddings (curved space for hierarchies):
+Operations for Poincaré ball embeddings—curved space that naturally represents hierarchies.
 
 | Function | Description | Use Case |
 |----------|-------------|----------|
@@ -132,6 +138,8 @@ Operations for Poincaré ball embeddings (curved space for hierarchies):
 
 #### Async & Batch Operations
 
+Utilities for high-throughput inference and training optimization.
+
 | Operation | Description | Performance |
 |-----------|-------------|-------------|
 | `asyncBatchCompute()` | Process batches in parallel | 3-5x faster |