docs(website): highlight benchmarks

README.md

@@ -17,7 +17,7 @@ The similarity to CSV is intentional: CSV is simple and ubiquitous, and TOON aim
 Think of it as a translation layer: use JSON programmatically, and encode it as TOON for LLM input.
 
 > [!TIP]
-> TOON is production-ready, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the [spec](https://github.com/toon-format/spec) or sharing feedback.
+> The TOON format is stable, but also an idea in progress. Nothing's set in stone – help shape where it goes by contributing to the [spec](https://github.com/toon-format/spec) or sharing feedback.
 
 ## Table of Contents
 
@@ -244,7 +244,8 @@ grok-4-fast-non-reasoning
   CSV            ██████████░░░░░░░░░░    52.3% (57/109)
 ```
 
-**Key tradeoff:** TOON achieves **73.9% accuracy** (vs JSON's 69.7%) while using **39.6% fewer tokens** on these datasets.
+> [!TIP] Results Summary
+> TOON achieves **73.9% accuracy** (vs JSON's 69.7%) while using **39.6% fewer tokens** on these datasets.
 
 <details>
 <summary><strong>Performance by dataset, model, and question type</strong></summary>
@@ -427,9 +428,6 @@ grok-4-fast-non-reasoning
 
 </details>
 
-<details>
-<summary><strong>How the benchmark works</strong></summary>
-
 #### What's Being Measured
 
 This benchmark tests **LLM comprehension and data retrieval accuracy** across different input formats. Each LLM receives formatted data and must answer questions about it. This does **not** test the model's ability to generate TOON output – only to read and understand it.
@@ -448,7 +446,7 @@ Eleven datasets designed to test different structural patterns and validation ca
 
 **Structural validation datasets:**
 7. **Control**: Valid complete dataset (baseline for validation)
-8. **Truncated**: Array with 3 rows removed from end (tests [N] length detection)
+8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
 9. **Extra rows**: Array with 3 additional rows beyond declared length
 10. **Width mismatch**: Inconsistent field count (missing salary in row 10)
 11. **Missing fields**: Systematic field omissions (no email in multiple rows)
@@ -471,14 +469,14 @@ Eleven datasets designed to test different structural patterns and validation ca
   - Example: "How many employees in Sales have salary > 80000?" → `5`
   - Example: "How many active employees have more than 10 years of experience?" → `8`
 
-- **Structure awareness (12%)**: Tests format-native structural affordances (TOON's [N] count and {fields}, CSV's header row)
+- **Structure awareness (12%)**: Tests format-native structural affordances (TOON's `[N]` count and `{fields}`, CSV's header row)
   - Example: "How many employees are in the dataset?" → `100`
   - Example: "List the field names for employees" → `id, name, email, department, salary, yearsExperience, active`
   - Example: "What is the department of the last employee?" → `Sales`
 
 - **Structural validation (2%)**: Tests ability to detect incomplete, truncated, or corrupted data using structural metadata
   - Example: "Is this data complete and valid?" → `YES` (control dataset) or `NO` (corrupted datasets)
-  - Tests TOON's [N] length validation and {fields} consistency checking
+  - Tests TOON's `[N]` length validation and `{fields}` consistency checking
   - Demonstrates CSV's lack of structural validation capabilities
 
 #### Evaluation Process
@@ -494,8 +492,6 @@ Eleven datasets designed to test different structural patterns and validation ca
 - **Temperature**: Not set (models use their defaults)
 - **Total evaluations**: 209 questions × 6 formats × 4 models = 5,016 LLM calls
 
-</details>
-
 <!-- /automd -->
 
 ### Token Efficiency