sifr-benchmark 0.1.1

Benchmark for evaluating LLM understanding of web UI: SiFR vs HTML vs AXTree vs Screenshots

sifr-benchmark

How well do AI agents understand web UI?
Benchmark comparing SiFR vs HTML vs AXTree vs Screenshots.

Prerequisites

Element-to-LLM Chrome Extension

To capture web pages in SiFR format, install the Element-to-LLM browser extension:

1. Chrome Web Store: Element-to-LLM
2. Open any webpage
3. Click extension icon → Capture as SiFR
4. Save the .sifr file to examples/ or datasets/formats/sifr/

Without this extension, you can only run benchmarks on pre-captured pages.

Results

Format	Tokens (avg)	Accuracy	Cost/Task
SiFR	2,100	89%	$0.002
Screenshot	4,200	71%	$0.012
AXTree	3,800	52%	$0.004
Raw HTML	8,500	45%	$0.008

→ SiFR: 75% fewer tokens, 2x accuracy vs HTML

What is SiFR?

Structured Interface Format for Representation.
A compact way to describe web UI for LLMs.

btn015:
  type: button
  text: "Add to Cart"
  position: [500, 300, 120, 40]
  state: enabled
  parent: product-card

Full spec: SPEC.md

Installation

pip install sifr-benchmark

Quick Start

1. Capture pages (using Element-to-LLM extension)

1. Install Element-to-LLM extension
2. Open target page (e.g., Amazon product page)
3. Click extension → Export SiFR
4. Save as examples/my_page.sifr

2. Run benchmark

# Set API keys
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...

# Run benchmark
sifr-bench run --models gpt-4o-mini,claude-haiku --formats sifr,html_raw

# Validate your SiFR files
sifr-bench validate examples/

# View info
sifr-bench info

Repository Structure

├── spec/
│   └── SPEC.md              # SiFR format specification
├── benchmark/
│   ├── protocol.md          # Test methodology
│   ├── tasks.json           # 25 standardized tasks
│   └── ground-truth/        # Verified answers per page
├── datasets/
│   ├── pages/               # Test page snapshots
│   │   ├── ecommerce/
│   │   ├── news/
│   │   ├── saas/
│   │   └── forms/
│   └── formats/             # Same page in each format
│       ├── sifr/
│       ├── html/
│       ├── axtree/
│       └── screenshots/
├── results/
│   ├── raw/                 # Model responses
│   └── analysis/            # Processed results
├── src/
│   └── runner.js            # Benchmark execution
└── examples/
    └── product_page.sifr    # Sample SiFR file

Tested Models

• GPT-4o (OpenAI)
• Claude 3.5 Sonnet (Anthropic)
• Gemini 2.0 Flash (Google)
• Llama 3.3 70B (Meta)
• Qwen 2.5 72B (Alibaba)

Key Findings

1. Token efficiency: SiFR uses 70-80% fewer tokens than raw HTML
2. Accuracy: Pre-computed salience improves task accuracy by 40%+
3. Consistency: SiFR results have 3x lower variance across models
4. Edge-ready: SiFR enables UI tasks on 3B parameter models

Contribute

• Add test pages: datasets/pages/
• Add tasks: benchmark/tasks.json
• Run on new models: src/runner.js

Citation

@misc{sifr2024,
  title={SiFR: Structured Interface Format for AI Agents},
  author={SiFR Contributors},
  year={2024},
  url={https://github.com/user/sifr-benchmark}
}

License

MIT — format is open.

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SiFR Spec | Extension | Discord