learningcircuit--local-deep-research/examples/benchmarks

Benchmarks for Local Deep Research

This directory contains scripts for running benchmarks to evaluate Local Deep Research's performance.

Available Benchmarks

SimpleQA

The SimpleQA benchmark evaluates factual question answering capabilities.

python run_simpleqa.py --examples 10 --iterations 3 --questions 3

Options:

• --examples: Number of examples to run (default: 10)
• --iterations: Number of search iterations (default: 3)
• --questions: Questions per iteration (default: 3)
• --search-tool: Search tool to use (default: "searxng")
• --output-dir: Directory to save results (default: "benchmark_results")
• --no-eval: Skip evaluation
• --human-eval: Use human evaluation
• --eval-model: Model to use for evaluation
• --eval-provider: Provider to use for evaluation

BrowseComp

The BrowseComp benchmark evaluates web browsing comprehension and complex question answering.

python run_browsecomp.py --examples 5 --iterations 3 --questions 3

Options:

• --examples: Number of examples to run (default: 2)
• --iterations: Number of search iterations (default: 1)
• --questions: Questions per iteration (default: 1)
• --search-tool: Search tool to use (default: "searxng")
• --output-dir: Directory to save results (default: "browsecomp_results")

See browsecomp_benchmark_readme.md for more information on how BrowseComp works.

Running All Benchmarks

To run both benchmarks and compare results:

# Run SimpleQA with default settings
python run_simpleqa.py

# Run BrowseComp with increased iterations and questions
python run_browsecomp.py --iterations 3 --questions 3

Evaluating Results

Results are saved in the specified output directories and include:

• Raw results (JSONL format)
• Evaluation results (JSONL format)
• Summary reports (Markdown format)

The scripts will also print a summary of the results to the console, including accuracy metrics.