In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets. We begin by downloading the dataset snapshot from Hugging Face, parsing the released task specifications, and examining the benchmark taxonomy, execution settings, internet requirements, judging logic, and scoring metadata. We then extract the leaderboard data directly from the repository README, standardize model names, reshape task-level results into an analysis-ready format, and compare performance across multiple time budgets. Finally, we fit log-sigmoid scaling curves, measure category-level score improvements, inspect the tasks with the largest gains, and study how SForge rescale functions transform raw evaluation outputs into normalized benchmark scores.

!pip -q install "huggingface_hub>=0.23" pandas numpy scipy matplotlib pyyaml

import os, glob, json, textwrap, warnings

import numpy as np, pandas as pd

import matplotlib.pyplot as plt