# Finally Outshining The Random Baseline A Simple

> Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive Entropy (ClaSP PE), a simple and effective query strategy that addresses two key lim...

- Skill: `adu2021/finally-outshining-the-random-baseline-a-simple` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/finally-outshining-the-random-baseline-a-simple`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/finally-outshining-the-random-baseline-a-simple/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/finally-outshining-the-random-baseline-a-simple

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## Overview

This skill covers research on finally outshining the random baseline: a simple and effective solution. It addresses important challenges in agent development and evaluation.

## Key Insights

The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners

## When to Use

Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement

## When NOT to Use

- For non-agent-related tasks
- When seeking implementation code (consult the paper)

## Resources

- ArXiv Abstract: https://arxiv.org/abs/2601.13677
- Full PDF: https://arxiv.org/pdf/2601.13677
- HTML: https://arxiv.org/html/2601.13677

Refer to the original paper for complete technical details, methodology, and experimental protocols.