finally-outshining-the-random-baseline-a-simple

name	finally-outshining-the-random-baseline-a-simple
title	Finally Outshining the Random Baseline: A Simple and Effective Solution
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.13677
keywords	["Agents","Benchmarking"]
description	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...

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.