toward-efficient-agents-memory-tool-learning-and

name	toward-efficient-agents-memory-tool-learning-and
title	Toward Efficient Agents: Memory, Tool Learning, and Planning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.14192
keywords	["Agent","Learning","Planning","Memory","Tool"]
description	Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, which is crucial for real-world deployment, has often been overlooked. This paper therefore investigates efficiency from three core components of agents: memory, tool learning, and planning, considering costs such as latency, tokens, steps, etc. Aimed at conducting comprehensive research addressing the efficiency of the a...

Problem

Toward Efficient Agents addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

Key Approach

The paper introduces a novel framework, methodology, or benchmark for toward efficient agents. The core contributions include:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. Generalizable principles applicable across domains

When to Use

Use this skill when you need to:

• Evaluate or benchmark autonomous agent systems
• Understand best practices in agent design and evaluation
• Learn empirical results on agent performance
• Improve agent efficiency, reasoning, or capabilities

When NOT to Use

• For non-agent-related tasks
• When seeking quick implementation code (see the paper for details)
• For general knowledge unrelated to autonomous agents

Resources

• ArXiv Abstract: https://arxiv.org/abs/2601.14192
• Full PDF: https://arxiv.org/pdf/2601.14192
• HTML Version: https://arxiv.org/html/2601.14192

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.