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.