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

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...

Ir a la instalación

Repositorio: ADu2021/skillXiv
Última actividad en el origen: 24 de marzo de 2026 a las 19:42
Idioma detectado de SKILL.md: inglés
Estrellas: 6
Forks: 0

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/ADu2021/skillXiv --skill toward-efficient-agents-memory-tool-learning-and

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

Volver a la búsqueda Volver a la instalación

meaningful-kebab-case-name

Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.

2026-03-26

action-quantization-behavior-cloning

Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.

2026-03-26

adaptive-lora-personalized-ranks

Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.

2026-03-26

Ver todos los de este repositorio

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:

Systematic framework or benchmark for agent evaluation and development
Empirical findings on agent performance, efficiency, or capabilities
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.

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

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Problem

Key Approach

When to Use

When NOT to Use

Resources

Problem

Key Approach

When to Use

When NOT to Use

Resources