# GeckOpt: LLM System Efficiency via Intent-Based Tool Selection

arXiv: https://arxiv.org/abs/2404.15804 · HTML: https://mlsanity.com/paper/2404.15804/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024)
- [Evaluating Tool-Augmented Agents in Remote Sensing Platforms](https://mlsanity.com/paper/2405.00709/) (2024)
- [Stable Diffusion For Aerial Object Detection](https://mlsanity.com/paper/2311.12345/) (2023)

## Built on by (most cited first, 12 of 12)

- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [An LLM-Tool Compiler for Fused Parallel Function Calling](https://mlsanity.com/paper/2405.17438/) (2024)
- [Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks](https://mlsanity.com/paper/2405.18831/) (2024)
- [LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching](https://mlsanity.com/paper/2406.06799/) (2024)
- [GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining](https://mlsanity.com/paper/2511.00457/) (2025)
- [Unlearning Climate Misinformation in Large Language Models](https://mlsanity.com/paper/2405.19563/) (2024)
- [MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models](https://mlsanity.com/paper/2507.00487/) (2025)
- [Automated Multi-Agent Workflows for RTL Design](https://mlsanity.com/paper/2509.20182/) (2025)
- [Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees](https://mlsanity.com/paper/2608.19993/) (2026)

## Abstract (arXiv)

In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By identifying the intent behind user prompts at runtime, we narrow down the API toolset required for task execution, reducing token consumption by up to 24.6%. Early results on a real-world, massively parallel Copilot platform with over 100 GPT-4-Turbo nodes show cost reductions and potential towards improving LLM-based system efficiency.