Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility

• URL: http://arxiv.org/abs/2607.14108v1
• Date: Thu, 07 May 2026 20:57:17 GMT
• Title: Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility
• Abstract summary: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory.<n>We also introduce marginal tool utility, a new quantitative metric defined per tool call indicating whether a tool is useful or whether it can be safely removed from the tool suite without affecting accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory. To ensure that tool efficiency is well-defined, we also introduce marginal tool utility, a new quantitative metric defined per tool call indicating whether a tool is useful or whether it can be safely removed from the tool suite without affecting accuracy while increasing tool efficiency; in this paper, we determine the sign of marginal tool utility for each tool call in a trajectory using LLM-as-a-Judge. While much prior work has been done to develop techniques that improve tool use by LLMs and design evaluation methods measuring efficiency indirectly using accuracy as a proxy, our work is centered on measuring efficiency directly via the quantitative metric proposed in this paper in post hoc trajectory analyses. It is our intention that this work contributes to the frontier of LLM evaluation research as a springboard for future benchmark designs and agent harness engineering (specifically with regards to creating lean tool suites) that optimize for metrics that complement but are distinct from accuracy.

Related papers

• AutoTool: Efficient Tool Selection for Large Language Model Agents [10.061664247482488]
  Large Language Model (LLM) agents have emerged as powerful tools for automating complex tasks by leveraging the reasoning and decision-making abilities of LLMs.<n>However, a major bottleneck lies in the high inference cost of tool selection, especially in approaches like ReAct that repeatedly invoke the LLM to determine which tool to use at each step.<n>We propose AutoTool, a novel graph-based framework that bypasses repeated LLM inference by exploiting a key empirical observation: tool usage inertia.
  arXiv  Detail & Related papers  (2025-11-18T16:41:48Z)
• TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use [74.47746287181383]
  Large language model (LLM)-based agents increasingly rely on tool use to complete real-world tasks.<n>We introduce TRAJECT-Bench, a trajectory-aware benchmark to comprehensively evaluate LLMs' tool use capability.
  arXiv  Detail & Related papers  (2025-10-06T07:30:25Z)
• SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs [35.180946816997164]
  Large Language Models (LLMs) have exhibited great performance in autonomously calling various tools in external environments.<n>This paper focuses on prospective ways to assess the safety of LLM tool utilization, aiming to avoid irreversible harm caused by directly executing tools.<n>We propose SafeToolBench, the first benchmark to comprehensively assess tool utilization security in a prospective manner.<n>We also propose a novel framework, SafeInstructTool, which aims to enhance LLMs' awareness of tool utilization security from three perspectives.
  arXiv  Detail & Related papers  (2025-09-09T01:31:25Z)
• MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models [33.250579401886206]
  This paper introduces MCP-RADAR, the first comprehensive benchmark specifically designed to evaluate Large Language Models (LLMs) performance within the Model Context Protocol (MCP) framework.<n> MCP-RADAR features a challenging dataset of 507 tasks spanning six domains: mathematical reasoning, web search, email, calendar, file management, and terminal operations.<n>Unlike traditional benchmarks that rely on subjective human evaluation or binary success metrics, MCP-RADAR adopts objective, quantifiable measurements across multiple task domains.
  arXiv  Detail & Related papers  (2025-05-22T14:02:37Z)
• Acting Less is Reasoning More! Teaching Model to Act Efficiently [87.28134636548705]
  Tool-integrated reasoning augments large language models with the ability to invoke external tools to solve tasks.<n>Current approaches typically optimize only for final correctness without considering the efficiency or necessity of external tool use.<n>We propose a framework that encourages models to produce accurate answers with minimal tool calls.<n>Our approach reduces tool calls by up to 68.3% and improves tool productivity by up to 215.4%, while maintaining comparable answer accuracy.
  arXiv  Detail & Related papers  (2025-04-21T05:40:05Z)
• PTR: Precision-Driven Tool Recommendation for Large Language Models [43.53494041932615]
  We propose a Precision-driven Tool Recommendation (PTR) approach for Large Language Models (LLMs) PTR captures an initial, concise set of tools by leveraging historical tool bundle usage and dynamically adjusts the tool set by performing tool matching. We present a new dataset, RecTools, and a metric, TRACC, designed to evaluate the effectiveness of tool recommendation for LLMs.
  arXiv  Detail & Related papers  (2024-11-14T17:33:36Z)
• From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions [60.733557487886635]
  This paper focuses on bridging the comprehension gap between Large Language Models and external tools.<n>We propose a novel framework, DRAFT, aimed at Dynamically Refining tool documentation.<n>This methodology pivots on an innovative trial-and-error approach, consisting of three distinct learning phases.
  arXiv  Detail & Related papers  (2024-10-10T17:58:44Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error [54.954211216847135]
  Existing large language models (LLMs) only reach a correctness rate in the range of 30% to 60%. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE) STE orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory.
  arXiv  Detail & Related papers  (2024-03-07T18:50:51Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.