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New Fan-Out Benchmark Tests LLM Safety in Concurrent Tool Calls

By PulseAugur Editorial · [1 sources] ·

A new benchmarking challenge called Fan-Out has been developed to evaluate Large Language Models (LLMs) on their ability to safely and efficiently handle concurrent tool calls, especially when these calls share common states. The benchmark measures safety, optimal scheduling, and speed, with results showing that Google's Gemini models performed best on the hardest rung, achieving high safety and optimal scheduling percentages. Other models like Anthropic's Claude and OpenAI's GPT-5.4-mini showed varying degrees of success, with some models excelling in safety but struggling with optimal scheduling in complex multi-agent scenarios. AI

IMPACT This benchmark highlights the critical need for LLMs to manage complex, concurrent operations safely, pushing development towards more robust agentic capabilities.

RANK_REASON The item describes a new benchmark for evaluating LLM capabilities in handling concurrent tool calls, which is a research-oriented contribution to the field. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Fan-Out Benchmark Tests LLM Safety in Concurrent Tool Calls

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19 / 100
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Tool
The item describes a new benchmark for evaluating LLM capabilities in handling concurrent tool calls, which is a research-oriented contribution to the field. [lever_c_demoted from research: ic=1 ai…
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Hotragn ·

    Fan-Out: Concurrent Tool Calls Under Shared-State Hazards

    <p><strong>DEV × Kaggle Benchmarking Challenge</strong> · <code>#kagglechallenge</code></p> <p><strong>Author:</strong> Hotragn Pettugani · <a href="https://www.kaggle.com/pettuganihotragn" rel="noopener noreferrer">Kaggle</a></p> <h2> The problem </h2> <p>Agents increasingly fir…

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