ManagerBench - Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs

Overview

ManagerBench is a comprehensive evaluation framework designed to assess the safety-pragmatism trade-off in autonomous Large Language Models (LLMs). This repository contains the implementation, benchmark, and evaluation tools presented in our paper.

Table of Contents

• Abstract
• Setup Instructions
• Usage
• Configuration
• Evaluation Parameters
• Results
• Citation

Abstract

As large language models (LLMs) evolve from conversational assistants into autonomous agents, evaluating the safety of their actions becomes critical. Prior safety benchmarks have primarily focused on preventing generation of harmful content, such as toxic text. However, they overlook the challenge of agents taking harmful actions when the most effective path to an operational goal conflicts with human safety. To address this gap, we introduce ManagerBench, a benchmark that evaluates LLM decision-making in realistic, human-validated managerial scenarios. Each scenario forces a choice between a pragmatic but harmful action that achieves an operational goal, and a safe action that leads to worse operational performance. A parallel control set, where potential harm is directed only at inanimate objects, measures a model's pragmatism and identifies its tendency to be overly safe. Our findings indicate that the frontier LLMs perform poorly when navigating this safety-pragmatism trade-off. Many consistently choose harmful options to advance their operational goals, while others avoid harm only to become overly safe and ineffective. Critically, we find this misalignment does not stem from an inability to perceive harm, as models' harm assessments align with human judgments, but from flawed prioritization. ManagerBench is a challenging benchmark for a core component of agentic behavior: making safe choices when operational goals and alignment values incentivize conflicting actions.

Setup Instructions

1. Clone the repository:

git clone https://github.com/technion-cs-nlp/ManagerBench.git
cd ManagerBench

2. Create and activate the conda environment:

conda env create -f environment.yml
conda activate manager_bench

3. Ensure the benchmark data is properly placed in the bench/ directory.

Usage

Basic Evaluation

To run ManagerBench on a specific model:

python ManagerBench.py --model_name <model_name> --full_evaluation

Replace <model_name> with your desired model identifier.

Example

python ManagerBench.py --model_name gpt-4o-2024-08-06 --full_evaluation

Results will be automatically saved in the results/ folder and displayed in the terminal, will show the result of the model similar to the results in Table 1.

Configuration

API Keys

If you're evaluating a model that requires an API key, configure it in the api_key.py file.

Evaluation Parameters

The following optional parameters allow you to customize the evaluation:

Parameter	Description	Default
--full_evaluation	Enable comprehensive evaluation as shown in Table 1	False
--benefit_percentage	Specify the percentage of benefit scenarios	50%
--harm_percentage	Specify the percentage of harm scenarios	5%
--push_evaluation	Enable goal-oriented prompt evaluation	False

Advanced Usage Example

python ManagerBench.py \
  --model_name gpt-4o-2024-08-06 \
  --benefit_percentage 60 \
  --harm_percentage 10 \
  --push_evaluation \

Will evaluate GPT-4o on with nudge, human harm percentage of 10% and a benefit of 60%.

Results

• Output Location: All evaluation results are saved in the results/ directory
• Format: Final result are provided at the terminal output

Citation

If you use ManagerBench in your research, please cite our paper:

@article{simhi2025managerbench,
  title={ManagerBench: Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs},
  author={Simhi, Adi and Herzig, Jonathan and Tutek, Martin and Itzhak, Itay and Szpektor, Idan and Belinkov, Yonatan},
  journal={arXiv preprint arXiv:2510.00857},
  year={2025}
}

Repository Structure

ManagerBench/
├── bench/                  # Benchmark datasets
├── results/                # Evaluation results
├── ManagerBench.py         # Main evaluation script
├── api_key.py              # API configuration
├── environment.yml         # Conda environment specification
└── README.md               # This file