CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space

• URL: http://arxiv.org/abs/2604.09029v1
• Date: Fri, 10 Apr 2026 06:47:09 GMT
• Title: CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space
• Abstract summary: We introduce CONDESION-BENCH, a benchmark to evaluate conditional decision-making in compositional action space.<n>By employing oracle-based evaluation, we provide a more rigorous assessment of large language models as decision-support tools.
• Score: 25.384914281879702
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models have been widely explored as decision-support tools in high-stakes domains due to their contextual understanding and reasoning capabilities. However, existing decision-making benchmarks rely on two simplifying assumptions: actions are selected from a finite set of pre-defined candidates, and explicit conditions restricting action feasibility are not incorporated into the decision-making process. These assumptions fail to capture the compositional structure of real-world actions and the explicit conditions that constrain their validity. To address these limitations, we introduce CONDESION-BENCH, a benchmark designed to evaluate conditional decision-making in compositional action space. In CONDESION-BENCH, actions are defined as allocations to decision variables and are restricted by explicit conditions at the variable, contextual, and allocation levels. By employing oracle-based evaluation of both decision quality and condition adherence, we provide a more rigorous assessment of LLMs as decision-support tools.

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