[2604.09029] CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space

Computer Science > Computation and Language arXiv:2604.09029 (cs) [Submitted on 10 Apr 2026] Title:CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space Authors:Yeonjun Hwang, Sungyong Park, Minju Kim, Dongha Lee, Jinyoung Yeo View a PDF of the paper titled CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space, by Yeonjun Hwang and 4 other authors View PDF HTML (experimental) 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 LL...