CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910118525796352 |
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| author | Hwang, Yeonjun Park, Sungyong Kim, Minju Lee, Dongha Yeo, Jinyoung |
| author_facet | Hwang, Yeonjun Park, Sungyong Kim, Minju Lee, Dongha Yeo, Jinyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09029 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space Hwang, Yeonjun Park, Sungyong Kim, Minju Lee, Dongha Yeo, Jinyoung Computation and Language Artificial Intelligence 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. |
| title | CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.09029 |