Seeing Is Not Deciding - Can Multimodal LLMs Act as Effective CEOs? arXiv preprint arXiv:2608.05864: Evaluation Design and Construct Validity
Keywords:
multimodal decision support, evaluation design and construct validity, evidence synthesis, reproducibility, research evaluationAbstract
Evaluation is persuasive only when the measured outcome corresponds to the construct claimed by the study and the comparison answers the stated research question. This structured evidence review evaluates "Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs? arXiv preprint arXiv:2608.05864" alongside nine author-disjoint, topically matched publications in multimodal decision support. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through evaluation design and construct validity, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda aligns claims, outcomes, comparators, sampling, and uncertainty before any performance estimate is interpreted.
References
- Dai, Y., Peng, X., Wang, Y., Nakov, P., & Xie, Z. (2026). Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs? arXiv preprint arXiv:2608.05864. arXiv. https://doi.org/10.48550/arXiv.2608.05864
- HIRAGAR, P.-R. (2025). Med-AgentX: Multimodal Large Language Model Agents with Explainable Reinforcement Learning for Trustworthy Biomedical Decision Support. . https://doi.org/10.21203/rs.3.rs-7773686/v1
- Wang, T., Zhang, B., Jiang, D., & Li, D. (2025). A Multimodal Large Language Model Framework for Intelligent Perception and Decision-Making in Smart Manufacturing. Sensors, 25(10), 3072. https://doi.org/10.3390/s25103072
- Jiang, Y., Li, Z., & Philip Chen, C.-L. (2024). Research on Financial Big Data Collection and Intelligent Decision-Making System Based on Multimodal Large Language Model. 2024 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 1-6. https://doi.org/10.1109/ifuzzy63051.2024.10662884
- CHEN, Y., Lyu, G., Zhang, J., & Liu, P. (2025). RECOGNITION AND CLINICAL DECISION-MAKING OF LIVER ULTRASOUND STANDARD PLANES BASED ON MULTIMODAL LARGE LANGUAGE MODEL. Ultrasound in Medicine & Biology, 51, S108. https://doi.org/10.1016/j.ultrasmedbio.2025.11.106
- Yang, L., Li, Y., Tan, J., & Mao, L. (2025). Research on risk decision-making generation method for water conservancy project based on multimodal knowledge graph and large language model. PLOS One, 20(8), e0330258. https://doi.org/10.1371/journal.pone.0330258
- GORSE, V., Mitteau, R., & Marot, J. (2025). Decision Support for In-Operation Monitoring of the West Tokamak First Wall Using Multimodal Large Language Model (Llm) on Infrared Imaging. . https://doi.org/10.2139/ssrn.5177592
- Liao, T., Fang, X., Feng, Y., & Wang, S. (2026). Reliable and Efficient Decision-Making for Large Language Model Agents through Uncertainty-Guided Adaptive Reasoning. . https://doi.org/10.2139/ssrn.6844659
- Gómez, C., Yin, J., Huang, C.-M., & Unberath, M. (2024). How Large Language Model-Powered Conversational Agents Influence Decision Making in Domestic Medical Triage Contexts. . https://doi.org/10.2139/ssrn.4797707
- Huang, H.-L., Liu, C., Roozbahani, M.-M., & Frost, J.-D. (2026). SeismoMind: A Zero-shot Decision-Tree Based Multimodal Large Language Model Framework for Post-Earthquake Infrastructure Damage Understanding. . https://doi.org/10.2139/ssrn.6466459
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Markets, Organizations & Public Value
This work is licensed under a Creative Commons Attribution 4.0 International License.