Seeing Is Not Deciding - Can Multimodal LLMs Act as Effective CEOs? arXiv preprint arXiv:2608.05864: Human Oversight and Decision Accountability
Keywords:
multimodal decision support, human oversight and decision accountability, evidence synthesis, reproducibility, research evaluationAbstract
Human oversight is meaningful when intervention points, responsibility, escalation paths, and the evidence available to decision makers are explicitly defined. 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 human oversight and decision accountability, 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 specifies reviewer authority, override logging, escalation procedures, and evaluation of automation bias.
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.