Current Issue
Vol. 2 No. 10 (2026): AI for Energy × Multimodal LLM × Computational Finance
The five papers in this issue appear to span three fields: energy, finance, and multimodal large models. In reality, both point to a single issue: **How long can we trust the "correct results" provided by AI when it moves beyond clean experimental environments into real-world scenarios characterized by missing data, relaxed constraints, and distorted reward signals?** **
Ren addresses short-term photovoltaic prediction under sensor failures, employing a dual-path hybrid expert system and conformal prediction intervals to answer \"how to gracefully degrade when data is scarce\";Yin directly incorporated small-signal stability verification into the capacity search of the islanded microgrid, pointing out that "cost optimization" often comes at the expense of dynamic instability; Jiang and Yang leverage the Louvain community detection algorithm and large language models to empirically demonstrate how the liberalization of China's stock market has prompted institutional investors to shift from chasing hot topics to embracing value investing.Hu and Song, using \"where visual information interfaces with language models\" as a unifying thread, mapped out an architectural taxonomy for multimodal large models from 2020 to 2025;Zhang and Song further exposed a blind spot—models might misread axes or count shapes incorrectly, yet "by chance" provide the correct answer, while a reward mechanism based solely on results is completely oblivious to this.
The boundaries of reliability are precisely the hidden main thread of this issue.
Published:
2026-09-22
